Overview

In this comprehensive course, you'll embark on a journey through the foundational elements and core concepts of PyTorch, one of the most popular deep learning frameworks. Starting with a detailed overview and system setup, you'll be guided through installing and configuring your environment to ensure a smooth learning experience. The course then transitions into the basics of machine learning and artificial intelligence, laying the groundwork for more advanced topics. As you delve deeper, you'll explore the intricacies of deep learning, including model performance, activation and loss functions, and optimization techniques. Each module builds on the last, gradually increasing in complexity. You'll learn to construct neural networks from scratch, understanding every component from data preparation to the backpropagation process. This hands-on approach ensures you not only grasp theoretical concepts but also gain practical skills in building and training your models. The course culminates in a detailed look at PyTorch-specific modeling. You will work on real-world exercises, such as implementing linear regression and hyperparameter tuning, using PyTorch’s powerful features. By the end, you'll be well-equipped to tackle complex deep learning problems, confident in your ability to utilize PyTorch effectively for your AI and machine learning projects. This course is ideal for tech professionals, data scientists, and AI enthusiasts looking to master PyTorch for deep learning. Prerequisites include prior experience in Python and a basic understanding of machine learning concepts.

Syllabus

Course Overview and System Setup

In this module, we will introduce you to the course structure, covering the main topics and learning objectives. You'll learn how to set up your system, including installing necessary software and creating a conda environment. We'll also guide you on accessing course materials and provide tips for navigating the course efficiently.

Machine Learning

In this module, we will delve into the basics of machine learning. You will start with an introduction to artificial intelligence and its core concepts. The module will then explore the essentials of machine learning and provide an overview of different machine learning models, laying the groundwork for more advanced topics.

Deep Learning Introduction

In this module, we will explore the foundational concepts of deep learning. You will gain insights into deep learning models, their performance evaluation, and the evolution from perceptrons to neural networks. The module also covers various types of neural network layers, activation functions, loss functions, and optimization techniques, providing a robust understanding of deep learning frameworks.

Model Evaluation

In this module, we will focus on evaluating machine learning models. You will learn about underfitting and overfitting, and how to mitigate these issues. The module will also cover the train-test split method and its importance in model evaluation, along with various resampling techniques to manage imbalanced datasets effectively.

Neural Network from Scratch

In this module, we will guide you through the process of constructing a neural network from scratch. You will start with data preparation and model initialization and proceed to implement essential functions such as forward and backward propagation. The module also covers training and evaluation techniques to help you build and assess your neural network model effectively.

Tensors

In this module, we will explore the concept of tensors and their significance in PyTorch. You will learn about the relationship between tensors and computational graphs and gain hands-on experience with tensor operations through coding exercises. This module aims to equip you with the skills to apply tensors in real-world machine learning scenarios.

PyTorch Modeling Introduction

In this module, we will introduce you to PyTorch modeling. You will learn to build and train models from scratch, including linear regression. The module covers batch processing, datasets, and dataloaders to manage data effectively. You will also explore techniques for saving, loading, and optimizing models, including hyperparameter tuning, to enhance your machine learning workflow.