Introduction to Neural Networks and Backpropagation with micrograd
Learn the basics of neural networks and backpropagation with micrograd, a library for building mathematical expressions.
00:00:00 Learn about the basics of neural networks and backpropagation using the micrograd library. Explore how to build mathematical expressions and understand the derivative's influence on the function.
🧠 Neural network training involves using an autograd engine to implement backpropagation.
🔬 Micrograd is an autograd engine that allows you to build mathematical expressions and evaluate the gradient of a loss function with respect to the weights of a neural network.
📈 Micrograd's functionality is best illustrated through an example of building a mathematical expression and evaluating its derivative.
00:20:52 This video explains the intro to neural networks and backpropagation, with an example of building micrograd, a value object for mathematical expressions.
🔑 We have implemented value objects for addition and multiplication operations.
🔑 We have created a data structure to build mathematical expressions and visualize them.
🔑 We have started implementing backpropagation to calculate gradients for each value.
00:41:46 This video explains the chain rule in calculus and how it is used in backpropagation for neural networks. It demonstrates how derivatives are calculated and propagated through a computation graph.
💡 The chain rule in calculus allows us to correctly differentiate through a function composition by multiplying the derivatives.
🔍 The chain rule helps us determine the instantaneous rate of change of one variable with respect to another in a complex equation.
✏️ Backpropagation is the process of recursively applying the chain rule backwards through a computation graph.
01:02:40 A tutorial on neural networks and backpropagation using micrograd, explaining the process of backpropagation step by step.
📝 Backpropagation is a technique used to calculate gradients in neural networks.
🧮 The local derivative of the tanh function is 1 - tanh^2(x).
🔀 In backpropagation, gradients are propagated from the output layer to the input layer.
01:23:32 The video explains how to implement neural networks and backpropagation using the micrograd library. It demonstrates the importance of accumulating gradients and shows how to break down complex expressions into simpler operations.
📝 The video explains the issue of gradient overriding in neural networks when using the backward pass.
💡 To solve the issue, we need to accumulate gradients using the 'plus equals' operation instead of setting them directly.
🏋️♂️ The video also demonstrates how to implement complex mathematical expressions and neural networks using PyTorch's API.
01:44:26 This video explains how to build a neural network using the micrograd library and demonstrates how to perform a forward pass and calculate loss and gradients in the network.
🧠 Neural networks can be built using modules and classes to represent neurons, layers, and an entire multi-layer perceptron (mlp).
⚙️ The forward pass of a neuron involves multiplying the input values with randomly initialized weights, adding a bias, and applying a non-linearity.
🔁 The backpropagation algorithm allows us to update the weights of the neural network to minimize the loss, which is a measure of performance.
02:05:18 This video explains the concept of neural networks and backpropagation using micrograd. It demonstrates the process of forward pass, backward pass, and gradient descent to train a neural net.
💡 Neural networks are mathematical expressions that take input data, weights, and parameters and use a loss function to measure the accuracy of predictions.
💭 Backpropagation is used to calculate the gradient, which allows us to tune the parameters to minimize the loss.
🔍 Gradient descent is an iterative process that follows the gradient to update the parameters and improve the predictions of the neural network.
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