An Introduction To Deep Learning
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Introduction To Deep Learning And Neural Networks With Pythontm
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Author : Ahmed Fawzy Gad
language : en
Publisher: Academic Press
Release Date : 2020-11-25
Introduction To Deep Learning And Neural Networks With Pythontm written by Ahmed Fawzy Gad and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Medical categories.
Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book's end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. - Examines the practical side of deep learning and neural networks - Provides a problem-based approach to building artificial neural networks using real data - Describes PythonTM functions and features for neuroscientists - Uses a careful tutorial approach to describe implementation of neural networks in PythonTM - Features math and code examples (via companion website) with helpful instructions for easy implementation
Introduction To Deep Learning
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Author : Eugene Charniak
language : en
Publisher: MIT Press
Release Date : 2019-02-19
Introduction To Deep Learning written by Eugene Charniak and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-19 with Computers categories.
A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.
Introduction To Deep Learning
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Author : Sandro Skansi
language : en
Publisher: Springer
Release Date : 2018-02-04
Introduction To Deep Learning written by Sandro Skansi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-04 with Computers categories.
This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.
Deep Learning Fundamentals
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Author : Chao Pan
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-06-15
Deep Learning Fundamentals written by Chao Pan and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-15 with categories.
This book is the first part of the book deep learning with Python write by the same author. If you already purchased deep learning with Python by Chao Pan no need for this book. Are you thinking of learning deep Learning fundamentals, concepts and algorithms? (For Beginners) If you are looking for a complete beginners guide to learn deep learning with examples, 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 hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples This book and the accompanying examples, you would be well suited to tackle problems, which pique your interests using machine learning and deep learning models. Instead of tough math formulas, this book contains several graphs and images. Book Objectives 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. Target Users The most suitable users would include: 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 machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Introduction Teaching Approach What is Artificial Intelligence, Machine Learning and Deep Learning? Mathematical Foundations of Deep Learning Machine Learning Fundamentals Fully Connected Neural Networks Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Deep Reinforcement Learning Introduction to Deep Neural Networks with Keras Sources & References 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. No programming experience is required. The present only the fundamentals concepts and algorithms of deep learning. It ll be a good introduction for beginners.Q: Can I loan this book to friends?A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days.Q: Does this book include everything I need to become a Machine Learning expert?A: Unfortunately, no. This book is designed for readers taking their first steps in Deep Learning and further learning will be required beyond this book to master all aspects.Q: Can I have a refund if this book is not fitted 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 at [email protected].
An Introduction To Deep Learning
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Author : Dr. Pinki Nayak
language : en
Publisher: Xoffencerpublication
Release Date : 2024-04-02
An Introduction To Deep Learning written by Dr. Pinki Nayak and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-02 with Computers categories.
Using model architectures, complex structures, or other techniques that are generated from a range of nonlinear transformations, such as neural networks, it is an area of machine learning that is based on algorithms that aim to express high-level abstractions in data. Neural networks are one example of such an approach. In the subject of machine learning, this particular topic is referred to as classification and regression. Deep learning is an area of machine learning that is the topic of the term "deep learning," which is one of the ways that it is defined. When it comes to machine learning, it is a member of a larger family of methodologies that serve as a basis for training models by using data representations as its base. few examples of the many different representations that may be used to characterize an observation (like an image) are a collection of edges, regions with certain shapes, and so on. These are only few of the many instances. For the purpose of representing the observation, you could even use a vector consisting of intensity values for each pixel. The creation of representations that help the learning of tasks, such as recognizing faces or facial expressions, may be accomplished via the use of particular examples. Accessibility is provided for these representations. There are claims that deep learning will ultimately replace manual features with effective techniques for learning features in an unsupervised or semi-supervised way, as well as hierarchical extraction of features. These claims are based on the assumption that deep learning would eventually replace manual features. Deep learning is one of the most typical ways to describe it, despite the fact that it has been discussed in a variety of other ways before. The term "deep learning" refers to a subset of machine learning techniques that comprises the following tools and techniques
An Introduction To Machine Learning
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Author : Gopinath Rebala
language : en
Publisher: Springer
Release Date : 2019-05-07
An Introduction To Machine Learning written by Gopinath Rebala and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-07 with Technology & Engineering categories.
Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any priorknowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.
An Introduction To Deep Learning
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Author : Vibhor Kumar Vishnoi
language : en
Publisher: Xoffencerpublication
Release Date : 2024-03-28
An Introduction To Deep Learning written by Vibhor Kumar Vishnoi and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-28 with Comics & Graphic Novels categories.
In deep learning, an artificial neural network (ANN) stores and processes large amounts of data. This is because artificial neural networks are used in deep learning. It is able to find both overt and covert connections across datasets. When working with deep learning, direct programming is not always necessary. Recent years have seen a meteoric rise in its popularity as a result of developments in processing power and the availability of massive datasets. This is one of the reasons why. For the reason that it was created using artificial designed to learn from large datasets. Deep Learning is a subfield of Machine Learning that use neural networks for modeling and problem solving; its development was spurred by the need to address complex problems. In order to train these networks to deal with challenging problems, the appropriate models must first be solved. Neural networks, which imitate the brain in structure and operation, process and transform data. These tasks are handled by multilayer neural networks consisting of numerous nodes communicating with one another. Fundamental to the idea which are defined by the existence of several layers of connected nodes. It is from this idea that the term "deep neural network" was coined. Because these networks can spot hierarchical patterns and features in the data, it's possible that they can develop elaborate representations of the data. If deep learning algorithms could independently learn and develop themselves depending on the data they were presented, then human engineers might not be needed to manually construct features. Deep learning has been very effective in several fields. These fields include picture identification, natural language processing, voice recognition, and recommendation systems. When training deep neural networks, it is generally necessary to have access to vast volumes of data and have a fast processing speed. Training deep neural networks, on the other hand, has become a great deal less complicated in recent years because to the proliferation of cloud computing and specialized equipment such as Graphics Processing Units (GPUs)
Deep Learning With Python
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Author : Chao Pan
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-06-14
Deep Learning With Python written by Chao Pan and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-14 with categories.
***** 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.
Introduction To Deep Learning And Neural Networks With Pythont
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Author : Ahmed Fawzy Gad
language : en
Publisher: Academic Press
Release Date : 2020-11-26
Introduction To Deep Learning And Neural Networks With Pythont written by Ahmed Fawzy Gad and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-26 with Medical categories.
Introduction to Deep Learning and Neural Networks with PythonT: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonT code examples to clarify neural network calculations, by book's end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonT examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network.
Deep Learning
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Author : Manel Martinez-Ramon
language : en
Publisher: John Wiley & Sons
Release Date : 2024-07-08
Deep Learning written by Manel Martinez-Ramon and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-08 with Computers categories.
An engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning tools Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures Practical discussions of recurrent neural networks and non-supervised approaches to deep learning Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.