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