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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