Deep Learning

📒Deep Learning ✍ Ian Goodfellow

Deep Learning Book PDF
✏Book Title : Deep Learning
✏Author : Ian Goodfellow
✏Publisher : MIT Press
✏Release Date : 2016-11-18
✏Pages : 775
✏ISBN : 9780262035613
✏Available Language : English, Spanish, And French

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✏Deep Learning Book Summary : An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

📒Deep Learning ✍ Frank Millstein

Deep Learning Book PDF
✏Book Title : Deep Learning
✏Author : Frank Millstein
✏Publisher : Frank Millstein
✏Release Date : 2020-08-14
✏Pages : 266
✏ISBN :
✏Available Language : English, Spanish, And French

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✏Deep Learning Book Summary : Deep Learning - 2 BOOK BUNDLE!! Deep Learning with Keras This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more. Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio. The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks. Here Is a Preview of What You’ll Learn Here… The difference between deep learning and machine learning Deep neural networks Convolutional neural networks Building deep learning models with Keras Multi-layer perceptron network models Activation functions Handwritten recognition using MNIST Solving multi-class classification problems Recurrent neural networks and sequence classification And much more... Convolutional Neural Networks in Python This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field. This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems. Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own. Here Is a Preview of What You’ll Learn In This Book… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator Different convolutional neural networks layers and their importance Arrangement of spatial parameters How and when to use stride and zero-padding Method of parameter sharing Matrix multiplication and its importance Pooling and dense layers Introducing non-linearity relu activation function How to train your convolutional neural network models using backpropagation How and why to apply dropout CNN model training process How to build a convolutional neural network Generating predictions and calculating loss functions How to train and evaluate your MNIST classifier How to build a simple image classification CNN And much, much more! Get this book bundle NOW and SAVE money!

📒Deep Learning ✍ Siddhartha Bhattacharyya

Deep Learning Book PDF
✏Book Title : Deep Learning
✏Author : Siddhartha Bhattacharyya
✏Publisher : Walter de Gruyter GmbH & Co KG
✏Release Date : 2020-06-22
✏Pages : 161
✏ISBN : 9783110670929
✏Available Language : English, Spanish, And French

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✏Deep Learning Book Summary : This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.

Machine Learning And Deep Learning In Real Time Applications Book PDF
✏Book Title : Machine Learning and Deep Learning in Real Time Applications
✏Author : Mahrishi, Mehul
✏Publisher : IGI Global
✏Release Date : 2020-04-24
✏Pages : 344
✏ISBN : 9781799830979
✏Available Language : English, Spanish, And French

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✏Machine Learning and Deep Learning in Real Time Applications Book Summary : Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

Deep Learning With Pytorch Book PDF
✏Book Title : Deep Learning with PyTorch
✏Author : Eli Stevens
✏Publisher : Manning Publications
✏Release Date : 2020-08-04
✏Pages : 520
✏ISBN : 9781617295263
✏Available Language : English, Spanish, And French

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✏Deep Learning with PyTorch Book Summary : Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Summary Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Foreword by Soumith Chintala, Cocreator of PyTorch. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Although many deep learning tools use Python, the PyTorch library is truly Pythonic. Instantly familiar to anyone who knows PyData tools like NumPy and scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s excellent for building quick models, and it scales smoothly from laptop to enterprise. Because companies like Apple, Facebook, and JPMorgan Chase rely on PyTorch, it’s a great skill to have as you expand your career options. It’s easy to get started with PyTorch. It minimizes cognitive overhead without sacrificing the access to advanced features, meaning you can focus on what matters the most - building and training the latest and greatest deep learning models and contribute to making a dent in the world. PyTorch is also a snap to scale and extend, and it partners well with other Python tooling. PyTorch has been adopted by hundreds of deep learning practitioners and several first-class players like FAIR, OpenAI, FastAI and Purdue. About the book Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning. What's inside Training deep neural networks Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Exploring code samples in Jupyter Notebooks About the reader For Python programmers with an interest in machine learning. About the author Eli Stevens had roles from software engineer to CTO, and is currently working on machine learning in the self-driving-car industry. Luca Antiga is cofounder of an AI engineering company and an AI tech startup, as well as a former PyTorch contributor. Thomas Viehmann is a PyTorch core developer and machine learning trainer and consultant. consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

📒Deep Learning ✍ Josh Patterson

Deep Learning Book PDF
✏Book Title : Deep Learning
✏Author : Josh Patterson
✏Publisher : "O'Reilly Media, Inc."
✏Release Date : 2017-07-28
✏Pages : 532
✏ISBN : 9781491914236
✏Available Language : English, Spanish, And French

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✏Deep Learning Book Summary : How can machine learning--especially deep neural networks--make a real difference in your organization? This hands-on guide not only provides practical information, but helps you get started building efficient deep learning networks. The authors provide the fundamentals of deep learning--tuning, parallelization, vectorization, and building pipelines--that are valid for any library before introducing the open source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

Deep Learning For Image Processing Applications Book PDF
✏Book Title : Deep Learning for Image Processing Applications
✏Author : D.J. Hemanth
✏Publisher : IOS Press
✏Release Date : 2017-12
✏Pages : 284
✏ISBN : 9781614998228
✏Available Language : English, Spanish, And French

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✏Deep Learning for Image Processing Applications Book Summary : Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.

Hands On Mathematics For Deep Learning Book PDF
✏Book Title : Hands On Mathematics for Deep Learning
✏Author : Jay Dawani
✏Publisher :
✏Release Date : 2020-06-12
✏Pages : 364
✏ISBN : 1838647295
✏Available Language : English, Spanish, And French

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✏Hands On Mathematics for Deep Learning Book Summary : The main aim of this book is to make the advanced mathematical background accessible to someone with a programming background. This book will equip the readers with not only deep learning architectures but the mathematics behind them. With this book, you will understand the relevant mathematics that goes behind building deep learning models.

Practical Deep Learning For Cloud Mobile And Edge Book PDF
✏Book Title : Practical Deep Learning for Cloud Mobile and Edge
✏Author : Anirudh Koul
✏Publisher : O'Reilly Media
✏Release Date : 2019-10-14
✏Pages : 620
✏ISBN : 9781492034834
✏Available Language : English, Spanish, And French

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✏Practical Deep Learning for Cloud Mobile and Edge Book Summary : 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

📒Programming With Python ✍ Frank Millstein

Programming With Python Book PDF
✏Book Title : Programming With Python
✏Author : Frank Millstein
✏Publisher : Frank Millstein
✏Release Date : 2020-09-05
✏Pages : 517
✏ISBN :
✏Available Language : English, Spanish, And French

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✏Programming With Python Book Summary : Programming With Python - 4 BOOK BUNDLE!! Deep Learning with Keras Here Is a Preview of What You’ll Learn Here… The difference between deep learning and machine learning Deep neural networks Convolutional neural networks Building deep learning models with Keras Multi-layer perceptron network models Activation functions Handwritten recognition using MNIST Solving multi-class classification problems Recurrent neural networks and sequence classification And much more... Convolutional Neural Networks in Python Here Is a Preview of What You’ll Learn In This Book… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator Different convolutional neural networks layers and their importance Arrangement of spatial parameters How and when to use stride and zero-padding Method of parameter sharing Matrix multiplication and its importance Pooling and dense layers Introducing non-linearity relu activation function How to train your convolutional neural network models using backpropagation How and why to apply dropout CNN model training process How to build a convolutional neural network Generating predictions and calculating loss functions How to train and evaluate your MNIST classifier How to build a simple image classification CNN And much, much more! Python Machine Learning Here Is A Preview Of What You’ll Learn Here… Basics behind machine learning techniques Different machine learning algorithms Fundamental machine learning applications and their importance Getting started with machine learning in Python, installing and starting SciPy Loading data and importing different libraries Data summarization and data visualization Evaluation of machine learning models and making predictions Most commonly used machine learning algorithms, linear and logistic regression, decision trees support vector machines, k-nearest neighbors, random forests Solving multi-clasisfication problems Data visualization with Matplotlib and data transformation with Pandas and Scikit-learn Solving multi-label classification problems And much, much more... Machine Learning With TensorFlow Here Is a Preview of What You’ll Learn Here… What is machine learning Main uses and benefits of machine learning How to get started with TensorFlow, installing and loading data Data flow graphs and basic TensorFlow expressions How to define your data flow graphs and how to use TensorBoard for data visualization Main TensorFlow operations and building tensors How to perform data transformation using different techniques How to build high performance data pipelines using TensorFlow Dataset framework How to create TensorFlow iterators Creating MNIST classifiers with one-hot transformation Get this book bundle NOW and SAVE money!

The Deep Learning Ai Playbook Book PDF
✏Book Title : The Deep Learning AI Playbook
✏Author : Carlos Perez
✏Publisher : Lulu.com
✏Release Date :
✏Pages :
✏ISBN : 9781365879234
✏Available Language : English, Spanish, And French

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✏The Deep Learning AI Playbook Book Summary :

Deep Learning Models Explored With Help Of Python Programming Book PDF
✏Book Title : Deep Learning Models explored with help of Python Programming
✏Author : Editor IJSMI
✏Publisher : International Journal of Statistics and Medical Informatics
✏Release Date : 2020-11-04
✏Pages : 102
✏ISBN : 9798558877953
✏Available Language : English, Spanish, And French

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✏Deep Learning Models explored with help of Python Programming Book Summary : This is the second book in the Deep Learning models series by the author. Deep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep learning models, features are selected automatically through the iterative process wherein the model learns the features by going deep into the dataset and selects the features to be modeled. In the traditional models the features of the dataset needs to be specified in advance. The Deep Learning algorithms are derived from Artificial Neural Network concepts and it is a part of broader Machine Learning Models. The book starts with the Introduction part which is adopted from Author’s Deep Learning Models and its application: An overview with the help of R software book and move on to the Python’s important data processing packages such Numpy, and Pandas. Book then explores the Deep Learning models with the help of packages such as Pytorch, Tensor Flow and Keras and their applications in image processing, stock market prediction, recommender systems and natural language processing. Editor International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php ISBN: 9798558877953 E-Books: https://www.amazon.com/dp/B08MQTM1ZP Paperbacks: https://www.amazon.com/dp/B08MSQ3R8R

Deep Learning For Vision Systems Book PDF
✏Book Title : Deep Learning for Vision Systems
✏Author : Mohamed Elgendy
✏Publisher : Manning Publications
✏Release Date : 2020-11-10
✏Pages : 410
✏ISBN : 9781617296192
✏Available Language : English, Spanish, And French

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✏Deep Learning for Vision Systems Book Summary : Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you'll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

The Deep Learning With Pytorch Workshop Book PDF
✏Book Title : The Deep Learning with PyTorch Workshop
✏Author : Hyatt Saleh
✏Publisher : Packt Publishing Ltd
✏Release Date : 2020-07-22
✏Pages : 330
✏ISBN : 9781838981846
✏Available Language : English, Spanish, And French

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✏The Deep Learning with PyTorch Workshop Book Summary : Get a head start in the world of AI and deep learning by developing your skills with PyTorch Key Features Learn how to define your own network architecture in deep learning Implement helpful methods to create and train a model using PyTorch syntax Discover how intelligent applications using features like image recognition and speech recognition really process your data Book Description Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you’re starting from scratch. It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures. The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues. By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps. What you will learn Explore the different applications of deep learning Understand the PyTorch approach to building neural networks Create and train your very own perceptron using PyTorch Solve regression problems using artificial neural networks (ANNs) Handle computer vision problems with convolutional neural networks (CNNs) Perform language translation tasks using recurrent neural networks (RNNs) Who this book is for This deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly.

📒Deep Learning For Beginners ✍ Dr. Pablo Rivas

Deep Learning For Beginners Book PDF
✏Book Title : Deep Learning for Beginners
✏Author : Dr. Pablo Rivas
✏Publisher : Packt Publishing Ltd
✏Release Date : 2020-09-18
✏Pages : 432
✏ISBN : 9781838647582
✏Available Language : English, Spanish, And French

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✏Deep Learning for Beginners Book Summary : This book is for beginners who are looking for a strong foundation to build deep learning models from scratch. You will test your understanding of the concepts and measure your progress at the end of each chapter. You will have a firm understanding of deep learning and will be able to identify which algorithms are appropriate for different tasks.

Deep Learning Architectures Book PDF
✏Book Title : Deep Learning Architectures
✏Author : Ovidiu Calin
✏Publisher : Springer Nature
✏Release Date : 2020-02-13
✏Pages : 760
✏ISBN : 9783030367213
✏Available Language : English, Spanish, And French

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✏Deep Learning Architectures Book Summary : This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.

📒Deep Learning With R ✍ Francois Chollet

Deep Learning With R Book PDF
✏Book Title : Deep Learning with R
✏Author : Francois Chollet
✏Publisher : Pearson Professional
✏Release Date : 2018
✏Pages : 360
✏ISBN : 161729554X
✏Available Language : English, Spanish, And French

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✏Deep Learning with R Book Summary : Introduces deep learning systems using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.

Artificial Intelligence Machine Learning And Deep Learning Book PDF
✏Book Title : Artificial Intelligence Machine Learning and Deep Learning
✏Author : Oswald Campesato
✏Publisher : Stylus Publishing, LLC
✏Release Date : 2020-01-23
✏Pages : 300
✏ISBN : 9781683924661
✏Available Language : English, Spanish, And French

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✏Artificial Intelligence Machine Learning and Deep Learning Book Summary : This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning architectures such as CNNs, RNNs, LSTMs, and auto encoders. Keras-based code samples are included to supplement the theoretical discussion. In addition, this book contains appendices for Keras, TensorFlow 2, and Pandas. Features: Covers an introduction to programming concepts related to AI, machine learning, and deep learning Includes material on Keras, TensorFlow2 and Pandas

📒Deep Learning ✍ Julius Porter

Deep Learning Book PDF
✏Book Title : Deep Learning
✏Author : Julius Porter
✏Publisher :
✏Release Date : 2016-01-01
✏Pages : 76
✏ISBN : 1634842251
✏Available Language : English, Spanish, And French

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✏Deep Learning Book Summary : Deep Learning is gaining more and more popularity due to its success in various applications like Natural Language Processing (NLP), Image recognition and other Machine Learning (ML) paradigms. There are three conventional approaches that formed the basis for deep learning, Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs) and Stacked Auto-encoders. Following the tradition of other ML paradigms, deep learning also adopted non-conventional approaches for improving the performance. This book presents research on the fundamentals, methods and applications of deep learning. The first chapter provides a detailed description of the aforementioned non-conventional approaches and their applications. The second chapter presents research on deep learning in a digital learning environment and raises the question if digital instructional designs can catalyse deeper learning than traditional classroom teaching. The final chapter discusses in detail eight distinctive components of student well-being experiences that the authors believe could foster and optimise positive educational and non-educational outcomes.

Deep Learning And Neural Networks Concepts Methodologies Tools And Applications Book PDF
✏Book Title : Deep Learning and Neural Networks Concepts Methodologies Tools and Applications
✏Author : Management Association, Information Resources
✏Publisher : IGI Global
✏Release Date : 2019-10-11
✏Pages : 1671
✏ISBN : 9781799804154
✏Available Language : English, Spanish, And French

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✏Deep Learning and Neural Networks Concepts Methodologies Tools and Applications Book Summary : Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.

📒Machine Learning Paradigms ✍ George A. Tsihrintzis

Machine Learning Paradigms Book PDF
✏Book Title : Machine Learning Paradigms
✏Author : George A. Tsihrintzis
✏Publisher : Springer Nature
✏Release Date : 2020-08-24
✏Pages : 430
✏ISBN : 9783030497248
✏Available Language : English, Spanish, And French

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✏Machine Learning Paradigms Book Summary : At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

Neural Networks And Deep Learning Book PDF
✏Book Title : Neural Networks and Deep Learning
✏Author : Charu C. Aggarwal
✏Publisher : Springer
✏Release Date : 2018-08-25
✏Pages : 497
✏ISBN : 9783319944630
✏Available Language : English, Spanish, And French

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✏Neural Networks and Deep Learning Book Summary : This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Fundamentals Of Deep Learning Book PDF
✏Book Title : Fundamentals of Deep Learning
✏Author : Nikhil Buduma
✏Publisher : "O'Reilly Media, Inc."
✏Release Date : 2017-05-25
✏Pages : 298
✏ISBN : 9781491925584
✏Available Language : English, Spanish, And French

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✏Fundamentals of Deep Learning Book Summary : With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning

📒Deep Learning ✍ Stellan Ohlsson

Deep Learning Book PDF
✏Book Title : Deep Learning
✏Author : Stellan Ohlsson
✏Publisher : Cambridge University Press
✏Release Date : 2011-01-31
✏Pages :
✏ISBN : 9781139496759
✏Available Language : English, Spanish, And French

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✏Deep Learning Book Summary : Although the ability to retain, process, and project prior experience onto future situations is indispensable, the human mind also possesses the ability to override experience and adapt to changing circumstances. Cognitive scientist Stellan Ohlsson analyzes three types of deep, non-monotonic cognitive change: creative insight, adaptation of cognitive skills by learning from errors, and conversion from one belief to another, incompatible belief. For each topic, Ohlsson summarizes past research, re-formulates the relevant research questions, and proposes information-processing mechanisms that answer those questions. The three theories are based on the principles of redistribution of activation, specialization of practical knowledge, and re-subsumption of declarative information. Ohlsson develops the implications of those mechanisms by scaling their effects with respect to time, complexity, and social interaction. The book ends with a unified theory of non-monotonic cognitive change that captures the abstract properties that the three types of change share.

📒Deep Learning For Dummies ✍ John Paul Mueller

Deep Learning For Dummies Book PDF
✏Book Title : Deep Learning For Dummies
✏Author : John Paul Mueller
✏Publisher : John Wiley & Sons
✏Release Date : 2019-05-14
✏Pages : 368
✏ISBN : 9781119543046
✏Available Language : English, Spanish, And French

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✏Deep Learning For Dummies Book Summary : Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.

Training Evaluating And Tuning Deep Neural Network Models With Tensorflow Slim Book PDF
✏Book Title : Training Evaluating and Tuning Deep Neural Network Models with TensorFlow Slim
✏Author : Marvin Bertin
✏Publisher :
✏Release Date : 2017
✏Pages :
✏ISBN : OCLC:1137156013
✏Available Language : English, Spanish, And French

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✏Training Evaluating and Tuning Deep Neural Network Models with TensorFlow Slim Book Summary : "This course builds on the training in Marvin Bertin's 'Introduction to TensorFlow-Slim', which covered the basic concepts and uses of the TensorFlow-Slim (TF-Slim) API. In a series of lessons designed for learners with basic machine learning knowledge and some previous TensorFlow experience, you'll explore many of TF-Slim's most advanced features; using them to build and train sophisticated deep learning models. As you work through the examples, you'll come to appreciate TF-Slim's primary benefit: its ability to enable the work of machine learning while avoiding code complexity, a significant problem in the world of increasingly deep neural networks."--Resource description page.

Deep Learning With Matlab Deep Networks Book PDF
✏Book Title : Deep Learning with Matlab Deep Networks
✏Author : A. Vidales
✏Publisher : Independently Published
✏Release Date : 2018-12-27
✏Pages : 232
✏ISBN : 1792790481
✏Available Language : English, Spanish, And French

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✏Deep Learning with Matlab Deep Networks Book Summary : Deep learning is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. Deep learning is especially suited for image recognition, which is important for solving problems such as facial recognition, motion detection, and many advanced driver assistance technologies such as autonomous driving, lane detection, pedestrian detection, and autonomous parking.Deep Learning Toolbox provides simple MATLAB commands for creating and interconnecting the layers of a deep neural network. Examples and pretrained networks make it easy to use MATLAB for deep learning, even without knowledge of advanced computer vision algorithms or neural networks.Deep learning uses neural networks to learn useful representations of features directly from data. Neural networks combine multiple nonlinear processing layers, using simple elements operating in parallel and inspired by biological nervous systems. Deep learning models can achieve state-of-the-art accuracy in object classification, sometimes exceeding human-level performance.

📒Deep Learning ✍ Michael Fullan

Deep Learning Book PDF
✏Book Title : Deep Learning
✏Author : Michael Fullan
✏Publisher : Corwin Press
✏Release Date : 2017-11-06
✏Pages : 208
✏ISBN : 9781506368566
✏Available Language : English, Spanish, And French

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✏Deep Learning Book Summary : Engage the World Change the World Deep Learning has claimed the attention of educators and policymakers around the world. This book not only defines what deep learning is, but takes up the question of how to mobilize complex, whole-system change and transform learning for all students. Deep Learning is a global partnership that works to: transform the role of teachers to that of activators who design experiences that build global competencies using real-life problem solving; and supports schools, districts, and systems to shift practice and how to measure learning in authentic ways. This comprehensive strategy incorporates practical tools and processes to engage students, educators, and families in new partnerships and drive deep learning.

Deep Learning In Biometrics Book PDF
✏Book Title : Deep Learning in Biometrics
✏Author : Mayank Vatsa
✏Publisher : CRC Press is
✏Release Date : 2018
✏Pages : 316
✏ISBN : 1138578231
✏Available Language : English, Spanish, And French

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✏Deep Learning in Biometrics Book Summary : Deep Learning is now synonymous with applied machine learning. Many technology giants (e.g. Google, Microsoft, Apple, IBM) as well as start-ups are focusing on deep learning-based techniques for data analytics and artificial intelligence. This technology applies quite strongly to biometrics. This book covers topics in deep learning, namely convolutional neural networks, deep belief network and stacked autoencoders. The focus is also on the application of these techniques to various biometric modalities: face, iris, palmprint, and fingerprints, while examining the future trends in deep learning and biometric research.

Deep Learning With Python Book PDF
✏Book Title : Deep Learning with Python
✏Author : Chao Pan
✏Publisher : Createspace Independent Publishing Platform
✏Release Date : 2016-06-14
✏Pages : 124
✏ISBN : 1721250972
✏Available Language : English, Spanish, And French

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✏Deep Learning with Python Book Summary : ***** 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.