PyTorch Tutorial 6: Training our First Neural Network (Part 2: Cross Entropy Loss & Backward pass)

Welcome to the second part of our two-part PyTorch Neural Networks Tutorial series! In this exciting video, we continue our journey into the world of deep learning by diving into the training process of our very first neural network using tensors and mathematical operations.

🚀 In Part 1, we laid the foundation by loading the data, initializing the neural network, and implementing the forward pass. Now, in Part 2, we're ready to take things to the next level!

🧠 What You'll Learn:

Implementing the Cross-Entropy Loss Function: Understand how to calculate and apply the essential loss function for training neural networks.
Unleash the Power of Backpropagation: Dive deep into the concept of backpropagation and learn how it optimizes our neural network's weights for improved performance.
Tracking Metrics for Success: Discover how to monitor key metrics like loss and accuracy as your neural network learns and improves.
Whether you're a beginner in the world of deep learning or looking to solidify your understanding of PyTorch and neural networks, this tutorial is designed for you. By the end of this video, you'll have a clear grasp of how to train a neural network from scratch using PyTorch, and you'll be well on your way to becoming a deep learning pro!

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