Developers Guide For Building Own Neural Network Library
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Developers Guide For Building Own Neural Network Library
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Author : Venish Patidar
language : en
Publisher: Venish Patidar
Release Date : 2022-10-01
Developers Guide For Building Own Neural Network Library written by Venish Patidar and has been published by Venish Patidar this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-01 with Computers categories.
This book contains everything that a curious mind seeks more. The book sets the seeker on the mathematical journey, which starts with the biological neuron and a network and ends by creating your own. The book will gently introduce the concept of imitation and the roots of neural networks. Firstly, it will introduce the working of a biological neuron, followed by the analogies with the artificial neurons. After an overview of those, the book will shift the gears from biology to mathematics. We will find our way of creating the neural network library with the help of mathematics and developing the code alongside the section.
Python Deep Learning Develop Your First Neural Network In Python Using Tensorflow Keras And Pytorch
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Author : Samuel Burns
language : en
Publisher: Step-By-Step Tutorial for Begi
Release Date : 2019-04-03
Python Deep Learning Develop Your First Neural Network In Python Using Tensorflow Keras And Pytorch written by Samuel Burns and has been published by Step-By-Step Tutorial for Begi this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-03 with Computers categories.
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.
Neural Network Projects With Python
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Author : James Loy
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-02-28
Neural Network Projects With Python written by James Loy 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 2019-02-28 with Computers categories.
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.
Deep Learning With Microsoft Cognitive Toolkit Quick Start Guide
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Author : Willem Meints
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-28
Deep Learning With Microsoft Cognitive Toolkit Quick Start Guide written by Willem Meints 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 2019-03-28 with Computers categories.
Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive Toolkit Key FeaturesUnderstand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment Train different types of neural networks using Cognitive Toolkit and deploy it to productionEvaluate the performance of your models and improve your deep learning skillsBook Description Cognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks. This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environment What you will learnSet up your deep learning environment for the Cognitive Toolkit on Windows and LinuxPre-process and feed your data into neural networksUse neural networks to make effcient predictions and recommendationsTrain and deploy effcient neural networks such as CNN and RNNDetect problems in your neural network using TensorBoardIntegrate Cognitive Toolkit with Azure ML Services for effective deep learningWho this book is for Data Scientists, Machine learning developers, AI developers who wish to train and deploy effective deep learning models using Microsoft CNTK will find this book to be useful. Readers need to have experience in Python or similar object-oriented language like C# or Java.
Learning Tensorflow
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Author : Tom Hope
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-08-09
Learning Tensorflow written by Tom Hope and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-09 with Computers categories.
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
Recurrent Neural Networks With Python Quick Start Guide
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Author : Simeon Kostadinov
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-11-30
Recurrent Neural Networks With Python Quick Start Guide written by Simeon Kostadinov 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 2018-11-30 with Computers categories.
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.
Hands On Deep Learning With Go
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Author : Gareth Seneque
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-08-08
Hands On Deep Learning With Go written by Gareth Seneque 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 2019-08-08 with Computers categories.
Apply modern deep learning techniques to build and train deep neural networks using Gorgonia Key FeaturesGain a practical understanding of deep learning using GolangBuild complex neural network models using Go libraries and GorgoniaTake your deep learning model from design to deployment with this handy guideBook Description Go is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you'll be able to use these tools to train and deploy scalable deep learning models from scratch. This deep learning book begins by introducing you to a variety of tools and libraries available in Go. It then takes you through building neural networks, including activation functions and the learning algorithms that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted Boltzmann machines (RBMs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. You'll also understand how you can scale model deployments on the AWS cloud infrastructure for training and inference. By the end of this book, you'll have mastered the art of building, training, and deploying deep learning models in Go to solve real-world problems. What you will learnExplore the Go ecosystem of libraries and communities for deep learningGet to grips with Neural Networks, their history, and how they workDesign and implement Deep Neural Networks in GoGet a strong foundation of concepts such as Backpropagation and MomentumBuild Variational Autoencoders and Restricted Boltzmann Machines using GoBuild models with CUDA and benchmark CPU and GPU modelsWho this book is for This book is for data scientists, machine learning engineers, and AI developers who want to build state-of-the-art deep learning models using Go. Familiarity with basic machine learning concepts and Go programming is required to get the best out of this book.
Artificial Intelligence With Python
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Author : Prateek Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-01-27
Artificial Intelligence With Python written by Prateek Joshi 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 2017-01-27 with Computers categories.
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.
Neural Network Programming
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Author : Rob Botwright
language : en
Publisher: Rob Botwright
Release Date : 101-01-01
Neural Network Programming written by Rob Botwright and has been published by Rob Botwright this book supported file pdf, txt, epub, kindle and other format this book has been release on 101-01-01 with Computers categories.
Unlock the Power of AI with Our Neural Network Programming Book Bundle Are you ready to embark on a journey into the exciting world of artificial intelligence? Do you dream of mastering the skills needed to create cutting-edge AI systems that can revolutionize industries and change the future? Look no further than our comprehensive book bundle, "Neural Network Programming: How to Create Modern AI Systems with Python, TensorFlow, and Keras." Why Choose Our Book Bundle? In this era of technological advancement, artificial intelligence is at the forefront of innovation. Neural networks, a subset of AI, are driving breakthroughs in fields as diverse as healthcare, finance, and autonomous vehicles. To harness the full potential of AI, you need knowledge and expertise. That's where our book bundle comes in. What You'll Gain · Book 1 - Neural Network Programming for Beginners: If you're new to AI, this book is your perfect starting point. Learn Python, TensorFlow, and Keras from scratch and build your first AI systems. Lay the foundation for a rewarding journey into AI development. · Book 2 - Advanced Neural Network Programming: Ready to take your skills to the next level? Dive deep into advanced techniques, fine-tune models, and explore real-world applications. Master the intricacies of TensorFlow and Keras to tackle complex AI challenges. · Book 3 - Neural Network Programming: Beyond the Basics: Discover the world beyond fundamentals. Explore advanced concepts and cutting-edge architectures like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). Be prepared to innovate in AI research and development. · Book 4 - Expert Neural Network Programming: Elevate yourself to expert status. Dive into quantum neural networks, ethical AI, model deployment, and the future of AI research. Push the boundaries of AI development with advanced Python, TensorFlow, and Keras techniques. Who Is This Bundle For? · Aspiring AI Enthusiasts: If you're new to AI but eager to learn, our bundle offers a gentle and structured introduction. · Seasoned Developers: Professionals seeking to master AI development will find advanced techniques and real-world applications. · Researchers: Dive into cutting-edge AI research and contribute to the forefront of innovation. Why Us? Our book bundle is meticulously crafted by experts with a passion for AI. We offer a clear, step-by-step approach, ensuring that learners of all backgrounds can benefit. With hands-on projects, real-world applications, and a focus on both theory and practice, our bundle equips you with the skills and knowledge needed to succeed in the ever-evolving world of AI. Don't miss this opportunity to unlock the power of AI. Invest in your future today with "Neural Network Programming: How to Create Modern AI Systems with Python, TensorFlow, and Keras." Start your journey into the exciting world of artificial intelligence now!
Neural Network Programming With Tensorflow
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Author : Manpreet Singh Ghotra
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-11-10
Neural Network Programming With Tensorflow written by Manpreet Singh Ghotra 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 2017-11-10 with Computers categories.
Neural Networks and their implementation decoded with TensorFlow About This Book Develop a strong background in neural network programming from scratch, using the popular Tensorflow library. Use Tensorflow to implement different kinds of neural networks – from simple feedforward neural networks to multilayered perceptrons, CNNs, RNNs and more. A highly practical guide including real-world datasets and use-cases to simplify your understanding of neural networks and their implementation. Who This Book Is For This book is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, book can be used as a generic resource to bridge the gap between the math and the implementation of deep learning. If you have some understanding of Tensorflow and Python and want to learn what happens at a level lower than the plain API syntax, this book is for you. What You Will Learn Learn Linear Algebra and mathematics behind neural network. Dive deep into Neural networks from the basic to advanced concepts like CNN, RNN Deep Belief Networks, Deep Feedforward Networks. Explore Optimization techniques for solving problems like Local minima, Global minima, Saddle points Learn through real world examples like Sentiment Analysis. Train different types of generative models and explore autoencoders. Explore TensorFlow as an example of deep learning implementation. In Detail If you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that. You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders. By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs. Style and Approach This book is designed to give you just the right number of concepts to back up the examples. With real-world use cases and problems solved, this book is a handy guide for you. Each concept is backed by a generic and real-world problem, followed by a variation, making you independent and able to solve any problem with neural networks. All of the content is demystified by a simple and straightforward approach.