Advanced Attention

Transformers Explained - How transformers work

Transformer Neural Networks - EXPLAINED! (Attention is all you need)

Illustrated Guide to Transformers Neural Network: A step by step explanation

Learn Transformers, explained: Understand the model behind GPT, BERT, and T5

Complete natural language processing specialization covering transformers, attention mechanisms, and modern NLP techniques.

Welcome to "LL Ms Mastery: Complete Guide to Generative AI &amp; Transformers"!This practical course is designed to equip you with the knowledge and skills to build efficient, production-ready Large Language Models using cutting-edge technologies.Key Topics Covered:Generative AI: Understand the principles and applications of Generative AI in creating new data instances.ChatGPT &amp; GPT4: Dive into the workings of advanced AI models like ChatGPT and GPT4.LL Ms: Start with the basics of LL Ms, learning how they decode, process inputs and outputs, and how they are taught to communicate effectively.Encoder-Decoders: Master the concept of encoder-decoder models in the context of Transformers.T5, GPT2, BERT: Get hands-on experience with popular Transformer models such as T5, GPT2, and BERT.Machine Learning &amp; Data: Understand the role of machine learning and data in training robust AI models.Advanced Techniques: Sophisticated training strategies like PeFT, Lo Ra, managing data memory and merging adapters.Specialised Skills:&nbsp; Cutting-edge training techniques, including 8-bit, 4-bit training and Flash-Attention.Scalable Solutions: Master the use of advanced tools like Deep Speed and FSDP to efficiently scale model training.Course Benefits:• Career Enhancement: Position yourself as a valuable asset in tech teams, capable of tackling significant AI challenges and projects.• <s

Unlock the potential of Generative AI with our comprehensive course, "Mastering Generative AI: LL Ms, Prompt Engineering &amp; More." This course is designed for both beginners and seasoned developers looking to deepen their understanding of the rapidly evolving field of artificial intelligence.In this course, you will explore a wide range of essential topics, including:· Python Programming: Learn the fundamentals of Python, the go-to language for AI development, and become proficient in data manipulation using libraries like Pandas and Num Py.· Natural Language Processing (NLP): Dive into the world of NLP, mastering techniques for text processing, feature extraction, and leveraging powerful libraries like NLTK and Spa Cy.· Deep Learning and Transformers: Understand the architecture of Transformer models, which are at the heart of many state-of-the-art AI applications. Discover the principles of deep learning and how to implement neural networks using TensorFlow and PyTorch.· Large Language Models (LL Ms): Gain insights into LL Ms, their training, and fine-tuning processes. Learn how to effectively use these models in various applications, from chatbots to content generation.· Retrieval-Augmented Generation (RAG): Explore the innovative concept of RAG, which combines retrieval techniques with generative models to enhance AI performance.· Prompt Engineering: Master the art of crafting effective prompts to improve the interaction with LL Ms and optimize the output for specific tasks.· Vector Databases: Discover how to implement and utilize vector databases for storing and retrieving high-dimensional data, a crucial skill in managing AI-generated content.The course culminates in a Capstone Project, where you will apply everything you've learned to solve a real-world problem using Generative AI te

Do not take this course if you are an ML beginner. This course is designed for those who are interested in pure coding and want to fine-tune LL Ms instead of focusing on prompt engineering. Otherwise, you may find it difficult to understand.Welcome to "Mastering Transformer Models and LLM Fine Tuning", a comprehensive and practical course designed for all levels, from beginners to advanced practitioners in Natural Language Processing (NLP). This course delves deep into the world of Transformer models, fine-tuning techniques, and knowledge distillation, with a special focus on popular BERT variants like Phi2, LLAMA, T5, BERT, DistilBERT, MobileBERT, and TinyBERT.Course Overview:Section 1: Introduction Get an overview of the course and understand the learning outcomes.Introduction to the resources and code files you will need throughout the course.Section 2: Understanding Transformers with Hugging Face Learn the fundamentals of Hugging Face Transformers.Explore Hugging Face pipelines, checkpoints, models, and datasets.Gain insights into Hugging Face Spaces and Auto-Classes for seamless model management.Section 3: Core Concepts of Transformers and LL Ms Delve into the architectures and key concepts behind Transformers.Understand the applications of Transformers in various NLP tasks.Introduction to transfer learning with Transformers.Section 4: BERT Architecture Deep Dive Detailed exploration of BERT's architecture and its importance in context understanding.Learn about Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) in BERT.Understand BERT fine-tuning and evaluation techniques.Section 5: Practical Fine-Tuning with BERT</strong

"This course contains the use of artificial intelligence in creating scripts, visuals, audio, and supporting content"This 8-week course is a complete foundation in Generative AI and Large Language Models (LL Ms), designed to help you build both conceptual understanding and practical skills. The program is structured to gradually move from the basics of generative models to advanced applications, customization, safety, and a capstone project that showcases your abilities. The course begins with an introduction to Generative AI, where you will explore tokenization, attention mechanisms, and the transformer architecture that forms the backbone of modern LL Ms. You will learn how text generation works, experiment with prompt design, and analyze the impact of model parameters like temperature and top-p on creativity and accuracy. Building on this, the course dives into the foundations of large language models, exploring embeddings, perplexity, and context windows. You will also study core generative models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models, gaining an intuitive understanding of how these models generate text, images, and structured data. The practical modules allow you to apply Generative AI in practice, including summarization, creative writing, code generation, data augmentation, and image synthesis. You will use modern <strong

This course was designed with the support of AI to provide an improved learning.Transform yourself from someone who struggles with AI buzzwords into a confident Natural Language Processing expert who understands both the foundational science and cutting-edge innovations that power today's AI revolution. This comprehensive course, developed with AI assistance, takes you on a complete journey from classical linguistics to the Transformer architecture behind ChatGPT, BERT, and every modern language model.Master the Complete NLP Pipeline From Classical Methods to Modern AI• Build rock-solid foundations with computational linguistics, morphology, and semantic analysis• Implement classic algorithms like TF-IDF, Hidden Markov Models, and Part-of-Speech tagging• Understand the revolutionary shift from RNNs to Transformers and why attention mechanisms changed everything• Decode the science behind BERT, GPT, and how RLHF makes AI assistants helpful and harmless• Navigate the ethical implications of bias in language models with practical mitigation strategies• Explore cutting-edge multimodal AI where vision meets language in models like CLIP and LLaVA• Grasp the geopolitical landscape of AI development, from data sovereignty to the global "chip war"This isn't just another coding tutorial – it's your complete guide to understanding how machines truly comprehend human language.The demand for NLP expertise has exploded by 400% over the past 3 years, with companies desperately seeking professionals who understand both the technical foundations and practical applications. While others struggle with surface-level tutorials, you'll gain deep comprehension of the underlying mechanisms that drive a $43 billion industry. The pressure to implement AI soluti

Deep Learning is a hot topic today! This is because of the impact it's having in several industries. One of the fields in which deep learning has the most influence today is Natural Language Processing.To understand why Deep Learning based Natural Language Processing is so popular; it suffices to take a look at the different domains where giving a computer the power to understand and make sense out of text and generate text has changed our lives.Some applications of Natural Language Processing are in:Helping people around the world learn about any topic ChatGPT Helping developers code more efficiently with Github Copilot.Automatic topic recommendation in our Twitter feeds Automatic Neural Machine Translation with&nbsp; Google TranslateE-commerce search engines like those of Amazon Correction of Grammar with Grammarly The demand for Natural Language Processing engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using TensorFlow 2 (the world's most popular library for deep learning, built by Google) and Hugging Face transformers (most popular NLP focused&nbsp;library ). We shall start by understanding how to build very simple models (like Linear regression model for car price prediction and RNNs text classifiers for movie revi

Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again.In this course, we cover everything you need to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR.We cover several key NLP frameworks including:Hugging Face's Transformers TensorFlow 2Py Torchspa CyNLTK Flair And learn how to apply transformers to some of the most popular NLP use-cases:Language classification/sentiment analysis Named entity recognition (NER)Question and Answering Similarity/comparative learning Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:History of NLP and where transformers come from Common preprocessing techniques for NLP The theory behind transformers How to fine-tune transformers We cover all this and more, I look forward to seeing you in the course!

Unlock the power of modern Natural Language Processing (NLP) and elevate your skills with this comprehensive course on NLP with a focus on Transformers. This course will guide you through the essentials of Transformer models, from understanding the attention mechanism to leveraging pre-trained models.&nbsp; If so, then this course is for you what you need! We have divided this course into Chapters. In each chapter, you will be learning a new concept for Natural Language Processing with Transformers. These are some of the topics that we will be covering in this course:Starting from an introduction to NLP and setting up your Python environment, you'll gain hands-on experience with text preprocessing methods, including tokenization, stemming, lemmatization, and handling special characters. You will learn how to represent text data effectively through Bag of Words, n-grams, and TF-IDF, and explore the groundbreaking Word2Vec model with practical coding exercises.Dive deep into the workings of transformers, including self-attention, multi-head attention, and the role of position encoding. Understand the architecture of transformer encoders and decoders and learn how to train and use these powerful models for real-world applications.The course features projects using state-of-the-art pre-trained models from Hugging Face, such as BERT for sentiment analysis and T5 for text translation. With guided coding exercises and step-by-step project walkthroughs, you’ll solidify your understanding and build your confidence in applying these models to complex NLP tasks.By the end of this course, you’ll be equipped with practical skills to tackle NLP challenges, build robust solutions, and a

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, Gemini Pro, Llama 3, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Hello friends!Welcome to Data Science: Transformers for Natural Language Processing.Ever since Transformers arrived on the scene, deep learning hasn't been the same.Machine learning is able to generate text essentially indistinguishable from that created by humans We've reached new state-of-the-art performance in many NLP&nbsp;tasks, such as machine translation, question-answering, entailment, named entity recognition, and more We've created multi-modal (text and image) models that can generate amazing art using only a text prompt We've solved a longstanding problem in molecular biology known as "protein structure prediction"In this course, you will learn very practical skills for applying transformers, and if you want, detailed theory behind how transformers and attention work.This is different from most other resources, which only cover the former.The course is split into 3 major parts:Using Transformers Fine-Tuning Transformers Transformers In-DepthPART&nbsp;1: Using Transformers In this section, you will learn how to use transformers which were trained for you. This costs millions of dollars to do, so it's not something you want to try by yourself!We'll see how these prebuilt models can already be used for a wide array of tasks, including:text classification (e.g. spam detection, sentiment analysis, document categorization)named entity recognitiontext summarizationmachine transla

Note: Have opened many videos for preview&nbsp; please only enroll if you follow the preview video's , Any suggestions or modifications ping me in Q&amp;A will respond within 3 business days in most times worst case 7 days if i am travelling&nbsp; Current Topics&nbsp; What are Transformers?&nbsp; (Technical) Concept of RNN3 main concepts in Transformers Positional Encoding Attention Self-Attention What is ChatGPT?&nbsp; (Technical) How You Can Use ChatGPT (Non-Technical) Creating your Generative Pre-trained Transformer 3 (GPT-3) account Basic Querying Generative Pre-trained Transformer 3 (GPT-3)&nbsp; and how ethical principles are upheld&nbsp;Prompt Engineering&nbsp; (Technical) Best Practices for&nbsp; Prompt Engineering OpenAI Models (Technical) &nbsp; &nbsp;We will explore when to use OpenAI&nbsp;models GPT3 Codex Content Filter Parameters in Playground (Technical) Temperature Max Tokens Stop Sequence Top-PInjecting Start &amp; Restart Text Frequency and Presence Penalty Best of Show Probabilities How is ChatGPT Trained?&nbsp; &nbsp;(Technical) What is Generative Pre-trained Transformer 3 (GPT-3)&nbsp; and how is it different from rest of transformers ChatGPT- AI Take Over ?&nbsp; (Non-Technical) Money making ideas with ChatGPT &nbsp; (Non-Technical) fiverr exampleWRITING &amp; TRANSLATION Social Media Marketing Art