The spelled-out intro to neural networks and backpropagation: building micrograd

Tutorial: The Spelled-Out Intro to Neural Networks and Backpropagation

Video Title: The Spelled-Out Intro to Neural Networks and Backpropagation: Building Micrograd

Channel Name: Andrej Karpathy

Description:

This tutorial provides a step-by-step explanation of backpropagation and training neural networks, assuming only basic Python knowledge and a vague recollection of high school calculus.

Steps:

1. Micrograd Overview (00:00:25)

   • Understand the basics of micrograd, a tool for building neural networks.

2. Derivative of a Simple Function with One Input (00:08:08)

   • Learn how to calculate the derivative of a function with a single input.

3. Derivative of a Function with Multiple Inputs (00:14:12)

   • Explore how to find the derivative of a function with multiple inputs.

4. Starting the Core Value Object of Micrograd and its Visualization (00:19:09)

   • Begin building the core Value object of micrograd and visualize its functionality.

5. Manual Backpropagation Example #1: Simple Expression (00:32:10)

   • Walk through a manual backpropagation example with a simple expression.

6. Preview of a Single Optimization Step (00:51:10)

   • Understand the process of a single optimization step.

7. Manual Backpropagation Example #2: A Neuron (00:52:52)

   • Dive into a manual backpropagation example involving a neuron.

8. Implementing Backward Functions for Operations (01:09:02)

   • Implement backward functions for individual operations.

9. Implementing Backward Function for a Whole Expression Graph (01:17:32)

   • Create a backward function for an entire expression graph.

10. Fixing Backprop Bug and Exploring More Operations (01:22:28)

    • Fix a backpropagation bug and work with additional operations.

11. Comparing with PyTorch and Building a Neural Net Library (01:39:31)

    • Compare the process with PyTorch and build a neural net library in micrograd.

12. Training the Network Manually (02:01:12)

    • Perform gradient descent optimization manually and train the network.

13. Summary and Modern Neural Nets (02:14:03)

    • Summarize the learnings and discuss transitioning to modern neural networks.

14. Walkthrough of Full Code on Github and PyTorch Comparison (02:16:46)

    • Review the full code on Github and compare with PyTorch's backward pass for tanh.

15. Conclusion and Outtakes (02:24:39)

    • Conclude the tutorial with final thoughts and outtakes.

Additional Resources:

• Github Repository for Micrograd: micrograd on Github
• Jupyter Notebooks: Github Jupyter Notebooks
• Website: Andrej Karpathy's Website
• Twitter: Andrej Karpathy's Twitter
• Exercises & Google Collab: Google Collab Exercise
• Neural Networks: Zero to Hero Discord Channel: Discord Channel

By following these steps and utilizing the provided resources, you can enhance your understanding of backpropagation and neural network training. Happy learning!