Deep Learning with PyTorch for Beginners – Part 1

“Deep Learning with PyTorch for Beginners is a series of courses covering various topics like the basics of Deep Learning, building neural networks with PyTorch, CNNs, RNNs, NLP, GANs, etc. This course is Part 1 of 5.

Topics Covered:

1. Introduction to Machine Learning & Deep Learning
2. Introduction on how to use Jovian platform
3. Introduction to PyTorch: Tensors & Gradients
4. Interoperability with Numpy
5. Linear Regression with PyTorch
    – System setup
    – Training data
    – Linear Regression from scratch
    – Loss function
    – Compute gradients
    – Adjust weights and biases using gradient descent
    – Train for multiple epochs
    – Linear Regression using PyTorch built-ins
    – Dataset and DataLoader
    – Using nn.Linear 
    – Loss Function
    – Optimizer
    – Train the model
    – Commit and update the notebook
7. Sharing Jupyter notebooks online with Jovian

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