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Explaining Neural Networks In Raw Python
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Book Synopsis Explaining neural networks in raw Python by : Wojciech Broniowski
Download or read book Explaining neural networks in raw Python written by Wojciech Broniowski and published by Wojciech Broniowski. This book was released on 2021-07-15 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: These lectures explain the very basic concepts of neural networks at a most elementary level, requiring only very rudimentary knowledge of Python, or actually any programming language. With simplicity in mind, the code for various algorithms of neural networks is written from absolute scratch, i.e. without any use of dedicated higher-level libraries. That way one can follow all the programming steps in an explicit manner. The book is intended for undergraduate students and for advanced high school pupils and their teachers.
Book Synopsis Artificial Intelligence with Python by : Prateek Joshi
Download or read book Artificial Intelligence with Python written by Prateek Joshi and published by Packt Publishing Ltd. This book was released on 2017-01-27 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
Book Synopsis Neural Network Projects with Python by : James Loy
Download or read book Neural Network Projects with Python written by James Loy and published by Packt Publishing Ltd. This book was released on 2019-02-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key FeaturesDiscover neural network architectures (like CNN and LSTM) that are driving recent advancements in AIBuild expert neural networks in Python using popular libraries such as KerasIncludes projects such as object detection, face identification, sentiment analysis, and moreBook Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio. What you will learnLearn various neural network architectures and its advancements in AIMaster deep learning in Python by building and training neural networkMaster neural networks for regression and classificationDiscover convolutional neural networks for image recognitionLearn sentiment analysis on textual data using Long Short-Term MemoryBuild and train a highly accurate facial recognition security systemWho this book is for This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.
Book Synopsis Deep Learning Step by Step with Python by : N. Lewis
Download or read book Deep Learning Step by Step with Python written by N. Lewis and published by . This book was released on 2016-07-26 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finally! Deep Neural Networks Simplified with Python Deep Learning Step by Step with Python takes you on a gentle, fun and unhurried journey to building your own deep neural network models in Python. Using plain English, it offers an intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available to the data scientist for deep neural networks using Python. NO EXPERIENCE REQUIRED This book is designed to be accessible - I'm assuming you never did like linear algebra, don't want to see things derived, dislike complicated computer code, and you're here because you want to see deep neural networks explained in plain English, and try them out for yourself. It is so straightforward and easy to follow even your ten year old nephew (who dislikes math) can understand it! THIS BOOK IS FOR YOU IF YOU WANT: Explanations rather than mathematical derivation Real world applications that make sense. Illustrations to deepen your understanding. Worked examples in Python you can easily follow and immediately implement. Ideas you can actually use and try on your own data. QUICK AND EASY: Bestselling Data Scientist Dr. N.D Lewis shows you the shortcut up the steep steps to the very top. It's easier than you think. Through a simple to follow process you will learn how to build deep neural network models with Python. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful data science applications. YOU'LL LEARN HOW TO: Unleash the power of Deep Neural Networks for effective forecasting. Develop hands on solutions for binary classification. Design successful applications for multi-class problems. Master techniques for efficient model construction. Fine tune deep networks to boost, accelerate, and transform predictive performance. Build Deep Learning Models Faster! Everything you need to get started is contained within this book. Deep Learning Step by Step with Python is your very own hands on practical, tactical, easy to follow guide to mastery Buy this book today your next big breakthrough using deep neural networks is only a page away!
Book Synopsis Deep Learning with Python by : Mark Graph
Download or read book Deep Learning with Python written by Mark Graph and published by . This book was released on 2019-10-15 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book doesn't have any superpowers or magic formula to help you master the art of neural networks and deep learning. We believe that such learning is all in your heart. You need to learn a concept by heart and then brainstorm its different possibilities. I don't claim that after reading this book you will become an expert in Python and Deep Learning Neural Networks. Instead, you will, for sure, have a basic understanding of deep learning and its implications and real-life applications. Most of the time, what confuses us is the application of a certain thing in our lives. Once we know that, we can relate the subject to that particular thing and learn. An interesting thing is that neural networks also learn the same way. This makes it easier to learn about them when we know the basics. Let's take a look at what this book has to offer: ● The basics of Python including data types, operators and numbers. ● Advanced programming in Python with Python expressions, types and much more. ● A comprehensive overview of deep learning and its link to the smart systems that we are now building. ● An overview of how artificial neural networks work in real life. ● An overview of PyTorch. ● An overview of TensorFlow. ● An overview of Keras. ● How to create a convolutional neural network. ● A comprehensive understanding of deep learning applications and its ethical implications, including in the present and future. This book offers you the basic knowledge about Python and Deep Learning Neural Networks that you will need to lay the foundation for future studies. This book will start you on the road to mastering the art of deep learning neural networks. When I say that I don't have the magic formula to make you learn, I mean it. My point is that you should learn Python coding and Python libraries to build neural networks by practicing hard. The more you practice, the better it is for your skills. It is only after thorough and in depth practice that you will be able to create your own programs. Unlike other books, I don't claim that this book will make you a master of deep learning after a single read. That's not realistic, in fact, it's even a bit absurd. What I claim is that you will definitely learn about the basics. The rest is practice. The more you practice the better you code.
Download or read book Neural Networks written by Steven Cooper and published by Roland Bind. This book was released on 2018-11-06 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: ☆★The Best Neural Networks Book for Beginners★☆ If you are looking for a complete beginners guide to learn neural networks with examples, in just a few hours, then you need to continue reading. Have you noticed the increasing prevalence of software that tries to learn from you? More and more, we are interacting with machines and platforms that try to predict what we are looking for. From movie and television show recommendations on Netflix based on your taste to the keyboard on your smartphone trying to predict and recommend the next word you may want to type, it's becoming obvious that machine learning will definitely be part of our future. If you are interested in learning more about the computer programs of tomorrow then, Understanding Neural Networks – A Practical Guide for Understanding and Programming Neural Networks and Useful Insights for Inspiring Reinvention is the book you have been waiting for. ★★ Grab your copy today and learn ★★ ♦ The history of neural networks and the way modern neural networks work ♦ How deep learning works ♦ The different types of neural networks ♦ The ability to explain a neural network to others, while simultaneously being able to build on this knowledge without being COMPLETELY LOST ♦ How to build your own neural network! ♦ An effective technique for hacking into a neural network ♦ Some introductory advice for modifying parameters in the code-based environment ♦ And much more... You'll be an Einstein in no time! And even if you are already up to speed on the topic, this book has the power to illustrate what a neural network is in a way that is capable of inspiring new approaches and technical improvements. The world can't wait to see what you can do! Most of all, this book will feed the abstract reasoning region of your mind so that you are able to theorize and invent new types and styles of machine learning. So, what are you waiting for? Scroll up and click the buy now button to learn everything you need to know in no time!
Book Synopsis Python Machine Learning by : Railey Brandon
Download or read book Python Machine Learning written by Railey Brandon and published by Roland Bind. This book was released on 2019-04-25 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: ★☆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!
Book Synopsis A Guide to Understanding and Building Artificial Neural Networks in Python by : Ahmed Fawzy Gad
Download or read book A Guide to Understanding and Building Artificial Neural Networks in Python written by Ahmed Fawzy Gad and published by Wiley-IEEE Press. This book was released on 2021-06-16 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers everything required to start in the field of deep learning by fully understanding, practicing, and building neural networks. Starting from the simplest model Y=X, the book gives intensive full step-by-step math and Python examples to clarify the neural network calculations. The gradient descent algorithm is discussed by examples for training a neural network with any architecture until building a generic Python implementation from scratch mainly using NumPy. So, rather than just using an implemented version of the neural network like that in Scikit-Learn, the reader will both understand and implement it themself. Because building such an implementation is not easy, the book plays a crucial role in simplifying it. This book ensures understanding even if the reader is familiar with neural networks.
Book Synopsis Neural Networks with Python by : Mei Wong
Download or read book Neural Networks with Python written by Mei Wong and published by GitforGits. This book was released on 2023-11-02 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Neural Networks with Python" serves as an introductory guide for those taking their first steps into neural network development with Python. It's tailored to assist beginners in understanding the foundational elements of neural networks and to provide them with the confidence to delve deeper into this intriguing area of machine learning. In this book, readers will embark on a learning journey, starting from the very basics of Python programming, progressing through essential concepts, and gradually building up to more complex neural network architectures. The book simplifies the learning process by using relatable examples and datasets, making the concepts accessible to everyone. You will be introduced to various neural network architectures such as Feedforward, Convolutional, and Recurrent Neural Networks, among others. Each type is explained in a clear and concise manner, with practical examples to illustrate their applications. The book emphasizes the real-world applications and practical aspects of neural network development, rather than just theoretical knowledge. Readers will also find guidance on how to troubleshoot and refine their neural network models. The goal is to equip you with a solid understanding of how to create efficient and effective neural networks, while also being mindful of the common challenges that may arise. By the end of your journey with this book, you will have a foundational understanding of neural networks within the Python ecosystem and be prepared to apply this knowledge to real-world scenarios. "Neural Networks with Python" aims to be your stepping stone into the vast world of machine learning, empowering you to build upon this knowledge and explore more advanced topics in the future. Key Learnings Master Python for machine learning, from setup to complex models. Gain flexibility with diverse neural network architectures for various problems. Hands-on experience in building, training, and fine-tuning neural networks. Learn strategic approaches for troubleshooting and optimizing neural models. Grasp advanced topics like autoencoders, capsule networks, and attention mechanisms. Acquire skills in crucial data preprocessing and augmentation techniques. Understand and apply optimization techniques and hyperparameter tuning. Implement an end-to-end machine learning project, from data to deployment. Table of Content Python, TensorFlow, and your First Neural Network Deep Dive into Feedforward Networks Convolutional Networks for Visual Tasks Recurrent Networks for Sequence Data Data Generation with GANs Transformers for Complex Tasks Autoencoders for Data Compression and Generation Capsule Networks
Book Synopsis Deep Learning with Python by : Chao Pan
Download or read book Deep Learning with Python written by Chao Pan and published by Createspace Independent Publishing Platform. This book was released on 2016-06-14 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: ***** BUY NOW (will soon return to 24.77 $) *****Are you thinking of learning deep Learning using Python? (For Beginners Only) If you are looking for a beginners guide to learn deep learning, in just a few hours, this book is for you. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses.To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach, which would lead to better mental representations.Step-by-Step Guide and Visual Illustrations and ExamplesThis book and the accompanying examples, you would be well suited to tackle problems, which pique your interests using machine learning and deep learning models. Book Objectives This book will help you: Have an appreciation for deep learning and an understanding of their fundamental principles. Have an elementary grasp of deep learning concepts and algorithms. Have achieved a technical background in deep learning and neural networks using Python. Target UsersThe book designed for a variety of target audiences. Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and deep learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Introduction What is Artificial Intelligence, Machine Learning and Deep Learning? Mathematical Foundations of Deep Learning Understanding Machine Learning Models Evaluation of Machine Learning Models: Overfitting, Underfitting, Bias Variance Tradeoff Fully Connected Neural Networks Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Deep Reinforcement Learning Introduction to Deep Neural Networks with Keras A First Look at Neural Networks in Keras Introduction to Pytorch The Pytorch Deep Learning Framework Your First Neural Network in Pytorch Deep Learning for Computer Vision Build a Convolutional Neural Network Deep Learning for Natural Language Processing Working with Sequential Data Build a Recurrent Neural Network Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: if you want to smash Deep Learning from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email.***** MONEY BACK GUARANTEE BY AMAZON ***** Editorial Reviews"This is an excellent book, it is a very good introduction to deep learning and neural networks. The concepts and terminology are clearly explained. The book also points out several good locations on the internet where users can obtain more information. I was extremely happy with this book and I recommend it for all beginners" - Prof. Alain Simon, EDHEC Business School. Statistician and DataScientist.
Book Synopsis Hands-on Machine Learning with Python by : Ashwin Pajankar
Download or read book Hands-on Machine Learning with Python written by Ashwin Pajankar and published by Apress. This book was released on 2022-03-20 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios. The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch. After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage. What You'll Learn Review data structures in NumPy and Pandas Demonstrate machine learning techniques and algorithm Understand supervised learning and unsupervised learning Examine convolutional neural networks and Recurrent neural networks Get acquainted with scikit-learn and PyTorch Predict sequences in recurrent neural networks and long short term memory Who This Book Is For Data scientists, machine learning engineers, and software professionals with basic skills in Python programming.
Book Synopsis Neural Networks with Python by : Mei Wong
Download or read book Neural Networks with Python written by Mei Wong and published by Gitforgits. This book was released on 2023-11-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Neural Networks with Python" serves as an introductory guide for those taking their first steps into neural network development with Python. It's tailored to assist beginners in understanding the foundational elements of neural networks and to provide them with the confidence to delve deeper into this intriguing area of machine learning. In this book, readers will embark on a learning journey, starting from the very basics of Python programming, progressing through essential concepts, and gradually building up to more complex neural network architectures. The book simplifies the learning process by using relatable examples and datasets, making the concepts accessible to everyone. You will be introduced to various neural network architectures such as Feedforward, Convolutional, and Recurrent Neural Networks, among others. Each type is explained in a clear and concise manner, with practical examples to illustrate their applications. The book emphasizes the real-world applications and practical aspects of neural network development, rather than just theoretical knowledge. Readers will also find guidance on how to troubleshoot and refine their neural network models. The goal is to equip you with a solid understanding of how to create efficient and effective neural networks, while also being mindful of the common challenges that may arise. By the end of your journey with this book, you will have a foundational understanding of neural networks within the Python ecosystem and be prepared to apply this knowledge to real-world scenarios. "Neural Networks with Python" aims to be your stepping stone into the vast world of machine learning, empowering you to build upon this knowledge and explore more advanced topics in the future. Key Learnings Master Python for machine learning, from setup to complex models. Gain flexibility with diverse neural network architectures for various problems. Hands-on experience in building, training, and fine-tuning neural networks. Learn strategic approaches for troubleshooting and optimizing neural models. Grasp advanced topics like autoencoders, capsule networks, and attention mechanisms. Acquire skills in crucial data preprocessing and augmentation techniques. Understand and apply optimization techniques and hyperparameter tuning. Implement an end-to-end machine learning project, from data to deployment. Table of Content Python, TensorFlow, and your First Neural Network Deep Dive into Feedforward Networks Convolutional Networks for Visual Tasks Recurrent Networks for Sequence Data Data Generation with GANs Transformers for Complex Tasks Autoencoders for Data Compression and Generation Capsule Networks
Book Synopsis Ultimate Step by Step Guide to Deep Learning Using Python by : Daneyal Anis
Download or read book Ultimate Step by Step Guide to Deep Learning Using Python written by Daneyal Anis and published by . This book was released on 2020-07-19 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: *Start your Data Science career using Python today!*Are you ready to start your new exciting career? Ready to master artificial intelligence and deep learning concepts?Are you overwhelmed with complexity of the books on this subject?Then let this breezy and fun little book on Python, Machine Learning and Deep Learning models make you a Data Scientist in 7 days!This book continues from where the first book in the series, Ultimate Step by Step Guide to Machine Learning Using Python, left of. In the first book you were introduced to Python concepts such as: -Data Structures like Pandas -Foundational libraries like Numpy, Seaborn and Scikit-Learn-Regression analysis-Classification-Clustering-Association Learning-Dimension ReductionThis book builds on those concepts to expand on Machine Learning algorithms like: -Linear and Logistical regression-Decision tree-Support vector machines (SVM)After that, this book takes you on a journey into Deep Learning and Neural Networks with important concepts and libraries like: -Convolutional and Recurrent Neural Networks-TensorFlow-Keras-PyTorch-Keras-Apache MXNet-Microsoft Cognitive Toolkit (CNTK)The final part of the book covers all foundational concepts that are required for Amazon Web Services (AWS) Certified Machine Learning Specialization by explaining how to deploy your models at scale on Cloud technologies. While AWS is used in the book for illustrative purposes, Microsoft Azure and Google Cloud are also introduced as alternative cloud technologies. After reading this book you will be able to: -Code in Python with confidence-Build new machine learning and deep learning models from scratch-Know how to clean and prepare your data for analytics-Speak confidently about statistical analysis techniquesData Science was ranked the fast-growing field by LinkedIn and Data Scientist is one of the most highly sought after and lucrative careers in the world!If you are on the fence about making the leap to a new and lucrative career, this is the book for you!What sets this book apart from other books on the topic of Python and Machine learning: -Step by step code examples and explanation-Complex concepts explained visually-Real world applicability of the machine learning and deep learning models introducedWhat do I need to get started?You will have a step by step action plan in place once you finish this book and finally feel that you, can master data science and artificial intelligence and start a lucrative and rewarding career! Ready to dive in to the exciting world of Python and Deep Learning?Then scroll up to the top and hit that BUY BUTTON!
Book Synopsis Deep Learning with Python by : Francois Chollet
Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Book Synopsis Python Deep Learning: Develop Your First Neural Network in Python Using Tensorflow, Keras, and Pytorch by : Samuel Burns
Download or read book Python Deep Learning: Develop Your First Neural Network in Python Using Tensorflow, Keras, and Pytorch written by Samuel Burns and published by Step-By-Step Tutorial for Begi. This book was released on 2019-04-03 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build your Own Neural Network today. Through easy-to-follow instruction and examples, you'll learn the fundamentals of Deep learning and build your very own Neural Network in Python using TensorFlow, Keras, PyTorch, and Theano. While you have the option of spending thousands of dollars on big and boring textbooks, we recommend getting the same pieces of information for a fraction of the cost. So Get Your Copy Now!! Why this book? Book ObjectivesThe following are the objectives of this book: To help you understand deep learning in detail To help you know how to get started with deep learning in Python by setting up the coding environment. To help you transition from a deep learning Beginner to a Professional. To help you learn how to develop a complete and functional artificial neural network model in Python on your own. Who this Book is for? The author targets the following groups of people: Anybody who is a complete beginner to deep learning with Python. Anybody in need of advancing their Python for deep learning skills. Professors, lecturers or tutors who are looking to find better ways to explain Deep Learning to their students in the simplest and easiest way. Students and academicians, especially those focusing on python programming, neural networks, machine learning, and deep learning. What do you need for this Book? You are required to have installed the following on your computer: Python 3.X. TensorFlow . Keras . PyTorch The Author guides you on how to install the rest of the Python libraries that are required for deep learning.The author will guide you on how to install and configure the rest. What is inside the book? What is Deep Learning? An Overview of Artificial Neural Networks. Exploring the Libraries. Installation and Setup. TensorFlow Basics. Deep Learning with TensorFlow. Keras Basics. PyTorch Basics. Creating Convolutional Neural Networks with PyTorch. Creating Recurrent Neural Networks with PyTorch. From the back cover. Deep learning is part of machine learning methods based on learning data representations. This book written by Samuel Burns provides an excellent introduction to deep learning methods for computer vision applications. The author does not focus on too much math since this guide is designed for developers who are beginners in the field of deep learning. The book has been grouped into chapters, with each chapter exploring a different feature of the deep learning libraries that can be used in Python programming language. Each chapter features a unique Neural Network architecture including Convolutional Neural Networks. After reading this book, you will be able to build your own Neural Networks using Tenserflow, Keras, and PyTorch. Moreover, the author has provided Python codes, each code performing a different task. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand.
Book Synopsis Deep Learning With Python by : Jason Brownlee
Download or read book Deep Learning With Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2016-05-13 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.
Book Synopsis Recurrent Neural Networks with Python Quick Start Guide by : Simeon Kostadinov
Download or read book Recurrent Neural Networks with Python Quick Start Guide written by Simeon Kostadinov and published by Packt Publishing Ltd. This book was released on 2018-11-30 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework. Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow libraryApply long short-term memory unitsExpand your skills in complex neural network and deep learning topicsBook Description Developers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling. Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field. What you will learnUse TensorFlow to build RNN modelsUse the correct RNN architecture for a particular machine learning taskCollect and clear the training data for your modelsUse the correct Python libraries for any task during the building phase of your modelOptimize your model for higher accuracyIdentify the differences between multiple models and how you can substitute themLearn the core deep learning fundamentals applicable to any machine learning modelWho this book is for This book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.
