Practical Deep Learning In Python
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Practical Deep Learning
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Author : Ronald T. Kneusel
language : en
Publisher: No Starch Press
Release Date : 2021-02-23
Practical Deep Learning written by Ronald T. Kneusel and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-23 with Computers categories.
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about artificial intelligence and machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
Practical Deep Learning With Python
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Author : Lalasa Mukku
language : en
Publisher: Notion Press
Release Date : 2024-05-30
Practical Deep Learning With Python written by Lalasa Mukku and has been published by Notion Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-30 with Education categories.
This book is written for people with Python programming experience who want to get started with machine learning and deep learning. But this book can also be valuable to many different types of readers: If you're a data scientist familiar with machine learning, this book will provide you with a solid, practical introduction to deep learning, the fastest-growing and most significant subfield of machine learning. If you're a deep-learning expert looking to get started with the Keras framework, you'll find this book to be the best Keras crash course available. If you're a graduate student studying deep learning in a formal setting, you'll find this book to be a practical complement to your education, helping you build intuition around the behavior of deep neural networks and familiarizing you with key best practices. Even technically minded people who don't code regularly will find this book useful as an introduction to both basic and advanced deep-learning concepts. In order to use Keras, you'll need reasonable Python proficiency. Additionally, familiarity with the Numpy library will be helpful, although it isn't required. You don't need previous experience with machine learning or deep learning: this book covers from scratch all the necessary basics. You don't need an advanced mathematics background, either-high school-level mathematics should suffice in order to follow along.
Practical Deep Learning In Python
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Author : Marcus C Lauritsen
language : en
Publisher: Independently Published
Release Date : 2025-08
Practical Deep Learning In Python written by Marcus C Lauritsen and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08 with Computers categories.
Unlock the Power of Deep Learning-No Experience Needed Are you fascinated by artificial intelligence but overwhelmed by where to begin? Do the endless tutorials, frameworks, and jargon make deep learning seem out of reach? This book is your roadmap-whether you're a complete beginner, a student, or a developer eager to build real AI solutions with confidence. Practical Deep Learning in Python gently guides you from your very first neural network to advanced projects, all with hands-on, step-by-step instructions. There's no need for a PhD or prior experience-just curiosity and the desire to learn. Every concept is broken down with plain language, practical tips, and complete code examples you can run, modify, and make your own. What Makes This Book Different? Four Frameworks, One Journey: Master PyTorch, TensorFlow, Keras, and JAX-discover each tool's strengths, see how they compare, and develop the flexibility to tackle any project. Project-Based Learning: Build image classifiers, sentiment analysis models, time series predictors, and more-across real-world datasets and domains. Step-by-Step Guidance: Each chapter builds on the last, ensuring you gain both a solid foundation and advanced techniques, including transfer learning, model optimization, and deployment. Beginner Friendly, Expert-Ready: Start from scratch and grow at your own pace. All essential Python tools and setup steps are covered, with troubleshooting tips to keep you moving forward. Encouraging and Supportive: Mistakes are normal-progress is celebrated at every stage. You'll learn how to experiment, debug, and grow, turning setbacks into breakthroughs. You'll Gain: The confidence to build, train, and evaluate deep learning models from the ground up Practical skills with today's most important Python AI frameworks A clear understanding of core deep learning concepts, from neural networks to deployment A flexible mindset for adapting to new tools and challenges as the AI field evolves Key Takeaways: Hands-on code in every chapter-experiment, modify, and make it your own Real-world projects: image classification, NLP, time series, and more Side-by-side framework comparisons for deep learning mastery Guidance on environment setup, hardware acceleration, and troubleshooting Insider tips for best practices, reproducibility, and staying up-to-date in AI Ready to Build Something Amazing? Start your practical journey into deep learning today-turn your curiosity into real skills, and your skills into intelligent solutions that make a difference. With this book as your mentor, you'll discover that anyone can master deep learning-one step at a time.
Practical Deep Learning 2nd Edition
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Author : Ronald T. Kneusel
language : en
Publisher: NO STARCH PRESS, INC
Release Date : 2025-07-08
Practical Deep Learning 2nd Edition written by Ronald T. Kneusel and has been published by NO STARCH PRESS, INC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-08 with Computers categories.
Deep learning made simple. Dip into deep learning without drowning in theory with this fully updated edition of Practical Deep Learning from experienced author and AI expert Ronald T. Kneusel. After a brief review of basic math and coding principles, you’ll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a thorough understanding of how each technique works under the hood. Whether you’re a developer looking to add AI to your toolkit or a student seeking practical machine learning skills, this book will teach you: How neural networks work and how they’re trained How to use classical machine learning models How to develop a deep learning model from scratch How to evaluate models with industry-standard metrics How to create your own generative AI models Each chapter emphasizes practical skill development and experimentation, building to a case study that incorporates everything you’ve learned to classify audio recordings. Examples of working code you can easily run and modify are provided, and all code is freely available on GitHub. With Practical Deep Learning, second edition, you’ll gain the skills and confidence you need to build real AI systems that solve real problems. New to this edition: Material on computer vision, fine-tuning and transfer learning, localization, self-supervised learning, generative AI for novel image creation, and large language models for in-context learning, semantic search, and retrieval-augmented generation (RAG).
Practical Deep Learning
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Author : Ronald T. Kneusel
language : en
Publisher: No Starch Press
Release Date : 2021-03-16
Practical Deep Learning written by Ronald T. Kneusel and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-16 with Computers categories.
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about artificial intelligence and machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
Practical Machine Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Apress
Release Date : 2017-12-20
Practical Machine Learning With Python written by Dipanjan Sarkar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-20 with Computers categories.
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries andframeworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
Practical Deep Learning
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Author : Ron Kneusel
language : en
Publisher:
Release Date : 2021
Practical Deep Learning written by Ron Kneusel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
If you�¢??ve been curious about machine learning but didn�¢??t know where to start, this is the book you�¢??ve been waiting for. Focusing on the subfield of machine learning known as deep learning , it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math�¢??the book will cover the rest. After an introduction to Python, you�¢??ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models�¢?? performance. You�¢??ll also learn: �¢?�¢How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines �¢?�¢How neural networks work and how they�¢??re trained �¢?�¢How to use convolutional neural networks �¢?�¢How to develop a successful deep learning model from scratch You�¢??ll conduct experiments along the way, building to a final case study that incorporates everything you�¢??ve learned. All of the code you�¢??ll use is available at the linked examples repo. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
Practical Machine Learning
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Author : Ally S. Nyamawe
language : en
Publisher: CRC Press
Release Date : 2025-02-07
Practical Machine Learning written by Ally S. Nyamawe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-07 with Computers categories.
The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct practical, real-world solutions powered by machine learning across diverse application domains. It demonstrates the fundamental techniques involved in data collection, integration, cleansing, transformation, development, and deployment of machine learning models. This book emphasizes the importance of integrating responsible and explainable AI into machine learning models, ensuring these principles are prioritized rather than treated as an afterthought. To support learning, this book also offers information on accessing additional machine learning resources such as datasets, libraries, pre-trained models, and tools for tracking machine learning models. This is a core resource for students and instructors of machine learning and data science looking for a beginner-friendly material which offers real-world applications and takes ethical discussions into account. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
Python Machine Learning
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Author : Railey Brandon
language : en
Publisher: Roland Bind
Release Date : 2019-04-25
Python Machine Learning written by Railey Brandon and has been published by Roland Bind this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-25 with Computers categories.
★☆Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes?☆★ If you responded yes to any of the above questions, you have come to the right place. Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it? ★★Apart from this, you will also learn more about★★ ♦ The Different Types Of Learning Algorithm That You Can Expect To Encounter ♦ The Numerous Applications Of Machine Learning And Deep Learning ♦ The Best Practices For Picking Up Neural Networks ♦ What Are The Best Languages And Libraries To Work With ♦ The Various Problems That You Can Solve With Machine Learning Algorithms ♦ And much more... Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network? So, what are you waiting for? Grab a copy of this book now!
Python Ai Programming
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Author : Patrick J
language : en
Publisher: GitforGits
Release Date : 2024-01-03
Python Ai Programming written by Patrick J and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-03 with Computers categories.
This book aspires young graduates and programmers to become AI engineers and enter the world of artificial intelligence by combining powerful Python programming with artificial intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to machine learning, where readers learn to implement Python in developing predictive models. The book provides a clear and accessible explanation of machine learning, incorporating practical examples and exercises that strengthen understanding. We go deep into deep learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered. The book's focus on computer vision and reinforcement learning is distinctive, presenting these cutting-edge AI fields in an approachable manner. Readers will learn how to use Python's intuitive programming paradigm to create systems that interpret visual data and make intelligent decisions based on environmental interactions. The book focuses on ethical AI development and responsible programming, emphasizing the importance of developing AI that is fair, transparent, and accountable. Each chapter is designed to improve learning by including practical examples, case studies, and exercises that provide hands-on experience. This book is an excellent starting point for anyone interested in becoming an AI engineer, providing the necessary foundational knowledge and skills to delve into the fascinating world of artificial intelligence. Key Learnings Explore Python basics and AI integration for real-world application and career advancement. Experience the power of Python in AI with practical machine learning techniques. Practice Python's deep learning tools for innovative AI solution development. Dive into NLP with Python to revolutionize data interpretation and communication strategies. Simple yet practical understanding of reinforcement learning for strategic AI decision making. Uncover ethical AI development and frameworks, and concepts of responsible and trustworthy AI. Harness Python's capabilities for creating AI applications with a focus on fairness and bias. Table of Content Introduction to Artificial Intelligence Python for AI Data as Fuel for AI Machine Learning Foundation Essentials of Deep Learning NLP and Computer Vision Hands-on Reinforcement Learning Ethics to AI