The Most Important Algorithm in Machine Learning

Step-by-Step Tutorial: Understanding Back Propagation Algorithm in Machine Learning Systems

1. Introduction to Back Propagation Algorithm:

   • Nearly all machine learning systems, from GPT to AlphaFold, share a common algorithm called back propagation.
   • Back propagation is the foundation of machine learning, enabling artificial networks to learn and solve complex problems efficiently.

2. Historical Background:

   • Back propagation has its roots in the 17th century but was modernized in 1986 by Rumelhart, Hinton, and Williams for neural networks.

3. Understanding the Concept:

   • Imagine fitting a curve to a set of points on a plane using a polynomial of degree 5.
   • The goal is to minimize the loss function, which quantifies the difference between the data points and the fitted curve.

4. Manual Curve Fitting:

   • Visualize a machine called Curve Fitter 6000 with adjustable knobs representing coefficients in the polynomial.
   • By iteratively adjusting these knobs based on the loss function value, you can move towards the best-fitting curve.

5. Introduction to Gradient Descent:

   • Gradient descent is a method to efficiently minimize the loss function by adjusting the parameters in the direction opposite to the gradient.

6. Extension to Higher Dimensions:

   • The concept of gradient descent extends to higher dimensions with multiple parameters, where the gradient vector guides the optimization process.

7. Derivatives and Chain Rule:

   • Derivatives of complex functions are computed using basic rules like addition, multiplication, and the chain rule.
   • The chain rule allows for efficient computation of derivatives for composite functions.

8. Backward Propagation in Neural Networks:

   • Construct a computational graph for the loss function and perform backward propagation to find gradients with respect to each parameter.

9. Training Process:

   • Update the parameters using the gradients obtained from backward propagation in each iteration.
   • Repeat the forward and backward pass iteratively to optimize the model's parameters.

10. Application to Neural Networks:

    • Neural networks leverage back propagation for training by adjusting weights and biases to minimize the loss function.
    • The algorithm is used in various machine learning models due to its efficiency and effectiveness in optimization.

11. Future Implications:

    • Understanding back propagation is crucial for developing and training advanced machine learning models.
    • The algorithm's versatility allows for solving complex problems like image classification and text generation.

12. Further Exploration:

    • Stay tuned for the next video on synaptic plasticity to explore how biological neural networks learn, contrasting with back propagation in artificial systems.

By following these steps, you can gain a comprehensive understanding of the back propagation algorithm and its significance in training machine learning systems effectively.