Programming Transformer Neural Networks with PyTorch

Course

This course will guide you through the essential concepts of Transformer Neural Networks and their implementation using PyTorch. Starting with an introduction to Transformers, you will learn to build and train Transformer models from scratch. Additionally, you will explore the advantages of using pre-trained Transformer models and how to leverage them effectively in your projects. By the end of this course, you will have a solid foundation in programming Transformer Neural Networks with PyTorch.

Beginner

4 hours

Completion Certificate

Last Updated July 31, 2024

Skills you'll learn:

Transformer neural networks

Prerequisites:

Basic Python

Course Lessons

Lesson 1

Introduction to Transformer Neural Networks

Explore Transformer neural networks, their architecture, and applications like ChatGPT. Delve into NLP basics, tokenization, and model training using PyTorch for AI advancements.

Lesson 2

Building Transformer Neural Networks with PyTorch

Learn to build a Transformer model with PyTorch, covering tokenization, embeddings, multi-head attention, training, and text generation for NLP tasks.

Lesson 3

Using Pre-Trained Transformers

Master the use of pre-trained transformers, including their training, fine-tuning, limitations, and applying them to NLP tasks like text generation and QA.

Taught By The Best

Ivan Mushketyk

Software Engineer

Experienced software engineer with over ten years in the field. Worked at at AWS and Stripe, as well as several start-ups. An experienced online instructor, creating courses on AI, data engineering, and AWS. Passionate about leveraging technology to solve complex problems and sharing knowledge with others.

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Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

Gain proven experience
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Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.