Understand and implement the attention mechanism, a key element of transformer-based LLMs, using PyTorch.

Learn how the attention mechanism in LLMs helps convert base token embeddings into rich context-aware embeddings. Learn how the attention mechanism in LLMs helps convert base token embeddings into rich context-aware embeddings.Understand the Query, Key, and Value matrices, what they are for, how to produce them, and how to use them in attention. Understand the Query, Key, and Value matrices, what they are for, how to produce them, and how to use them in attention.Learn the difference between self-attention, masked self-attention, and cross-attention, and how multi-head attention scales the algorithm. Learn the difference between self-attention, masked self-attention, and cross-attention, and how multi-head attention scales the algorithm._This course clearly explains the ideas behind the attention mechanism. It walks through the algorithm itself and how to code it in Pytorch. Attention in Transformers: Concepts and Code in PyTorch, was built in collaboration with StatQuest, and taught by its Founder and CEO, Josh Starmer.The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper “Attention is All You Need” by Ashish Vaswani and others, revolutionized AI with their scalable design. Learn how this foundational architecture works to improve your intuition about building reliable, functional, and scalable AI applications.What you’ll do: Understand the evolution of the attention mechanism, a key breakthrough that led to transformers.Learn the relationships between word embeddings, positional embeddings, and attention.Learn about the Query, Key, and Value matrices, how to produce them, and how to use them in attention.Go through the math required to calculate self-attention and masked self-attention to learn how and why the equation works the way it does.Understand the difference between self-attention and masked self-attention, and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs.Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention, and how they are incorporated into a transformer.Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention._Anyone who has basic Python knowledge and wants to learn how the attention mechanism in LLMs like ChatGPT works.

Attention in Transformers: Concepts and Code in PyTorch

Learn and build diffusion models from the ground up, understanding each step. Learn about diffusion models in use today and implement algorithms to speed up sampling.

Understand diffusion models in use today Understand diffusion models in use today.Build your own diffusion model, and learn to train it  Build your own diffusion model, and learn to train it.Implement algorithms to speed up sampling 10x Implement algorithms to speed up sampling 10x._In How Diffusion Models Work, you will gain a deep familiarity with the diffusion process and the models which carry it out. More than simply pulling in a pre-built model or using an API, this course will teach you to build a diffusion model from scratch.In this course you will:Explore the cutting-edge world of diffusion-based generative AI and create your own diffusion model from scratch.Gain deep familiarity with the diffusion process and the models driving it, going beyond pre-built models and APIs.Acquire practical coding skills by working through labs on sampling, training diffusion models, building neural networks for noise prediction, and adding context for personalized image generation.At the end of the course, you will have a model that can serve as a starting point for your own exploration of diffusion models for your applications.This one-hour course, taught by Sharon Zhou will expand your generative AI capabilities to include building, training, and optimizing diffusion models.Hands-on examples make the concepts easy to understand and build upon. Built-in Jupyter notebooks allow you to seamlessly experiment with the code and labs presented in the course. _How Diffusion Models Work is an intermediate-level course. Knowledge of Python, Tensorflow, or Pytorch will help you get the most out of this course.

How Diffusion Models Work

Deploy AI for edge devices and smartphones. Learn model conversion, quantization, and how to modify for deployment on diverse devices. 

Learn to deploy AI models on edge devices like smartphones, using their local compute power for faster and more secure inference. Learn to deploy AI models on edge devices like smartphones, using their local compute power for faster and more secure inference.Explore model conversion by, converting your PyTorch/TensorFlow models for device compatibility, and quantize them to achieve performance gains while reducing model size. Explore model conversion by, converting your PyTorch/TensorFlow models for device compatibility, and quantize them to achieve performance gains while reducing model size.Learn about device integration, including runtime dependencies, and how GPU, NPU, and CPU compute unit utilization affect performance. Learn about device integration, including runtime dependencies, and how GPU, NPU, and CPU compute unit utilization affect performance._As AI moves beyond the cloud, on-device inference is rapidly expanding to smartphones, IoT devices, robots, AR/VR headsets, and more. Billions of mobile and other edge devices are ready to run optimized AI models. This course equips you with key skills to deploy AI on device:Explore how deploying models on device reduces latency, enhances efficiency, and preserves privacy.Go through key concepts of on-device deployment such as neural network graph capture, on-device compilation, and hardware acceleration.Convert pretrained models from PyTorch and TensorFlow for on-device compatibility.Deploy a real-time image segmentation model on device with just a few lines of code.Test your model performance and validate numerical accuracy when deploying to on-device environmentsQuantize and make your model up to 4x faster and 4x smaller for higher on-device performance.See a demonstration of the steps for integrating the model into a functioning Android app.Learn from Krishna Sridhar, Senior Director of Engineering at Qualcomm, who has played a pivotal role in deploying over 1,000 models on devices and, with his team, has created the infrastructure used by over 100,000 applications.By learning these techniques, you’ll be positioned to develop and deploy AI to billions of devices and optimize your complex models to run efficiently on the edge._This course is designed for beginner AI developers, ML engineers, data scientists, and mobile developers looking to deploy optimized models on edge devices. Familiarity with Python, as well as PyTorch or TensorFlow is recommended.

Introduction to on-device AI

Understand the transformer architecture that powers LLMs to use them more effectively.

Gain an understanding of the key components of transformers, including tokenization, embeddings, self-attention, and transformer blocks, to build a strong technical foundation. Gain an understanding of the key components of transformers, including tokenization, embeddings, self-attention, and transformer blocks, to build a strong technical foundation.Understand recent transformer improvements to the attention mechanism such as KV cache, multi-query attention, grouped query attention, and sparse attention. Understand recent transformer improvements to the attention mechanism such as KV cache, multi-query attention, grouped query attention, and sparse attention.Compare tokenization strategies used in modern LLMS and explore transformers in the Hugging Face Transformers library. Compare tokenization strategies used in modern LLMS and explore transformers in the Hugging Face Transformers library._Introducing “How Transformer LLMs Work,” created with Jay Alammar and Maarten Grootendorst, authors of the “Hands-On Large Language Models” book. This course offers a deep dive into the main components of the transformer architecture that powers large language models (LLMs). The transformer architecture revolutionized generative AI. In fact, the “GPT” in ChatGPT stands for “Generative Pre-Trained Transformer.” Originally introduced in the groundbreaking 2017 paper Attention Is All You Need, by Ashish Vaswani and others, transformers were a highly scalable model for machine translation tasks. Variants of this architecture now power today’s LLMs such as those from OpenAI, Google, Meta, Cohere, and Anthropic.In their book, Jay and Maarten beautifully illustrated the underlying architecture of LLMs through insightful and easy-to-understand explanations.In this course, you’ll learn how a transformer network architecture that powers LLMs works. You’ll build the intuition of how LLMs process text and work with code examples that illustrate the key components of the transformer architecture.Key topics covered in this course include:The evolution of how language has been represented numerically, from the Bag-of-Words model through Word2Vec embeddings to the transformer architecture that captures word meanings in full context.How LLM inputs are broken down into tokens, which represent words or pieces before they are sent to the language model.The details of a transformer and the three main stages, consisting of tokenization and embedding, the stack of transformer blocks, and the language model head.The details of the transformer block, including attention, which calculates relevance scores followed by the feedforward layer, which incorporates stored information learned in training.How cached calculations make transformers faster, how the transformer block has evolved over the years since the original paper was released, and how they continue to be widely used.Explore an implementation of recent models in the Hugging Face transformer library.By the end of this course,  you’ll have a deep understanding of how LLMs process language and you’ll be able to read through papers describing models and understand the details that are used to describe these architectures. This intuition will help improve your approach to building  LLM applications._Anyone interested in understanding the inner workings of transformer architectures that power today’s LLMs.