PyTorch Basics & Linear Regression
“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
Course Information
Course Instructor
