Deep Learning with PyTorch

Photo by Artturi Jalli on Unsplash

Deep learning is a subset of machine learning that deals with algorithms inspired by the structure and function of the brain, called artificial neural networks. PyTorch is an open-source machine learning library developed by Facebook’s AI Research lab. It is widely used for developing and training deep learning models due to its flexibility and ease of use.

Key Concepts in Deep Learning

1. Neural Networks: Models composed of layers of interconnected nodes (neurons) that process data.
2. Training: The process of teaching a neural network by adjusting weights using labeled data.
3. Backpropagation: A method for updating the weights by propagating the error backwards through the network.
4. Optimization: Algorithms like SGD (Stochastic Gradient Descent) that minimize the error by adjusting the weights.
5. Loss Function: A function that measures the difference between the model’s predictions and the actual labels.

Why PyTorch?

• Dynamic Computation Graphs: PyTorch uses dynamic computation graphs, which means the graph is built on-the-fly. This is useful for tasks where the network structure can change during runtime.
• Pythonic Nature: PyTorch is designed to be deeply integrated with Python…