Deep Learning with PyTorch for Beginners - Part 1

PyTorch Basics & Linear Regression

What you'll learn:

• Introduction to Machine Learning and Deep Learning
• PyTorch Basics: Tensors & Gradients
• Linear Regression with PyTorch
• Working with Image Data in PyTorch
• Image Classification using Convolutional Neural Networks
• Residual Networks, Data Augmentation and Regularization Techniques
• Generative Adverserial Networks

“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