Deep Learning. Das umfassende Handbuch

Grundlagen, aktuelle Verfahren und Algorithmen, neue Forschungsansätze

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Author: Ian Goodfellow,Yoshua Bengio,Aaron Courville

Publisher: MITP-Verlags GmbH & Co. KG

ISBN: 3958457010

Category: Computers

Page: 912

View: 3624

• Mathematische Grundlagen für Machine und Deep Learning • Umfassende Behandlung zeitgemäßer Verfahren: tiefe Feedforward-Netze, Regularisierung, Performance-Optimierung sowie CNNs, Rekurrente und Rekursive Neuronale Netze • Zukunftsweisende Deep-Learning-Ansätze sowie von Ian Goodfellow neu entwickelte Konzepte wie Generative Adversarial Networks Deep Learning ist ein Teilbereich des Machine Learnings und versetzt Computer in die Lage, aus Erfahrungen zu lernen. Dieses Buch behandelt umfassend alle Aspekte, die für den Einsatz und die Anwendung von Deep Learning eine Rolle spielen: In Teil I erläutern die Autoren die mathematischen Grundlagen für Künstliche Intelligenz, Neuronale Netze, Machine Learning und Deep Learning. In Teil II werden die aktuellen in der Praxis genutzten Verfahren und Algorithmen behandelt. In Teil III geben die Autoren Einblick in aktuelle Forschungsansätze und zeigen neue zukunftsweisende Verfahren auf. Dieses Buch richtet sich an Studenten und alle, die sich in der Forschung mit Deep Learning beschäftigen sowie an Softwareentwickler und Informatiker, die Deep Learning für eigene Produkte oder Plattformen einsetzen möchten. Dabei werden Grundkenntnisse in Mathematik, Informatik und Programmierung vorausgesetzt.

Deep Learning mit Python und Keras

Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek

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Author: Chollet, François

Publisher: MITP-Verlags GmbH & Co. KG

ISBN: 3958458408

Category: Computers

Page: 447

View: 3333

Deep Learning Kochbuch

Praxisrezepte für einen schnellen Einstieg

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Author: Douwe Osinga

Publisher: O'Reilly

ISBN: 3960102658

Category: Computers

Page: 262

View: 2643

Lassen Sie sich von Deep Learning nicht abschrecken! Dank Frameworks wie Keras und TensorFlow ist der schnelle Einstieg in die Entwicklung von Deep-Learning-Anwendungen nun auch für Softwareentwickler ohne umfassende Machine-Learning-Kenntnisse möglich. Mit den Rezepten aus diesem Buch lernen Sie, typische Aufgabenstellungen des Deep Learning zu lösen, wie etwa die Klassifizierung und Generierung von Texten, Bildern und Musik.Jedes Kapitel behandelt ein Projekt, wie z.B. das Trainieren eines Empfehlungssystems für Musik. Schritt für Schritt wird gezeigt, wie das jeweilige Projekt umgesetzt wird. Darüber hinaus beschreibt der Autor Douwe Osinga zahlreiche Techniken, die Ihnen helfen, wenn Sie einmal nicht mehr weiterwissen. Alle Codebeispiele sind in Python geschrieben und auf GitHub als Python-Notebooks frei verfügbar.Aus dem Inhalt:Entwickeln Sie Deep-Learning-Anwendungen, die Nutzern einen echten Mehrwert bietenBerechnen Sie Ähnlichkeiten von Texten mithilfe von Word-EmbeddingsErstellen Sie ein Empfehlungssystem für Filme basierend auf Wikipedia-LinksVisualisieren Sie die internen Vorgänge einer künstlichen Intelligenz, um nachvollziehen zu können, wie diese arbeitetEntwickeln Sie ein Modell, das passende Emojis für Textpassagen vorschlägtRealisieren Sie einen Reverse-Image-Search-Dienst mithilfe von vortrainierten NetzwerkenVergleichen Sie, wie Generative Adversarial Networks, Autoencoder und LSTM-Netzwerke Icons erzeugenTrainieren Sie ein Klassifikationsmodell für Musikstile und lassen Sie es Musikstücke dementsprechend zuordnen

Praxiseinstieg Deep Learning

Mit Python, Caffe, TensorFlow und Spark eigene Deep-Learning-Anwendungen erstellen

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Author: Ramon Wartala

Publisher: O'Reilly

ISBN: 3960101570

Category: Computers

Page: 226

View: 3052

Deep Learning ist ein Teilbereich des Machine Learning und basiert auf künstlichen neuronalen Netzen. Dieser praktische Leitfaden bietet einen schnellen Einstieg in die Schlüsseltechnologie und erschließt Grundlagen und Arbeitsweisen von Deep Learning. Anhand Python-basierter Beispielanwendungen wird der Umgang mit den Frameworks Caffe/Caffe2 und TensorFlow gezeigt. Einfache, alltagstaugliche Beispiele laden zum Nachprogrammieren ein. Darüber hinaus erfahren Sie, warum moderne Grafikkarten, Big Data und Cloud Computing beim Deep Learning so wichtig sind. Wenn Sie bereits mit Python, NumPy und matplotlib arbeiten, ermöglicht Ihnen dieses Buch, praktische Erfahrungen mit Deep-Learning-Anwendungen zu machen. Deep Learning – die Hintergründe - Lernmethoden, die Deep Learning zugrunde liegen - Aktuelle Anwendungsfelder wie maschinelle Übersetzungen, Sprach- und Bilderkennung bei Google, Facebook, IBM oder Amazon Der Werkzeugkasten mit Docker - Der Docker-Container zum Buch: Alle nötigen Tools und Programme sind bereits installiert, damit Sie die Beispiele des Buchs und eigene Deep-Learning-Anwendungen leicht ausführen können. - Die Arbeitsumgebung kennenlernen: Jupyter Notebook, Beispieldatensätze, Web Scraping Der Praxiseinstieg - Einführung in Caffe/Caffe2 und TensorFlow - Deep-Learning-Anwendungen nachprogrammieren: Handschrifterkennung, Bilderkennung und -klassifizierung, Deep Dreaming - Lösungen für Big-Data-Szenarien: verteilte Anwendungen, Spark, Cloud-Systeme - Modelle in produktive Systeme überführen

Neural Networks and Deep Learning

A Textbook

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Author: Charu C. Aggarwal

Publisher: Springer

ISBN: 3319944630

Category: Computers

Page: 497

View: 3751

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.

Deep Learning with Keras

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Author: Antonio Gulli,Sujit Pal

Publisher: Packt Publishing Ltd

ISBN: 1787129039

Category: Computers

Page: 318

View: 5433

Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Who This Book Is For If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book. What You Will Learn Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. Style and approach This book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras.

Hands-On Transfer Learning with Python

Implement advanced deep learning and neural network models using TensorFlow and Keras

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Author: Dipanjan Sarkar,Raghav Bali,Tamoghna Ghosh

Publisher: Packt Publishing Ltd

ISBN: 1788839056

Category: Computers

Page: 438

View: 2540

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.

MATLAB Deep Learning

With Machine Learning, Neural Networks and Artificial Intelligence

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Author: Phil Kim

Publisher: Apress

ISBN: 1484228456

Category: Computers

Page: 151

View: 5735

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

Applied Deep Learning

A Case-Based Approach to Understanding Deep Neural Networks

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Author: Umberto Michelucci

Publisher: Apress

ISBN: 1484237900

Category: Computers

Page: 410

View: 8973

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). What You Will Learn Implement advanced techniques in the right way in Python and TensorFlow Debug and optimize advanced methods (such as dropout and regularization) Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) Set up a machine learning project focused on deep learning on a complex dataset Who This Book Is For Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.

Java Deep Learning Projects

Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs

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Author: Md. Rezaul Karim

Publisher: Packt Publishing Ltd

ISBN: 1788996526

Category: Computers

Page: 436

View: 6638

Build and deploy powerful neural network models using the latest Java deep learning libraries Key Features Understand DL with Java by implementing real-world projects Master implementations of various ANN models and build your own DL systems Develop applications using NLP, image classification, RL, and GPU processing Book Description Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you’ll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems. What you will learn Master deep learning and neural network architectures Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs Train ML agents to learn from data using deep reinforcement learning Use factorization machines for advanced movie recommendations Train DL models on distributed GPUs for faster deep learning with Spark and DL4J Ease your learning experience through 69 FAQs Who this book is for If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required.

Machine Learning. Eine Analyse des State of the Art

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Author: Kevin Donath

Publisher: GRIN Verlag

ISBN: 3668614466

Category: Computers

Page: 51

View: 4559

Machine Learning ist eine mögliche Umsetzung von künstlicher Intelligenz (kurz KI), die in Software für Dinge wie Computer Vision, Spracherkennung, Sprachverarbeitung und Steuerung von Robotern eingesetzt wird. KI ist ein Zweig der Informatik, der sich damit beschäftigt intelligentes Verhalten in Computern zu simulieren. Dieses Konzept wird für Firmen aus allen Wirtschaftszweigen sowohl in internen Prozessen als auch in Produkten immer bedeutender. In dieser Publikation gibt der Autor einen Überblick über den aktuellen Stand des Machine Learning. Sein Fokus liegt dabei auf der Darstellung des aktuellen Standes der Technologien, den Aktivitäten der Key Player und den Anwendungsgebieten.

Analyse einer Deep Learning-Architektur mit neo4j anhand des Graphify-Plugins

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Author: Fabian Broszat

Publisher: GRIN Verlag

ISBN: 3668730423

Category: Computers

Page: 21

View: 887

Studienarbeit aus dem Jahr 2017 im Fachbereich Informatik - Wirtschaftsinformatik, Fachhochschule der Wirtschaft Bergisch Gladbach, Sprache: Deutsch, Abstract: Künstliche Intelligenz (KI) ist in der momentanen Welt einem großen Hype ausgesetzt und es wird stark in dieses Technologiegebiet investiert. Deep Learning als ein Untergebiet des Oberbegriffs „KI“ wird in dieser wissenschaftlichen Arbeit näher betrachtet. Es wird erläutert, wie Deep Learning im Allgemeinen funktioniert, Use Cases aufgezeigt und anhand eines Beispiels mit Reden amerikanischer Präsidenten plastisch dargestellt. Die vorliegende Arbeit setzt Vorkenntnisse in Graphentheorie, künstlichen neuronalen Netzen, Machine Learning, Representation Learning und Graphdatenbanken voraus.

Deep Learning

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Author: Ian Goodfellow,Yoshua Bengio,Aaron Courville

Publisher: MIT Press

ISBN: 0262035618

Category: Computers

Page: 800

View: 4851

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.

Deep Learning Essentials

Your hands-on guide to the fundamentals of deep learning and neural network modeling

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Author: Anurag Bhardwaj,Wei Di,Jianing Wei

Publisher: Packt Publishing Ltd

ISBN: 1785887777

Category: Computers

Page: 284

View: 6871

Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.

Python Deep Learning

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Author: Valentino Zocca,Gianmario Spacagna,Daniel Slater,Peter Roelants

Publisher: Packt Publishing Ltd

ISBN: 1786460661

Category: Computers

Page: 406

View: 8813

Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more Who This Book Is For This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired. What You Will Learn Get a practical deep dive into deep learning algorithms Explore deep learning further with Theano, Caffe, Keras, and TensorFlow Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines Dive into Deep Belief Nets and Deep Neural Networks Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Get to know device strategies so you can use deep learning algorithms and libraries in the real world In Detail With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside. Style and approach Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects.

Introduction to Deep Learning

From Logical Calculus to Artificial Intelligence

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Author: Sandro Skansi

Publisher: Springer

ISBN: 3319730045

Category: Computers

Page: 191

View: 9518

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.