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