A free, community-driven learning tool called the Open Source LLM Course was created to assist students in comprehending and developing applications using Large Language Models (LLMs). The course covers LLM basics, transformer designs, fine-tuning, Retrieval-Augmented Generation (RAG), model quantization, deployment, and LLM-powered applications through a combination of organized learning roadmaps, [...]

Cohere’s LLM University (LLMU) is a free, self-paced learning platform that teaches the principles and real-world applications of enterprise artificial intelligence (AI), natural language processing (NLP), and large language models (LLMs). In order to cover topics like embeddings, semantic search, rapid engineering, retrieval-augmented generation (RAG), deployment, tool use, and creating [...]

The principles of Transformer models, Natural Language Processing (NLP), and Large Language Models (LLMs) are taught in this extensive, free, practical course. You will learn how to do inference, refine pretrained models, create NLP applications, and share models using the Hugging Face ecosystem, which includes Transformers, Datasets, Tokenizers, Accelerate, and [...]

The principles of Transformer models, Natural Language Processing (NLP), and Large Language Models (LLMs) are taught in this extensive, free, practical course. You will learn how to do inference, refine pretrained models, create NLP applications, and share models using the Hugging Face ecosystem, which includes Transformers, Datasets, Tokenizers, Accelerate, and [...]

A free, practical deep learning course that teaches you how to use Python, PyTorch, and the fastai package to create, train, and implement cutting-edge AI models. Computer vision, natural language processing (NLP), tabular data, recommendation systems, model deployment, and deep learning principles are all covered in this hands-on, project-first course. [...]

One of the most prestigious graduate-level NLP courses in the world is CS224N. With topics including word embeddings, recurrent neural networks (RNNs), attention mechanisms, Transformers, large language models (LLMs), machine translation, question answering, and model fine-tuning, it offers a thorough introduction to contemporary deep learning-based natural language processing. Lectures, PyTorch [...]

Jay Alammar’s free visual instructional, Illustrated Transformer, describes the Transformer architecture that was first presented in the seminal Attention Is All You Need paper. It covers fundamental ideas including embeddings, self-attention, multi-head attention, positional encoding, encoder-decoder architecture, and the training process using clear illustrations and detailed explanations. Universities and AI [...]

Andrej Karpathy’s online, interactive deep learning course Neural Networks: Zero to Hero teaches neural networks from the ground up. You will use Python and PyTorch to create neural networks, backpropagation, language models, tokenizers, and even a GPT-style Transformer from scratch rather than depending on high-level frameworks. Through detailed coding tasks, [...]

AssemblyAI’s free, interactive course Transformers from Scratch shows you how Transformer models operate by developing them from the ground up in PyTorch. Token embeddings, positional encodings, self-attention, multi-head attention, feed-forward networks, encoder/decoder layers, and training loops are among the fundamental building blocks that you will construct yourself rather than depending [...]

The architecture and implementation of Transformer neural networks, the basis of contemporary Large Language Models (LLMs), are introduced to students through NVIDIA’s Building Transformer Models course. Through practical exercises, you will learn how to use Transformer models for Natural Language Processing (NLP) tasks like text categorization, translation, and question answering. [...]