PyTorch Online Course

PyTorch Online Course

PyTorch, a Python framework developed by Facebook, is widely used for developing and deploying deep learning models and has become one of the most popular frameworks in the field. You will start by learning the core concepts of deep learning, followed by an in-depth exploration of tensor handling, where you’ll gain the skills to create and manipulate tensors while utilizing PyTorch’s Autograd for automatic gradient computation.

Next, you’ll move on to modeling, building linear regression models from scratch. Finally, you will delve into classification models, mastering both multilabel and multiclass classification techniques.

By the end of this course, you will have mastered essential concepts, models, and techniques, equipping you with the confidence to design and deploy powerful deep-learning solutions.

Who is this Course for?

This course is perfect for Python developers and data enthusiasts looking to enhance their skills. It is equally valuable for aspiring data scientists, machine learning engineers, AI enthusiasts, and anyone fascinated by the transformative capabilities of deep learning. Whether you're a beginner or have some prior experience, the course provides a seamless learning path to help you build, deploy, and innovate with deep learning models using PyTorch.

Pre-requisites: Basic Python knowledge is required to fully engage with the material.

Course Curriculum

• Course Overview and System Setup
• Machine Learning
• Deep Learning Introduction
• Model Evaluation
• Neural Network from Scratch
• Tensors
• PyTorch Modeling Introduction
• Classification Models
• CNN: Image Classification
• CNN: Audio Classification
• CNN: Object Detection
• Style Transfer
• Pre-Trained Networks and Transfer Learning
• Recurrent Neural Networks
• Recommender Systems
• Autoencoders
• Generative Adversarial Networks
• Graph Neural Networks
• Transformers
• PyTorch Lightning
• Semi-Supervised Learning
• Natural Language Processing (NLP)
• Miscellaneous Topics
• Model Debugging
• Model Deployment
• Final Section