Developing Large Language Models

SKILL PATH

Developing Large Language Models

An LLM is an advanced AI system designed to understand and generate human language based on vast amounts of text data. With LLMs being a hot trend in AI, used in industries from customer service to creative writing, you’ll gain highly relevant and in-demand skills. In this Skill Path, you’ll start with the basics of PyTorch and neural networks, then dive into deep learning concepts and transformers, which form the backbone of LLMs. You’ll learn how to train, fine-tune, and evaluate these models and explore their real-world applications, like text generation and natural language processing.

12 hours

94 Lessons

Learning Objectives

Gain proficiency in PyTorch basics, including tensor operations, autograd, and solving simple regression problems.

Master the principles of generative adversarial networks (GANs) and develop models for tasks like image synthesis and text-to-image conversion.

Understand the architecture and components of Transformer models, focusing on multi-head attention and positional encoding.

Explore the capabilities and fine-tuning techniques of large language models (LLMs) with hands-on experience using GPT-2 and GPT-3.

Apply advanced AI models to practical NLP tasks, optimizing and deploying them for real-world applications in the evolving AI landscape.

Path Content

Module 1

PyTorch Basics

In this module, learn the fundamentals of PyTorch in this module, including tensor operations, tensor types, and basic neural network concepts.

Module 2

Deep Learning with PyTorch

In this module, dive deeper into regression using PyTorch. Understand loading data, autograd, optimization, and creating and updating parameters.

Module 3

GANs in PyTorch

In this module, explore Generative Adversarial Networks (GANs) in PyTorch. Learn to build, optimize, and apply GANs for tasks like text-to-image synthesis.

Module 4

Transformers

In this module, understand the architecture and training of Transformer models. Focus on components like multi-head attention and positional encoding.

Module 5

Large Language Models (LLMs)

In this module, explore LLMs like GPT. Understand their components, capabilities, and fine-tuning techniques. Apply your knowledge to real-world NLP tasks.

Module 6

LLM Applications

In this module, apply LLMs in practical scenarios. Learn about zero-shot models, fine-tuning techniques, and the role of AI specialists.

What Our Learners Say

Your method is simple, straight to the point and I can practice with it everywhere, even from my phone, that's something I have never had in other learning platforms.

Felipe Matheus

I highly recommend Educative. The courses are well organized and easy to understand.

Adina Ong

I prefer Educative courses because they have a nice mix of text & images. I find that with full video courses, it can often be too easy to go into passive learning mode.

Clifford Fajardo

I prefer Educative courses because they have a nice mix of text & images. I find that with full video courses, it can often be too easy to go into passive learning mode.

Clifford Fajardo

What Our Learners Say

Your method is simple, straight to the point and I can practice with it everywhere, even from my phone, that's something I have never had in other learning platforms.

Felipe Matheus

I highly recommend Educative. The courses are well organized and easy to understand.

Adina Ong

I prefer Educative courses because they have a nice mix of text & images. I find that with full video courses, it can often be too easy to go into passive learning mode.

Clifford Fajardo

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