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
