Practical Deep Learning
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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 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 : 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 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 With Keras And Python
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Author : Mohammad Nauman
language : en
Publisher:
Release Date : 2018
Practical Deep Learning With Keras And Python written by Mohammad Nauman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
"This course is for you if you are new to Machine Learning but want to learn it without all the math. This course is also for you if you have tried to use a machine learning course but could never figure out how to use it to solve your own problems. In this course, we will start from scratch. So we will immediately start coding even before installation! You will see a brief bit of absolutely essential theory and then we will get into environment setup and explain almost all concepts through code. You will be using Keras, one of the easiest and most powerful machine learning tools out there. All this with only a few lines of code. All the examples used in the course come with starter code which will get you started and without the hard work."--Resource description page.
Practical Deep Learning With Tensorflow 2 Keras Tflite And Onnx
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Author : Dr Quinn Miles
language : en
Publisher: Independently Published
Release Date : 2025-08-24
Practical Deep Learning With Tensorflow 2 Keras Tflite And Onnx written by Dr Quinn Miles and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-24 with Computers categories.
Does the world of artificial intelligence feel out of reach? Worried deep learning is only for experts? You're not alone-and this book is your warm, patient guide into the future of AI, even if you've never written a line of code. Practical Deep Learning with TensorFlow 2, Keras, TFLite, and ONNX was created for absolute beginners who want real, hands-on skills-not theory overload. Whether you dream of building smart apps, exploring image recognition, or deploying neural networks to mobile devices, you'll find step-by-step support at every stage. What Makes This Book Different? No Experience Needed: Every chapter starts from scratch, explaining deep learning, neural networks, and TensorFlow 2 in clear, friendly language. You don't need a technical background-just curiosity and a willingness to try. Gently Builds Confidence: Complex topics like model training, data preparation, and deployment are broken down into bite-sized steps. Mistakes are welcomed as learning moments, not failures. Practical, Project-Driven Learning: Build real-world machine learning models with Keras, optimize them for accuracy, and see your work come alive on real devices using TensorFlow Lite and ONNX. End-to-End Guidance: Go from "What is deep learning?" all the way to deploying AI on edge devices-with troubleshooting tips, cheat sheets, and supportive encouragement at every turn. Celebrates Your Progress: Every small win is a big deal here. You'll find stories, "aha!" moments, and practical advice that keep you motivated-no matter where you start. Inside, You'll Learn How To: Understand the foundations of deep learning and neural networks Install and use TensorFlow 2, Keras, and related tools with ease Build and train models for real-world tasks-image, sequence, and beyond Prepare data, avoid common pitfalls, and optimize for best results Deploy trained models to mobile and edge devices with TensorFlow Lite Convert and run models in other frameworks using ONNX Troubleshoot errors, experiment safely, and grow your AI skills with confidence Who Is This Book For? Complete beginners in AI, coding, or data science Students, hobbyists, and career-changers looking for an accessible entry point Anyone who wants practical, modern skills in deep learning-without the overwhelm You'll discover that AI isn't reserved for experts-it's for anyone willing to learn, experiment, and celebrate small victories along the way. Ready to unlock the power of deep learning, step by step? Open this book and start your coding journey today-your future self will thank you.
Practical Machine Learning
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Author : Sunila Gollapudi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-01-30
Practical Machine Learning written by Sunila Gollapudi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-30 with Computers categories.
Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn Implement a wide range of algorithms and techniques for tackling complex data Get to grips with some of the most powerful languages in data science, including R, Python, and Julia Harness the capabilities of Spark and Hadoop to manage and process data successfully Apply the appropriate machine learning technique to address real-world problems Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data. This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naive Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies. Style and approach A practical data science tutorial designed to give you an insight into the practical application of machine learning, this book takes you through complex concepts and tasks in an accessible way. Featuring information on a wide range of data science techniques, Practical Machine Learning is a comprehensive data science resource.
Practical Deep Learning
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Author : Hao Dong
language : en
Publisher:
Release Date : 2019
Practical Deep Learning written by Hao Dong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Practical Deep Learning For Cloud Mobile And Edge
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Author : Anirudh Koul
language : en
Publisher: O'Reilly Media
Release Date : 2019-10-14
Practical Deep Learning For Cloud Mobile And Edge written by Anirudh Koul and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Computers categories.
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users