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        },
        "d7f938a87cf346b8826367eaf1326ff7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
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          }
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      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# [MDB] Machine Learning Coding Demo\n",
        "\n",
        "> **Author(s)**: Amol Budhiraja (abudhiraja@berkeley.edu)\n",
        "\n",
        "This notebook aims to provide an introduction to machine learning and computer vision basics for new members at Mobile Developers of Berkeley (MDB: https://mdb.dev/). It provides a very basic introduction to some core components and is used as a lecture aid during classroom sessions.\n",
        "\n"
      ],
      "metadata": {
        "id": "qiLHZjKSecVX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup and Importing Necessary Files"
      ],
      "metadata": {
        "id": "XbtetqmRgkU9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we want to install the important libraries and dependencies that we will use in our exploration!"
      ],
      "metadata": {
        "id": "sJHIGhlApX8x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers diffusers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "117Lfs6aoLCs",
        "outputId": "cd1b9727-0625-4151-bcab-fbb34911b802"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
            "Requirement already satisfied: diffusers in /usr/local/lib/python3.10/dist-packages (0.30.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.7)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.6)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.10/dist-packages (from diffusers) (8.5.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from diffusers) (10.4.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.10.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
            "Requirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata->diffusers) (3.20.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "aS4Gu5RseU2P"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "from diffusers import StableDiffusionPipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To ensure that we are getting consistent results each time, we set the random seeds for both numpy and torch. This is an OPTIONAL step."
      ],
      "metadata": {
        "id": "5VxtZFprp6fE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set random seeds for reproducibility\n",
        "np.random.seed(0)\n",
        "torch.manual_seed(0)\n",
        "\n",
        "print(\"Notebook successfully setup!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7ObgxbAgxKT",
        "outputId": "6ac69145-9e23-41ca-a556-888f968f3472"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Notebook successfully setup!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we discussed in class, there are three problems in Machine Learning:\n",
        "\n",
        "1. Regression\n",
        "2. Classification\n",
        "3. Generation\n",
        "\n",
        "In this notebook we will explore all three of these problems.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "9iGWctFahW8-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Regression"
      ],
      "metadata": {
        "id": "vRpj1o57hhca"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In machine learning, **regression** is a type of supervised learning used to predict continuous values, such as predicting house prices, stock prices, or temperatures.\n",
        "In this lesson, we’ll cover:\n",
        "- Linear regression\n",
        "- Mean squared error (MSE) as a loss function\n",
        "- Training a model to minimize the loss using gradient descent\n",
        "\n",
        "Let's get started!"
      ],
      "metadata": {
        "id": "FkFUdQY5hnXe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we are going to generate some sample data points..."
      ],
      "metadata": {
        "id": "1ixGdfGNhpDz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_data(n_samples=100):\n",
        "    X = np.linspace(-1, 1, n_samples)\n",
        "    y = 3 * X + 2 + 0.2 * np.random.randn(n_samples)\n",
        "    return X, y\n",
        "\n",
        "X, y = generate_data()\n",
        "plt.scatter(X, y, color='blue', alpha=0.6)\n",
        "plt.title(\"Synthetic Data for Regression\")\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "TfvjDjbgg0bV",
        "outputId": "66b3aa4d-fa53-4a20-a4b0-ed08dc22129e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this demo we will be using PyTorch, which is a popular Machine Learning library that was created by Facebook and is used in research and throughout industry! We will convert these data values to tensors."
      ],
      "metadata": {
        "id": "zaDkt5-OiBp5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_tensor = torch.tensor(X, dtype=torch.float32).view(-1, 1)\n",
        "y_tensor = torch.tensor(y, dtype=torch.float32).view(-1, 1)\n",
        "print(\"Data prepared for PyTorch!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTZppHPahtFC",
        "outputId": "b70003aa-89f9-425c-9a98-4cdf2f668faf"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data prepared for PyTorch!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Aside: Introduction to Tensors\n",
        "\n",
        "In machine learning, **tensors** are a fundamental data structure, especially in frameworks like PyTorch. They allow efficient computation and are versatile in representing data across multiple dimensions.\n",
        "\n",
        "### What is a Tensor?\n",
        "\n",
        "A tensor is a multidimensional array:\n",
        "- **Scalar**: 0-dimensional tensor (e.g., a single number).\n",
        "- **Vector**: 1D tensor (e.g., [3, 5, 7]).\n",
        "- **Matrix**: 2D tensor (e.g., a table of numbers).\n",
        "- **n-D Tensor**: Tensors can extend to higher dimensions (e.g., 3D tensors for images, 4D for videos).\n",
        "\n",
        "### Tensors in PyTorch\n",
        "\n",
        "\n",
        "In PyTorch, tensors work similarly to NumPy arrays but offer:\n",
        "1. **Multi-Dimensional Storage**: Ideal for complex data like images and videos.\n",
        "2. **Optimized Computation**: Supports CPU and GPU calculations.\n",
        "3. **Automatic Differentiation**: Enables gradient calculations for model training.\n",
        "\n",
        "\n",
        "Tensors allow models to process and learn from data efficiently. In deep learning, input data (tensors) pass through models to produce outputs, using operations like matrix multiplication—optimized through tensors.\n"
      ],
      "metadata": {
        "id": "-xYwyflzknbQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scalar = torch.tensor(42)\n",
        "vector = torch.tensor([1.0, 2.0, 3.0])\n",
        "matrix = torch.tensor([[1.0, 2.0], [3.0, 4.0]])\n",
        "\n",
        "print(\"Scalar (0D Tensor): \", scalar)\n",
        "print(\"Vector (1D Tensor): \", vector)\n",
        "print(\"Matrix (2D Tensor): \", matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AyZBLUR_hxGa",
        "outputId": "1a2bf53f-5b55-4894-82da-7401a144aae3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scalar (0D Tensor):  tensor(42)\n",
            "Vector (1D Tensor):  tensor([1., 2., 3.])\n",
            "Matrix (2D Tensor):  tensor([[1., 2.],\n",
            "        [3., 4.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Solving Linear Regression with Least Squares"
      ],
      "metadata": {
        "id": "1_9oC4b5lZgP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this example, we’ll solve a linear regression problem using the **least squares method** in NumPy. The least squares approach provides a direct way to find the optimal parameters without iterative training.\n",
        "\n",
        "### Theory: Least Squares Solution\n",
        "\n",
        "In a linear regression model, we aim to find a line that best fits the data points. This line has the equation:\n",
        "\n",
        "$\n",
        "y = W \\cdot X + b\n",
        "$\n",
        "\n",
        "where:\n",
        "- \\( y \\) represents the target values.\n",
        "- \\( X \\) is the input data.\n",
        "- \\( W \\) is the weight (slope).\n",
        "- \\( b \\) is the bias (intercept).\n",
        "\n",
        "### Matrix Formulation\n",
        "\n",
        "To find \\( W \\) and \\( b \\), we can represent the model in matrix form:\n",
        "\n",
        "$\n",
        "y = X \\cdot \\theta\n",
        "$\n",
        "\n",
        "where:\n",
        "- \\( y \\) is a vector of outputs.\n",
        "- \\( X \\) is a matrix of inputs (with an additional column of ones for the bias term).\n",
        "- $ ( \\theta ) $ is a vector of parameters \\([W, b]\\).\n",
        "\n",
        "### The Least Squares Formula\n",
        "\n",
        "Using linear algebra, the least squares solution for \\(\\theta\\) is:\n",
        "\n",
        "$\n",
        "\\theta = (X^T X)^{-1} X^T y\n",
        "$\n",
        "\n",
        "This formula provides the exact values for \\( W \\) and \\( b \\) that minimize the mean squared error between the predictions and the true values.\n"
      ],
      "metadata": {
        "id": "mQBqf1fQmKWX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_b = np.c_[X, np.ones(X.shape[0])]\n",
        "theta_best = np.linalg.inv(X_b.T @ X_b) @ X_b.T @ y\n",
        "W, b = theta_best\n",
        "print(f\"Optimal parameters: W = {W:.4f}, b = {b:.4f}\")\n",
        "\n",
        "y_pred = W * X + b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZCuRN0Wkwsq",
        "outputId": "d41dac1d-544f-48fe-918c-f8835050cdbe"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal parameters: W = 2.9703, b = 2.0120\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X, y, color='blue', alpha=0.6, label=\"Original data\")\n",
        "plt.plot(X, y_pred, color='red', label=\"Fitted line (Least Squares)\")\n",
        "plt.title(\"Linear Regression using Least Squares\")\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "qKdmdtCCmAs5",
        "outputId": "5ebb618d-ac57-4c44-d489-e865e4cb91f7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multilayer Perceptron (MLP) Example with PyTorch\n",
        "\n",
        "In this example, we’ll implement a basic MLP for a regression task using PyTorch. An MLP is a type of feedforward neural network with one or more hidden layers. It can learn complex, non-linear relationships in data, making it a powerful tool for a wide range of machine learning tasks."
      ],
      "metadata": {
        "id": "tPMnfS8HmqHI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Generating Synthethic Data"
      ],
      "metadata": {
        "id": "K3evpoM8mv-D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate synthetic non-linear data\n",
        "np.random.seed(0)\n",
        "X = np.linspace(-1, 1, 200)\n",
        "y = X ** 3 + 0.1 * np.random.randn(200)  # non-linear relationship with some noise\n",
        "\n",
        "# Convert data to PyTorch tensors\n",
        "X_tensor = torch.tensor(X, dtype=torch.float32).view(-1, 1)  # reshaped to (200, 1)\n",
        "y_tensor = torch.tensor(y, dtype=torch.float32).view(-1, 1)\n",
        "\n",
        "# Plot the data\n",
        "plt.scatter(X, y, color='blue', alpha=0.6)\n",
        "plt.title(\"Synthetic Non-linear Data\")\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "VWzMNFRcmlkq",
        "outputId": "99541c73-e1d2-474b-f243-8ba7839f847d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 2: Defining the MLP Model"
      ],
      "metadata": {
        "id": "psx3qxl-m0b6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the MLP model\n",
        "class MLP(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MLP, self).__init__()\n",
        "        self.hidden1 = nn.Linear(1, 64)  # First hidden layer with 64 neurons\n",
        "        self.hidden2 = nn.Linear(64, 32) # Second hidden layer with 32 neurons\n",
        "        self.output = nn.Linear(32, 1)   # Output layer\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = torch.relu(self.hidden1(x))  # ReLU activation for hidden layer 1\n",
        "        x = torch.relu(self.hidden2(x))  # ReLU activation for hidden layer 2\n",
        "        x = self.output(x)               # Linear activation for output layer\n",
        "        return x\n",
        "\n",
        "model = MLP()\n",
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IieTK3zPmD4m",
        "outputId": "79a3753c-25a7-4d85-803d-29a411de4d81"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MLP(\n",
            "  (hidden1): Linear(in_features=1, out_features=64, bias=True)\n",
            "  (hidden2): Linear(in_features=64, out_features=32, bias=True)\n",
            "  (output): Linear(in_features=32, out_features=1, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 3: Setting up Loss Function and Optimizer"
      ],
      "metadata": {
        "id": "qwXPq71vm4AP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.MSELoss()\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
        "\n",
        "print(\"Loss function and optimizer initialized.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-RnKeRGOm2d-",
        "outputId": "5c183646-63a6-4085-e2bf-efb4778f3d4e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss function and optimizer initialized.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4: Training the Model"
      ],
      "metadata": {
        "id": "vURzqGVvm76m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Training loop with tqdm progress bar\n",
        "epochs = 1000\n",
        "losses = []\n",
        "\n",
        "# Use tqdm to wrap the range of epochs\n",
        "for epoch in tqdm(range(epochs), desc=\"Training Progress\"):\n",
        "    # Forward pass\n",
        "    predictions = model(X_tensor)\n",
        "    loss = criterion(predictions, y_tensor)\n",
        "    losses.append(loss.item())\n",
        "\n",
        "    # Backward pass and optimization\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    # Print loss every 100 epochs for reference\n",
        "    if (epoch + 1) % 100 == 0:\n",
        "        print(f\"Epoch [{epoch + 1}/{epochs}], Loss: {loss.item():.4f}\")\n",
        "\n",
        "print(\"Training complete!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nh1_YGq3m6AB",
        "outputId": "32886435-852a-4516-a6f9-74fa16de0bea"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Progress:  15%|█▌        | 153/1000 [00:00<00:02, 357.09it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [100/1000], Loss: 0.0442\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Progress:  31%|███       | 310/1000 [00:00<00:01, 487.94it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [200/1000], Loss: 0.0329\n",
            "Epoch [300/1000], Loss: 0.0312\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Progress:  51%|█████     | 508/1000 [00:01<00:00, 580.07it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [400/1000], Loss: 0.0292\n",
            "Epoch [500/1000], Loss: 0.0275\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Progress:  70%|███████   | 705/1000 [00:01<00:00, 584.67it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [600/1000], Loss: 0.0264\n",
            "Epoch [700/1000], Loss: 0.0256\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Progress:  87%|████████▋ | 872/1000 [00:01<00:00, 451.60it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [800/1000], Loss: 0.0248\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Progress: 100%|██████████| 1000/1000 [00:02<00:00, 493.28it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [900/1000], Loss: 0.0241\n",
            "Epoch [1000/1000], Loss: 0.0233\n",
            "Training complete!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 5: Plotting the training loss"
      ],
      "metadata": {
        "id": "SLUccTPHqvBn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(losses)\n",
        "plt.title(\"Training Loss over Epochs\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"MSE Loss\")\n",
        "plt.show()\n",
        "\n",
        "with torch.no_grad():\n",
        "    y_pred = model(X_tensor).numpy()\n",
        "\n",
        "plt.scatter(X, y, color='blue', alpha=0.6, label=\"Original data\")\n",
        "plt.plot(X, y_pred, color='red', label=\"MLP Prediction\")\n",
        "plt.title(\"MLP Fit on Non-linear Data\")\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "1w_88Z6cm-WB",
        "outputId": "05806eec-c3ad-455d-ab54-297c057dcc3e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classification"
      ],
      "metadata": {
        "id": "PV6dYUCNnkqF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification** is a type of supervised learning where the goal is to categorize input data into predefined classes or labels. It’s widely used in tasks such as image recognition, spam detection, and medical diagnosis.\n",
        "\n",
        "In classification, a model learns patterns in labeled training data to assign new, unseen inputs to one of the predefined classes. For example, in an image classification task with the CIFAR-10 dataset, the model learns to classify images into one of ten categories, such as \"airplane,\" \"car,\" or \"cat.\"\n",
        "\n",
        "Some common algorithms for classification include:\n",
        "- **Multilayer Perceptron (MLP)**: A simple feedforward neural network.\n",
        "- **Convolutional Neural Networks (CNNs)**: Effective for image data due to their ability to capture spatial hierarchies.\n",
        "- **Decision Trees and Random Forests**: Non-neural approaches often used for structured data."
      ],
      "metadata": {
        "id": "qXogyBSrq6pY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "When working with high dimensional data, Machine Learning is best done with GPUs! GPUs allow us to perform the matrix multiplacations in parallel!"
      ],
      "metadata": {
        "id": "b58Li27WlcLj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Using device: {device}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BrK3gpMankZz",
        "outputId": "539d05fe-d314-49b8-a60e-0b2dfbf0b995"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classifying Handwritten Digits (MNIST MLP)"
      ],
      "metadata": {
        "id": "ucyOuuJrlxl4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The MNIST dataset consists of 28x28 grayscale images of handwritten digits (0-9). We’ll normalize the pixel values to make the model training more efficient. We use a DataLoader to batch and shuffle the data."
      ],
      "metadata": {
        "id": "AOZ54MpOlwDc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "transform_mnist = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n",
        "\n",
        "train_mnist = torchvision.datasets.MNIST(root='./data', train=True, transform=transform_mnist, download=True)\n",
        "test_mnist = torchvision.datasets.MNIST(root='./data', train=False, transform=transform_mnist, download=True)\n",
        "\n",
        "train_loader_mnist = torch.utils.data.DataLoader(train_mnist, batch_size=64, shuffle=True)\n",
        "test_loader_mnist = torch.utils.data.DataLoader(test_mnist, batch_size=64, shuffle=False)"
      ],
      "metadata": {
        "id": "d1BzGIHblwVR"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def visualize_mnist_data(loader):\n",
        "    data_iter = iter(loader)\n",
        "    images, labels = next(data_iter)\n",
        "\n",
        "    # Plot the images in a 3x3 grid\n",
        "    plt.figure(figsize=(6, 6))\n",
        "    for i in range(9):\n",
        "        plt.subplot(3, 3, i + 1)\n",
        "        plt.imshow(images[i].squeeze(), cmap=\"gray\")\n",
        "        plt.title(f\"Label: {labels[i].item()}\")\n",
        "        plt.axis(\"off\")\n",
        "    plt.show()\n",
        "\n",
        "# Visualize images from the train_loader_mnist\n",
        "visualize_mnist_data(train_loader_mnist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "bQycelSDyLoL",
        "outputId": "66028fdc-e445-40ac-bd6c-2b103839bdda"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Define the Model"
      ],
      "metadata": {
        "id": "Zgg09oy7rGra"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We define a simple MLP model with:\n",
        "\n",
        "\t•\tInput layer: Flattening the 28x28 pixel image into a single vector.\n",
        "\t•\tTwo hidden layers: Each with ReLU activation for non-linearity.\n",
        "\t•\tOutput layer: Produces scores for each of the 10 digit classes (0-9)."
      ],
      "metadata": {
        "id": "HrLl02gRm359"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MLP_MNIST(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MLP_MNIST, self).__init__()\n",
        "        self.fc1 = nn.Linear(28 * 28, 256)\n",
        "        self.fc2 = nn.Linear(256, 128)\n",
        "        self.fc3 = nn.Linear(128, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(-1, 28 * 28)\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        x = torch.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "model_mnist = MLP_MNIST().to(device)\n",
        "print(model_mnist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWq1rtg8l9zw",
        "outputId": "e3cbec08-3a22-4f6d-d53b-396c7fd02a24"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MLP_MNIST(\n",
            "  (fc1): Linear(in_features=784, out_features=256, bias=True)\n",
            "  (fc2): Linear(in_features=256, out_features=128, bias=True)\n",
            "  (fc3): Linear(in_features=128, out_features=10, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We define the loss function (CrossEntropyLoss) and optimizer (Adam). Using tqdm, we add a progress bar for the training loop."
      ],
      "metadata": {
        "id": "_IAeJFtHm6Mt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 2: Define the Loss Function and Optimizer"
      ],
      "metadata": {
        "id": "clXxHcYcrI9L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define loss function and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model_mnist.parameters(), lr=0.01)"
      ],
      "metadata": {
        "id": "wMDmpUaFmE_H"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Steps 3 and 4: Training and Testing Loops"
      ],
      "metadata": {
        "id": "Uu_akRkYrLZU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize lists to store loss values\n",
        "train_losses = []\n",
        "test_losses = []\n",
        "\n",
        "# Training loop with loss recording\n",
        "epochs = 5\n",
        "for epoch in range(epochs):\n",
        "    model_mnist.train()  # Set the model to training mode\n",
        "    running_loss = 0.0\n",
        "\n",
        "    for images, labels in tqdm(train_loader_mnist, desc=f\"Epoch {epoch + 1}/{epochs}\"):\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model_mnist(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward pass and optimization\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "\n",
        "    # Calculate and store the average training loss for the epoch\n",
        "    avg_train_loss = running_loss / len(train_loader_mnist)\n",
        "    train_losses.append(avg_train_loss)\n",
        "    print(f\"Epoch [{epoch + 1}/{epochs}], Training Loss: {avg_train_loss:.4f}\")\n",
        "\n",
        "    # Run the testing loop to calculate test loss\n",
        "    model_mnist.eval()  # Set the model to evaluation mode\n",
        "    test_loss = 0.0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for images, labels in test_loader_mnist:\n",
        "            images, labels = images.to(device), labels.to(device)\n",
        "\n",
        "            # Forward pass\n",
        "            outputs = model_mnist(images)\n",
        "            loss = criterion(outputs, labels)\n",
        "\n",
        "            test_loss += loss.item()\n",
        "\n",
        "    # Calculate and store the average test loss for the epoch\n",
        "    avg_test_loss = test_loss / len(test_loader_mnist)\n",
        "    test_losses.append(avg_test_loss)\n",
        "    print(f\"Epoch [{epoch + 1}/{epochs}], Test Loss: {avg_test_loss:.4f}\")\n",
        "\n",
        "print(\"Training complete!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5_AkqK5mH32",
        "outputId": "50de7aef-3f63-4259-c84b-620d54c31b42"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 1/5: 100%|██████████| 938/938 [00:22<00:00, 41.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/5], Training Loss: 0.3856\n",
            "Epoch [1/5], Test Loss: 0.2899\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 2/5: 100%|██████████| 938/938 [00:14<00:00, 62.60it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [2/5], Training Loss: 0.2550\n",
            "Epoch [2/5], Test Loss: 0.2118\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 3/5: 100%|██████████| 938/938 [00:14<00:00, 63.84it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [3/5], Training Loss: 0.2451\n",
            "Epoch [3/5], Test Loss: 0.2256\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 4/5: 100%|██████████| 938/938 [00:17<00:00, 54.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [4/5], Training Loss: 0.2361\n",
            "Epoch [4/5], Test Loss: 0.2084\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 5/5: 100%|██████████| 938/938 [00:16<00:00, 58.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [5/5], Training Loss: 0.2229\n",
            "Epoch [5/5], Test Loss: 0.2422\n",
            "Training complete!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 5: Plot the training and testing losses"
      ],
      "metadata": {
        "id": "S1a2Tfz8rOck"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the training and testing losses\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(train_losses, label='Training Loss')\n",
        "plt.plot(test_losses, label='Testing Loss')\n",
        "plt.title(\"Training and Testing Losses over Epochs\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "sEy9mnaEmYls",
        "outputId": "62415d59-e2b4-4a58-ac7c-4a1ce59f0601"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Object Classification (CIFAR-10 CNN)\n",
        "\n",
        "The CIFAR-10 dataset contains 32x32 color images across 10 classes (e.g., airplanes, cars, birds). We’ll apply some basic data augmentations like horizontal flipping and random cropping."
      ],
      "metadata": {
        "id": "50w5xjivnADf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define transformations for CIFAR-10 data\n",
        "transform_cifar10 = transforms.Compose([\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.RandomCrop(32, padding=4),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "])\n",
        "\n",
        "# Download and load CIFAR-10 dataset\n",
        "train_cifar10 = torchvision.datasets.CIFAR10(root='./data', train=True, transform=transform_cifar10, download=True)\n",
        "test_cifar10 = torchvision.datasets.CIFAR10(root='./data', train=False, transform=transform_cifar10, download=True)\n",
        "\n",
        "train_loader_cifar10 = torch.utils.data.DataLoader(train_cifar10, batch_size=64, shuffle=True)\n",
        "test_loader_cifar10 = torch.utils.data.DataLoader(test_cifar10, batch_size=64, shuffle=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7KSQbY4TmbT3",
        "outputId": "17648a8e-08cc-4280-c4f2-92ad04ed5f76"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def visualize_cifar10_data(loader, classes):\n",
        "    data_iter = iter(loader)\n",
        "    images, labels = next(data_iter)\n",
        "\n",
        "    # Plot the images in a 3x3 grid\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    for i in range(9):\n",
        "        plt.subplot(3, 3, i + 1)\n",
        "        img = images[i].numpy().transpose((1, 2, 0))  # Convert to HWC format for plotting\n",
        "        img = img * 0.5 + 0.5  # Unnormalize to [0, 1] range\n",
        "        plt.imshow(img)\n",
        "        plt.title(f\"Label: {classes[labels[i].item()]}\")\n",
        "        plt.axis(\"off\")\n",
        "    plt.show()\n",
        "\n",
        "# CIFAR-10 classes\n",
        "cifar10_classes = [\n",
        "    'airplane', 'automobile', 'bird', 'cat', 'deer',\n",
        "    'dog', 'frog', 'horse', 'ship', 'truck'\n",
        "]\n",
        "\n",
        "# Visualize images from the train_loader_cifar10\n",
        "visualize_cifar10_data(train_loader_cifar10, cifar10_classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "NF_bVWfvySWH",
        "outputId": "5f09567d-eea9-49a9-8643-d2064e178e72"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Define the CNN Model"
      ],
      "metadata": {
        "id": "xCwAejxGrT0O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "A CNN is well-suited for image data due to its convolutional layers, which capture spatial hierarchies. Our model will include:\n",
        "\n",
        "\t•\tThree convolutional layers with ReLU and max-pooling.\n",
        "\t•\tTwo fully connected layers for classification."
      ],
      "metadata": {
        "id": "FGD7vdY8nKcJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a simple CNN for CIFAR-10 classification\n",
        "class CNN_CIFAR10(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CNN_CIFAR10, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n",
        "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
        "        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.fc1 = nn.Linear(128 * 4 * 4, 256)\n",
        "        self.fc2 = nn.Linear(256, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(torch.relu(self.conv1(x)))\n",
        "        x = self.pool(torch.relu(self.conv2(x)))\n",
        "        x = self.pool(torch.relu(self.conv3(x)))\n",
        "        x = x.view(-1, 128 * 4 * 4)\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "model_cifar10 = CNN_CIFAR10().to(device)\n",
        "print(model_cifar10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mq_OmPYDnK5-",
        "outputId": "a06a3b6a-fea7-4c88-e919-03e55b44595a"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CNN_CIFAR10(\n",
            "  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (fc1): Linear(in_features=2048, out_features=256, bias=True)\n",
            "  (fc2): Linear(in_features=256, out_features=10, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 2: Define the Loss Function and Optimizer"
      ],
      "metadata": {
        "id": "QEBdOxqHrWXV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We define the loss function and optimizer We use tqdm for progress visualization."
      ],
      "metadata": {
        "id": "F1xNeetTnNnq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define loss function and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model_cifar10.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "MnjzkqcpnMQv"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 3: Define Testing and Training Loops"
      ],
      "metadata": {
        "id": "NttV6RvBrb9a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "  Now let's define our testing and training losses!\n",
        "    \n",
        "    •\tTrack the training loss for each epoch.\n",
        "\t•\tRun the testing loop at the end of each epoch to calculate the test loss.\n",
        "\t•\tStore both losses for plotting."
      ],
      "metadata": {
        "id": "KeFVx_O7ngdN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize lists to store loss values\n",
        "train_losses_cifar10 = []\n",
        "test_losses_cifar10 = []\n",
        "\n",
        "# Training loop with loss recording\n",
        "epochs = 10\n",
        "for epoch in range(epochs):\n",
        "    model_cifar10.train()\n",
        "    running_loss = 0.0\n",
        "\n",
        "    for images, labels in tqdm(train_loader_cifar10, desc=f\"Epoch {epoch + 1}/{epochs}\"):\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "\n",
        "        outputs = model_cifar10(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "\n",
        "    avg_train_loss = running_loss / len(train_loader_cifar10)\n",
        "    train_losses_cifar10.append(avg_train_loss)\n",
        "    print(f\"Epoch [{epoch + 1}/{epochs}], Training Loss: {avg_train_loss:.4f}\")\n",
        "\n",
        "    model_cifar10.eval()\n",
        "    test_loss = 0.0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for images, labels in test_loader_cifar10:\n",
        "            images, labels = images.to(device), labels.to(device)\n",
        "\n",
        "            outputs = model_cifar10(images)\n",
        "            loss = criterion(outputs, labels)\n",
        "\n",
        "            test_loss += loss.item()\n",
        "\n",
        "    avg_test_loss = test_loss / len(test_loader_cifar10)\n",
        "    test_losses_cifar10.append(avg_test_loss)\n",
        "    print(f\"Epoch [{epoch + 1}/{epochs}], Test Loss: {avg_test_loss:.4f}\")\n",
        "\n",
        "print(\"Training complete for CIFAR-10!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3WkaTufnO18",
        "outputId": "67edadf5-e86c-4a0a-ebcd-c354a533521c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 1/10: 100%|██████████| 782/782 [00:25<00:00, 31.20it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/10], Training Loss: 1.5562\n",
            "Epoch [1/10], Test Loss: 1.3214\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 2/10: 100%|██████████| 782/782 [00:25<00:00, 31.08it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [2/10], Training Loss: 1.1758\n",
            "Epoch [2/10], Test Loss: 1.0615\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 3/10: 100%|██████████| 782/782 [00:25<00:00, 31.24it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [3/10], Training Loss: 0.9921\n",
            "Epoch [3/10], Test Loss: 0.9428\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 4/10: 100%|██████████| 782/782 [00:25<00:00, 31.05it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [4/10], Training Loss: 0.8823\n",
            "Epoch [4/10], Test Loss: 0.8417\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 5/10: 100%|██████████| 782/782 [00:24<00:00, 31.62it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [5/10], Training Loss: 0.8068\n",
            "Epoch [5/10], Test Loss: 0.8074\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 6/10: 100%|██████████| 782/782 [00:25<00:00, 31.02it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [6/10], Training Loss: 0.7578\n",
            "Epoch [6/10], Test Loss: 0.7758\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 7/10: 100%|██████████| 782/782 [00:25<00:00, 31.28it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [7/10], Training Loss: 0.7076\n",
            "Epoch [7/10], Test Loss: 0.7479\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 8/10: 100%|██████████| 782/782 [00:25<00:00, 30.12it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [8/10], Training Loss: 0.6717\n",
            "Epoch [8/10], Test Loss: 0.7023\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 9/10: 100%|██████████| 782/782 [00:25<00:00, 30.60it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [9/10], Training Loss: 0.6423\n",
            "Epoch [9/10], Test Loss: 0.7055\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 10/10: 100%|██████████| 782/782 [00:26<00:00, 29.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [10/10], Training Loss: 0.6232\n",
            "Epoch [10/10], Test Loss: 0.6717\n",
            "Training complete for CIFAR-10!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4: Plot the training and testing losses"
      ],
      "metadata": {
        "id": "UQlFoUP5rlNa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(train_losses_cifar10, label='Training Loss (CIFAR-10)')\n",
        "plt.plot(test_losses_cifar10, label='Testing Loss (CIFAR-10)')\n",
        "plt.title(\"Training and Testing Losses over Epochs (CIFAR-10)\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "LpZyrHWznwf9",
        "outputId": "0c3f7424-324d-4f62-c260-4d55d17b0f72"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generative AI\n",
        "\n",
        "**Generative AI** refers to a branch of artificial intelligence that focuses on creating new data instances that resemble existing data. Unlike traditional AI, which focuses on classification or regression tasks, generative models can produce entirely new content, including images, text, audio, and more.\n",
        "\n",
        "Generative AI models learn the underlying patterns in data and use them to generate similar data. Some popular applications of generative AI include:\n",
        "- **Image Synthesis**: Models like GANs (Generative Adversarial Networks) and diffusion models can generate realistic images from noise or textual descriptions.\n",
        "- **Text Generation**: Language models like GPT-3 create coherent text based on a prompt.\n",
        "- **Style Transfer**: Image-to-image translation models (e.g., CycleGAN) can transform an image from one style to another.\n",
        "\n",
        "Training a Generative Model like a Stable Diffusion model or GAN would take too long for the sake of this demo, so we are going to look at a pretrained model! Using existing ML models in your apps is always a great idea! Why waste time reinventing the wheel, when you can focus on adapting it for your needs (i.e. Transfer Learning)."
      ],
      "metadata": {
        "id": "SlfMTwaUoQXX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Write the prompt"
      ],
      "metadata": {
        "id": "2YQvucDdr251"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"lebron james and his son bronny playing basketball\""
      ],
      "metadata": {
        "id": "_2-bnhiur1nQ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 2: Load the model and supply the prompt"
      ],
      "metadata": {
        "id": "ASeQDU1jr48M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the pre-trained Stable Diffusion model\n",
        "model_id = \"CompVis/stable-diffusion-v1-4\"\n",
        "pipe = StableDiffusionPipeline.from_pretrained(model_id).to(device)\n",
        "\n",
        "with torch.no_grad():\n",
        "    image = pipe(prompt, guidance_scale=7.5).images[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240,
          "referenced_widgets": [
            "202651d3e5754185b9554e03e7b7fee0",
            "0e26d9d89c9746fdb4736a5804add347",
            "39062c543aa44c96a1f5f1419ce010af",
            "ab8c1c26d7bc4283bd743330461c34ad",
            "26e90bf539c7458d9c7c63120248cecd",
            "c3bf665c4a444b088b7d207d9d9cb4cf",
            "b0dde715328b45d19af5aaf52d153c99",
            "5af744f275d64f15a3324b47b59eb27c",
            "3feca13f2c024279bfc306b9805760fe",
            "bf7e2eb8aebd4e81af28fe9708da8765",
            "6b55fb46705f44a68fc45f7bd4515ea8",
            "39c182ebf4b14ccebbe72a2a5d538bc9",
            "a0007fcea7484b1e81c6481b54787e61",
            "5e2a669212e94b40b7314d5a2f196362",
            "6cbfb7c79f8b4438bfd27baa8b4a325b",
            "3de20ebfc67c40fdb50b0603ce3687cb",
            "06fa1dc39dc84069a040010ecef3b210",
            "4e25901b7bc84c1ea616aed7ade7eb40",
            "48d64f44f3224151a56c85615a012c33",
            "0485651612ef4977ae5ebbf011fd52af",
            "1048c4593e384d718cf9c6f15d8c1f4c",
            "d7f938a87cf346b8826367eaf1326ff7"
          ]
        },
        "id": "lid39-7wolq6",
        "outputId": "c0710e8c-7162-4bee-9d33-797c6ca54ea2"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "202651d3e5754185b9554e03e7b7fee0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "39c182ebf4b14ccebbe72a2a5d538bc9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 3: Visualize the results"
      ],
      "metadata": {
        "id": "WTZfr8TEr72W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the image\n",
        "plt.imshow(image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "vmz5pd7GpBtb",
        "outputId": "697bcff9-1c76-4de8-e135-f099ede60279"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7d4009d9ebc0>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion\n",
        "\n",
        "In this notebook, we explored key concepts in machine learning, covering **regression**, **classification**, and **generative AI**. Here’s a summary of what we learned:\n",
        "\n",
        "1. **Regression**:\n",
        "   - We introduced regression as a technique for predicting continuous values and explored it through two approaches:\n",
        "     - **Least Squares Method**: A straightforward mathematical approach for linear regression, where we calculated optimal parameters directly without iterative training.\n",
        "     - **Multilayer Perceptron (MLP) for Regression**: We used a simple neural network to learn a non-linear relationship in data, demonstrating the flexibility of MLPs for capturing complex patterns.\n",
        "   - We visualized the training and testing losses for regression models, helping us understand model performance over time.\n",
        "\n",
        "2. **Classification**:\n",
        "   - We trained an **MLP** on the MNIST dataset to classify handwritten digits, providing insight into basic neural networks for classification tasks.\n",
        "   - We then used a **Convolutional Neural Network (CNN)** on the CIFAR-10 dataset to classify images into ten categories. The CNN’s structure allowed it to capture spatial hierarchies in image data, which is crucial for accurate image recognition.\n",
        "   - We plotted training and testing losses, observing the model’s performance and identifying signs of overfitting or underfitting.\n",
        "\n",
        "3. **Generative AI**:\n",
        "   - We introduced the concept of generative AI, which focuses on creating new data that resembles existing data. Applications of generative AI include text generation, image synthesis, and style transfer.\n",
        "   - Using a pre-trained generative model, we generated images based on textual prompts, showcasing the power of generative AI without needing to train complex models from scratch."
      ],
      "metadata": {
        "id": "aWO68jGcsE3C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Key Takeaways\n",
        "- **Regression models** are essential for predicting continuous values and can be implemented using methods ranging from simple least squares to more flexible neural networks.\n",
        "- **Classification models** categorize data into specific labels, with CNNs being particularly effective for image-based tasks due to their ability to capture spatial features.\n",
        "- **Generative AI models** are powerful tools for creating new data instances, supporting applications in image synthesis, language generation, and creative tasks.\n",
        "- **Pre-trained models** make it easier to leverage advanced generative technologies without high computational demands, making state-of-the-art AI more accessible.\n",
        "\n",
        "This notebook provided hands-on experience with building and evaluating various machine learning models, from regression and classification to generative tasks. These foundational skills serve as a strong base for more advanced AI applications, and we encourage further experimentation to deepen your understanding."
      ],
      "metadata": {
        "id": "Hzf_WLnysUlR"
      }
    }
  ]
}