<p><b>Be an adaptive thinker that leads the way to Artificial Intelligence</b></p>Key Features<ul>
<li>AI-based examples to guide you in designing and implementing machine intelligence</li>
<li>Develop your own method for future AI solutions</li>
<li>Acquire advanced AI, machine learning, and deep learning design skills</li>
</ul>Book Description<p>Artificial intelligence has the potential to replicate humans in every field. Artificial Intelligence By Example serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies.</p>
<p>Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks.</p>
<p>You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own.</p>
<p>By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop your business vision.</p>What you will learn<ul>
<li>Use adaptive thinking to solve real-life AI case studies</li>
<li>Rise beyond being a modern-day factory code worker</li>
<li>Acquire advanced AI, machine learning, and deep learning designing skills</li>
<li>Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology</li>
<li>Understand future AI solutions and adapt quickly to them</li>
<li>Develop out-of-the-box thinking to face any challenge the market presents</li>
</ul>Who This Book Is For<p>Artificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book.</p>Table of Contents<ol>
<li>Become an Adaptive Thinker</li>
<li>Think Like a Machine</li>
<li>Apply Machine Thinking to a Human Problem</li>
<li>Become an Unconventional Innovator</li>
<li>Manage the Power of Machine Learning and Deep Learning</li>
<li>Don't Get Lost in Techniques – Focus on Optimizing Your Solutions</li>
<li>When and How to Use Artificial Intelligence</li>
<li>Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies</li>
<li>Getting Your Neurons to Work</li>
<li>Applying Biomimicking to Artificial Intelligence</li>
<li>Conceptual Representation Learning</li>
<li>Automated Planning and Scheduling</li>
<li>AI and the Internet of Things (IoT)</li>
<li>Optimizing Blockchains with AI</li>
<li>Cognitive NLP Chatbots</li>
<li>Improve the Emotional Intelligence Deficiencies of Chatbots</li>
<li>Quantum Computers That Think</li>
<li>Appendix - Answers to the Questions</li>
</ol>

Artificial Intelligence By Example: Develop machine intelligence from scratch using real artificial intelligence use cases

Computers & Technology

Rothman, Denis

Computer Science

Packt Publishing

<p><b>Develop real-world applications powered by the latest advances in intelligent systems</b></p> Key Features <ul>
<li>Gain real-world contextualization using deep learning problems concerning research and application </li> <li>Get to know the best practices to improve and optimize your machine learning systems and algorithms </li> <li>Design and implement machine intelligence using real-world AI-based examples </li> </ul> Book Description <p>This Learning Path offers practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. You will be introduced to various machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. You will learn to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open-source Python libraries. </p> <p>Throughout the Learning Path, you'll learn how to develop deep learning applications for machine learning systems. Discover how to attain deep learning programming on GPU in a distributed way. </p> <p>By the end of this Learning Path, you know the fundamentals of AI and have worked through a number of case studies that will help you apply your skills to real-world projects. </p> <p>This Learning Path includes content from the following Packt products: </p> <ul>
<li>Artificial Intelligence By Example by Denis Rothman</li> <li>Python Deep Learning Projects by Matthew Lamons, Rahul Kumar, and Abhishek Nagaraja</li> <li>Hands-On Artificial Intelligence with TensorFlow by Amir Ziai, Ankit Dixit</li> </ul> What you will learn <ul>
<li>Use adaptive thinking to solve real-life AI case studies </li> <li>Rise beyond being a modern-day factory code worker </li> <li>Understand future AI solutions and adapt quickly to them </li> <li>Master deep neural network implementation using TensorFlow </li> <li>Predict continuous target outcomes using regression analysis </li> <li>Dive deep into textual and social media data using sentiment analysis </li> </ul> Who this book is for <p>This Learning Path is for anyone who wants to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. You will learn to extend your machine learning and deep learning knowledge by creating practical AI smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this Learning Path. </p>Table of Contents <ol>
<li>Become an Adaptive Thinker</li> <li>Think Like a Machine</li> <li>Apply Machine Thinking to a Human Problem </li> <li>Become an Unconventional Innovator</li> <li>Manage the Power of Machine Learning and Deep Learning</li> <li>Focus on Optimizing Your Solutions</li> <li>When and How to Use Artificial Intelligence</li> <li>Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies</li> <li>Getting Your Neurons to Work</li> <li>Applying Biomimicking to Artificial Intelligence</li> <li>Conceptual Representation Learning</li> <li>Optimizing Blockchains with AI</li> <li>Cognitive NLP Chatbots</li> <li>Improve the Emotional Intelligence Deficiencies of Chatbots</li> <li>Building Deep Learning Environments</li> <li>Training NN for Prediction Using Regression</li> <li>Generative Language Model for Content Creation</li> <li>Building Speech Recognition with DeepSpeech2</li> <li>Handwritten Digits Classification Using ConvNets</li> <li>Object Detection Using OpenCV and TensorFlow</li> <li>Building Face Recognition Using FaceNet</li> <li>Generative Adversarial Networks</li> <li>From GPUs to Quantum computing - AI Hardware</li> <li>TensorFlow Serving</li>
</ol>

Python: Beginner's Guide to Artificial Intelligence: Build applications to intelligently interact with the world around you using Python

Lamons, Matthew

Kumar, Rahul

Nagaraja, Abhishek

Ziai, Amir

Dixit, Ankit

<p><p><b>Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces.</b></p> <h4>Key Features</h4> <ul> <li>Learn explainable AI tools and techniques to process trustworthy AI results</li> <li>Understand how to detect, handle, and avoid common issues with AI ethics and bias</li> <li>Integrate fair AI into popular apps and reporting tools to deliver business value using Python and associated tools</li> </ul> <h4>Book Description</h4> <p>Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex.</p> <p>Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications.</p> <p>You will build XAI solutions in Python, TensorFlow 2, Google Cloud's XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle.</p> <p>You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces.</p> <p>By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI.</p> <h4>What you will learn</h4> <ul> <li>Plan for XAI through the different stages of the machine learning life cycle</li> <li>Estimate the strengths and weaknesses of popular open-source XAI applications</li> <li>Examine how to detect and handle bias issues in machine learning data</li> <li>Review ethics considerations and tools to address common problems in machine learning data</li> <li>Share XAI design and visualization best practices</li> <li>Integrate explainable AI results using Python models</li> <li>Use XAI toolkits for Python in machine learning life cycles to solve business problems</li> </ul> <h4>Who this book is for</h4> <p>This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book.</p> <p>Some of the potential readers of this book include:</p> <ul> <li>Professionals who already use Python for as data science, machine learning, research, and analysis</li> <li>Data analysts and data scientists who want an introduction into explainable AI tools and techniques</li> <li>AI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications</li> </ul></p>

Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps

<p><p><b>Take your NLP knowledge to the next level and become an AI language understanding expert by mastering the quantum leap of Transformer neural network models</b></p> <h4>Key Features</h4> <ul> <li>Build and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning models</li> <li>Go through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine</li> <li>Test transformer models on advanced use cases</li> </ul> <h4>Book Description</h4> <p>The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers.</p> <p>The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.</p> <p>The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification.</p> <p>By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.</p> <h4>What you will learn</h4> <ul> <li>Use the latest pretrained transformer models</li> <li>Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models</li> <li>Create language understanding Python programs using concepts that outperform classical deep learning models</li> <li>Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP</li> <li>Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more</li> <li>Measure the productivity of key transformers to define their scope, potential, and limits in production</li> </ul> <h4>Who this book is for</h4> <p>Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers.</p> <p>Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.</p></p>

Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

<p>Review\n"Transformers for Natural Language Processing, Second Edition, is a reference for everyone interested in understanding how transformers work both from a theoretical and practical perspective. The author does a tremendous job of explaining how to use transformers step by step with a hands-on approach. After reading this book, you will be ready to use this state-of-the-art set of techniques for empowering your deep learning applications, including popular models such as BERT, RoBERTa, T5, and GPT-3.<br />
The first edition always has a place on my desk, and now so will the second edition." --<br />
Antonio Gulli, Engineering Director for the Office of the CTO, Google\nLearn how to use and implement transformers with Hugging Face and OpenAI (and others) by reading, running examples, investigating issues, asking the author questions, and interacting with our AI/ML community Key Features Pretrain a BERT-based model from scratch using Hugging Face Fine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your data Perform root cause analysis on hard NLP problems Book Description<br />
Transformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs?<br />
Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses.<br />
You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model.<br />
If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides.<br />
The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details).<br />
You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using Codex.<br />
By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective! What you will learn Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-E Discover new techniques to investigate complex language problems Compare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformers Carry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3 Measure the productivity of key transformers to define their scope, potential, and limits in production Who this book is for<br />
If you want to learn about and apply transformers to your natural language (and image) data, this book is for you.<br />
You'll need a good understanding of Python and deep learning and a basic understanding of NLP to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters. And, don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community and author, Denis Rothman. So, he'll be there to guide you on your transformers journey! Table of Contents What are Transformers? Getting Started with the Architecture of the Transformer Model Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer The Rise of Suprahuman Transformers with GPT-3 Engines Applying Transformers to Legal and Financial Documents for AI Text Summarization Matching Tokenizers and Datasets Semantic Role Labeling with BERT-Based Transformers Let</p>

Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

<p><b>Unleash the full potential of transformers with this comprehensive guide covering architecture, capabilities, risks, and practical implementations on OpenAI, Google Vertex AI, and Hugging FacePurchase of the print or Kindle book includes a free eBook in PDF format</b> <h4>Key Features</h4> <ul> <li>Master NLP and vision transformers, from the architecture to fine-tuning and implementation</li> <li>Learn how to apply Retrieval Augmented Generation (RAG) with LLMs using customized texts and embeddings</li> <li>Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases</li> </ul> <h4>Book Description</h4> Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV).The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You'll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You'll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs.Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication.This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices. <h4>What you will learn</h4> <ul> <li>Learn how to pretrain and fine-tune LLMs</li> <li>Learn how to work with multiple platforms, such as Hugging Face, OpenAI, and Google Vertex AI</li> <li>Learn about different tokenizers and the best practices for preprocessing language data</li> <li>Implement Retrieval Augmented Generation and rules bases to mitigate hallucinations</li> <li>Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP</li> <li>Create and implement cross-platform chained models, such as HuggingGPT</li> <li>Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V</li> </ul> <h4>Who this book is for</h4> <p>This book is ideal for NLP and CV engineers, software developers, data scientists, machine learning engineers, and technical leaders looking to advance their LLMs and generative AI skills or explore the latest trends in the field.Knowledge of Python and machine learning concepts is required to fully understand the use cases and code examples. However, with examples using LLM user interfaces, prompt engineering, and no-code model building, this book is great for anyone curious about the AI revolution.</p> ]]></p>

Transformers for Natural Language Processing and Computer Vision: Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3

<p><b>Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedbackPurchase of the print or Kindle book includes a free eBook in PDF format</b> <h4>Key Features</h4> <ul> <li>Implement RAG's traceable outputs, linking each response to its source document to build reliable multimodal conversational agents</li> <li>Deliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphs</li> <li>Balance cost and performance between dynamic retrieval datasets and fine-tuning static data</li> </ul> <h4>Book Description</h4> RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs.This book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. You'll discover techniques to optimize your project's performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs.You'll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project. <h4>What you will learn</h4> <ul> <li>Scale RAG pipelines to handle large datasets efficiently</li> <li>Employ techniques that minimize hallucinations and ensure accurate responses</li> <li>Implement indexing techniques to improve AI accuracy with traceable and transparent outputs</li> <li>Customize and scale RAG-driven generative AI systems across domains</li> <li>Find out how to use Deep Lake and Pinecone for efficient and fast data retrieval</li> <li>Control and build robust generative AI systems grounded in real-world data</li> <li>Combine text and image data for richer, more informative AI responses</li> </ul> <h4>Who this book is for</h4> <p>This book is ideal for data scientists, AI engineers, machine learning engineers, and MLOps engineers. If you are a solutions architect, software developer, product manager, or project manager looking to enhance the decision-making process of building RAG applications, then you'll find this book useful.</p> ]]></p>

RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone

<p><b>Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples.</b></p>Key Features<ul>
<li>AI-based examples to guide you in designing and implementing machine intelligence</li>
<li>Build machine intelligence from scratch using artificial intelligence examples</li>
<li>Develop machine intelligence from scratch using real artificial intelligence </li>
</ul>Book Description<p>Artificial intelligence (AI) has the potential to replicate humans in every field. Artificial Intelligence by Example serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples.</p>
<p>This book will make you an adaptive thinker and help you apply concepts to real world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to a cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs).</p>
<p>This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with Decision Trees, Random Forests, combining deep learning and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, brain-computer interface (BCI) and genetic algorithms.</p>
<p>By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.</p>What you will learn<ul>
<li>Apply KNN to language translations and explore the opportunities in Google Translate</li>
<li>Chained algorithms combining unsupervised learning with decision trees</li>
<li>Solve the XOR problem with FNN (Feedforward Neural Networks) and build its architecture to represent a data flow graph</li>
<li>Learn about meta learning models with Hybrid Neural Networks</li>
<li>Create a chatbot and optimize its emotional intelligence deficiencies with RNN &amp; LSTM</li>
<li>Building Conversational User Interfaces(CUI) for Chatbots</li>
<li>Writing genetic algorithms that optimize deep learning neural networks</li>
<li>Building Quantum computing circuits</li>
</ul>Who This Book Is For<p>Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.</p>

Artificial Intelligence By Example: Acquire advanced AI, machine learning, and deep learning design skills, 2nd Edition

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