<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

Computers & Technology

Rothman, Denis

Lamons, Matthew

Kumar, Rahul

Computer Science

Packt Publishing

<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

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