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
Requirements
- Basic Linear Algebra (matrix multiplication)
- Basic Python Programming
- Basic Calculus (Derivatives)
Description
“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
Who this course is for:
- Beginner Python developers curious about Deep Learning and PyTorch
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Course content
