Sep 20, 2024
# Lecture on Training Neural Networks
## INTRODUCTION
- **Lecturer:** Andre, with over ten years of experience in training deep neural networks.
- **Goal:** To understand training a neural network from scratch using Jupyter Notebook.
- **Focus:** Building and understanding the library called Micrograd.
## Overview of Micrograd
- **Micrograd:** An autograd engine that implements backpropagation.
- **Purpose:** Efficiently evaluating gradients of loss functions with respect to the weights of a neural network.
- **Importance of Backpropagation:** It is at the core of modern deep neural network libraries like PyTorch.
## Building Multiplications with Micrograd
- **Example:** Creating mathematical multiplications using Micrograd.
- **Essential operations:** Add, multiply, etc.
- **Multiplication graphs:** Built with inputs a, b, c leading to an output g.
- **Value object:** Encapsulates numbers and supports operations.
- **Multiplication graph:** Stores children nodes and operations.
## Backpropagation
- **Purpose:** To evaluate outputs concerning inputs.
- **Example:** Determining the output regarding inputs a and b through g.
- **Chain rule:** Key to backpropagation, allowing recursive calculation of outputs.
- **Gradient:** Indicates the effect of inputs on the output, essential for adjusting weights.
## Implementing Micrograd
- **Scalar-value engine:** Operates on individual scalars, not tensors.
- **Pedagogical tool:** Helps in understanding the fundamentals of training neural networks.
- **Efficiency:** Real-world applications use tensors for parallel operations.
## Implementing Neural Networks
- **Neurons:** Simple mathematical models with weights and biases.
- **Layers:** Composed of fully connected neurons to inputs.
- **MLP (Multi-Layer Perceptron):** Hierarchical layers forming the neural network.
## Training Neural Networks
- **Loss function:** A measure to be minimized, e.g., mean squared error or cross-entropy.
- **Gradient descent:** Adjusting weights incrementally to minimize loss.
- **Learning rate:** Determines the step size in gradient descent.
## PyTorch and Micrograd
- **PyTorch:** Utilizes tensors and optimizes operations for efficiency.
- **Comparison:** Micrograd is scalar-based and educational, while PyTorch is tensor-oriented for production.
- **Backward pass:** PyTorch similarly uses backward operations like Micrograd.
## Common Mistakes
- **Zeroing gradients:** It's essential to clear gradients before backpropagation.
## Takeaway
- **Neural networks:** Mathematical mappings that mimic computations.
- **Backpropagation:** A fundamental algorithm for learning used in various libraries.
- **Micrograd:** Demonstrates the simplicity of training a neural network.
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This summarized overview touches on the main points of the lecture, offering a broad insight into training neural networks, Micrograd, and related concepts.