Author: Oluwole Fagbohun
Publisher: Packt Publishing Ltd
ISBN: 180324920X
Category : Computers
Languages : en
Pages : 350
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Book Description
Achieve TensorFlow certification with this comprehensive guide covering all exam topics using a hands-on, step-by-step approach—perfect for aspiring TensorFlow developers Key Features Build real-world computer vision, natural language, and time series applications Learn how to overcome issues such as overfitting with techniques such as data augmentation Master transfer learning—what it is and how to build applications with pre-trained models Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe TensorFlow Developer Certificate Guide is an indispensable resource for machine learning enthusiasts and data professionals seeking to master TensorFlow and validate their skills by earning the certification. This practical guide equips you with the skills and knowledge necessary to build robust deep learning models that effectively tackle real-world challenges across diverse industries. You’ll embark on a journey of skill acquisition through easy-to-follow, step-by-step explanations and practical examples, mastering the craft of building sophisticated models using TensorFlow 2.x and overcoming common hurdles such as overfitting and data augmentation. With this book, you’ll discover a wide range of practical applications, including computer vision, natural language processing, and time series prediction. To prepare you for the TensorFlow Developer Certificate exam, it offers comprehensive coverage of exam topics, including image classification, natural language processing (NLP), and time series analysis. With the TensorFlow certification, you’ll be primed to tackle a broad spectrum of business problems and advance your career in the exciting field of machine learning. Whether you are a novice or an experienced developer, this guide will propel you to achieve your aspirations and become a highly skilled TensorFlow professional. What you will learn Prepare for success in the TensorFlow Developer Certification exam Master regression and classification modelling with TensorFlow 2.x Build, train, evaluate, and fine-tune deep learning models Combat overfitting using techniques such as dropout and data augmentation Classify images, encompassing preprocessing and image data augmentation Apply TensorFlow for NLP tasks like text classification and generation Predict time series data, such as stock prices Explore real-world case studies and engage in hands-on exercises Who this book is forThis book is for machine learning and data science enthusiasts, as well as data professionals aiming to demonstrate their expertise in building deep learning applications with TensorFlow. Through a comprehensive hands-on approach, this book covers all the essential exam prerequisites to equip you with the skills needed to excel as a TensorFlow developer and advance your career in machine learning. A fundamental grasp of Python programming is the only prerequisite.
Author: Patrick J
Publisher: GitforGits
ISBN: 8119177746
Category : Computers
Languages : en
Pages : 296
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Book Description
Designed with both beginners and professionals in mind, the book is meticulously structured to cover a broad spectrum of concepts, applications, and hands-on practices that form the core of the TensorFlow Developer Certificate exam. Starting with foundational concepts, the book guides you through the fundamental aspects of TensorFlow, Machine Learning algorithms, and Deep Learning models. The initial chapters focus on data preprocessing, exploratory analysis, and essential tools required for building robust models. The book then delves into Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and advanced neural network techniques such as GANs and Transformer Architecture. Emphasizing practical application, each chapter is peppered with detailed explanations, code snippets, and real-world examples, allowing you to apply the concepts in various domains such as text classification, sentiment analysis, object detection, and more. A distinctive feature of the book is its focus on various optimization and regularization techniques that enhance model performance. As the book progresses, it navigates through the complexities of deploying TensorFlow models into production. It includes exhaustive sections on TensorFlow Serving, Kubernetes Cluster, and edge computing with TensorFlow Lite. The book provides practical insights into monitoring, updating, and handling possible errors in production, ensuring a smooth transition from development to deployment. The final chapters are devoted to preparing you for the TensorFlow Developer Certificate exam. From strategies, tips, and coding challenges to a summary of the entire learning journey, these sections serve as a robust toolkit for exam readiness. With hints and solutions provided for challenges, you can assess your knowledge and fine-tune your problem solving skills. In essence, this book is more than a mere certification guide; it's a complete roadmap to mastering TensorFlow. It aligns perfectly with the objectives of the TensorFlow Developer Certificate exam, ensuring that you are not only well-versed in the theoretical aspects but are also skilled in practical applications. Key Learnings Comprehensive guide to TensorFlow, covering fundamentals to advanced topics, aiding seamless learning. Alignment with TensorFlow Developer Certificate exam, providing targeted preparation and confidence. In-depth exploration of neural networks, enhancing understanding of model architecture and function. Hands-on examples throughout, ensuring practical understanding and immediate applicability of concepts. Detailed insights into model optimization, including regularization, boosting model performance. Extensive focus on deployment, from TensorFlow Serving to Kubernetes, for real-world applications. Exploration of innovative technologies like BiLSTM, attention mechanisms, Transformers, fostering creativity. Step-by-step coding challenges, enhancing problem-solving skills, mirroring real-world scenarios. Coverage of potential errors in deployment, offering practical solutions, ensuring robust applications. Continual emphasis on practical, applicable knowledge, making it suitable for all levels Table of Contents Introduction to Machine Learning and TensorFlow 2.x Up and Running with Neural Networks Building Basic Machine Learning Models Image Recognition with CNN Object Detection Algorithms Text Recognition and Natural Language Processing Strategies to Prevent Overfitting & Underfitting Advanced Neural Networks for NLP Productionizing TensorFlow Models Preparing for TensorFlow Developer Certificate Exam
Author: Jayant Deshmukh
Publisher: Jayant Deshmukh
ISBN:
Category : Computers
Languages : en
Pages : 105
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Book Description
Building a Career in AI: A Practical Guide for Aspiring Professionals Artificial intelligence is reshaping industries, creating new opportunities, and revolutionizing the way we work and live. Are you ready to become part of this transformation? Whether you're a student curious about AI or a professional considering a career shift, this book is your ultimate guide to building a rewarding career in one of the most dynamic fields of our time. Written by Jayant Deshmukh, a Certified Project Management Professional (PMP), accomplished AI practitioner, and seasoned leader in digital transformation, this book combines deep expertise with a human touch. Jayant has worked with top global financial institutions, orchestrating transformative AI-driven initiatives, and has traveled extensively, gaining unique insights into diverse cultures, industries, and challenges. With this wealth of experience, he delivers an engaging and practical roadmap tailored for aspiring AI professionals. What This Book Offers This isn’t just another technical manual—it’s a hands-on, inspiring journey into the world of AI. Building a Career in AI demystifies complex concepts and equips you with the tools, skills, and strategies you need to succeed. A Beginner-Friendly Approach: Complex AI terms like machine learning, neural networks, and data science are explained in simple, relatable language, making them accessible even to those new to technology. Step-by-Step Guidance: Learn how to acquire essential skills like Python programming, mathematics, and domain knowledge. Follow clear roadmaps to build your expertise, whether you're starting from scratch or transitioning from another field. Practical Resources: Discover the best online courses, books, certifications, and tools to enhance your learning. Get insights into platforms like TensorFlow, PyTorch, and Kaggle, and learn how to build a portfolio of AI projects that stand out. Real-Life Stories: Be inspired by the journeys of individuals who started with no technical background but successfully transitioned into AI careers. From college graduates to mid-career professionals, these stories prove that success in AI is achievable for anyone with determination. Career Strategies: Master the art of building a personal brand through LinkedIn, GitHub, and Kaggle. Gain insider tips for crafting resumes, acing interviews, and presenting your projects effectively. Future-Proofing Your Career: Stay updated with emerging trends like generative AI, and learn how to evolve into leadership roles, from practitioner to strategist. Why This Book Matters The field of AI is rapidly growing, with a global demand for skilled professionals outpacing supply. This creates unparalleled opportunities for those who are prepared. However, starting your journey can feel overwhelming. This book bridges the gap, providing a clear, actionable framework to help you navigate the AI landscape with confidence. Jayant’s unique perspective—combining technical expertise, global industry experience, and an empathetic understanding of aspiring professionals’ challenges—ensures that every chapter is both practical and inspiring. His engaging storytelling, combined with motivational quotes and interactive exercises, makes this book more than a guide; it’s a mentor on your AI journey. Who Should Read This Book Students: If you’re in college and curious about AI, this book will guide you through building foundational skills, exploring career paths, and preparing for the job market. Professionals: If you’re looking to transition into AI from another field, you’ll find step-by-step strategies and inspiring examples to help you pivot successfully. Aspiring Innovators: If you dream of leveraging AI to create meaningful solutions, this book will equip you with the mindset, tools, and knowledge to make an impact. Start Your AI Journey Today The future belongs to those who embrace change and seize new opportunities. With Building a Career in AI, you’ll gain not only technical knowledge but also the confidence and motivation to take the first step—and every step after—toward a fulfilling career in AI. "The only limit to our realization of tomorrow will be our doubts of today." — Franklin D. Roosevelt This is more than a book; it’s your companion in navigating the exciting and ever-evolving world of artificial intelligence. Whether you’re starting small or dreaming big, your journey begins here. Take the leap, embrace the possibilities, and let this book guide you to a future shaped by your potential and the limitless power of AI. Are you ready to build your career in AI? The time is now!
Author: William Hawkins
Publisher: Springer Nature
ISBN:
Category :
Languages : en
Pages : 193
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Book Description
Author: Noah Gift
Publisher: "O'Reilly Media, Inc."
ISBN: 1098102983
Category : Computers
Languages : en
Pages : 461
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Book Description
Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
Author: Luciano Ramalho
Publisher: "O'Reilly Media, Inc."
ISBN: 1491946253
Category : Computers
Languages : en
Pages : 755
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Book Description
Python’s simplicity lets you become productive quickly, but this often means you aren’t using everything it has to offer. With this hands-on guide, you’ll learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—features. Author Luciano Ramalho takes you through Python’s core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time. Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3. This book covers: Python data model: understand how special methods are the key to the consistent behavior of objects Data structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode age Functions as objects: view Python functions as first-class objects, and understand how this affects popular design patterns Object-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritance Control flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packages Metaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work
Author: Mattmann A. Chris
Publisher: Manning
ISBN: 1617297712
Category : Computers
Languages : en
Pages : 454
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Book Description
Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape
Author: Aurélien Géron
Publisher: "O'Reilly Media, Inc."
ISBN: 149203259X
Category : Computers
Languages : en
Pages : 830
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Book Description
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Author: Tom Hope
Publisher: "O'Reilly Media, Inc."
ISBN: 1491978481
Category : Computers
Languages : en
Pages : 242
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Book Description
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
