| Description |
1 online resource (300 pages) |
| Contents |
Cover ; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Theano Basics; The need for tensors; Installing and loading Theano; Conda package and environment manager; Installing and running Theano on CPU; GPU drivers and libraries; Installing and running Theano on GPU; Tensors; Graphs and symbolic computing; Operations on tensors; Dimension manipulation operators; Elementwise operators; Reduction operators; Linear algebra operators; Memory and variables; Functions and automatic differentiation |
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Loops in symbolic computingConfiguration, profiling and debugging; Summary; Chapter 2: Classifying Handwritten Digits with a Feedforward Network; The MNIST dataset; Structure of a training program; Classification loss function; Single-layer linear model; Cost function and errors; Backpropagation and stochastic gradient descent; Multiple layer model; Convolutions and max layers; Training; Dropout; Inference; Optimization and other update rules; Related articles; Summary; Chapter 3: Encoding Word into Vector; Encoding and embedding; Dataset; Continuous Bag of Words model; Training the model |
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Visualizing the learned embeddingsEvaluating embeddings -- analogical reasoning; Evaluating embeddings -- quantitative analysis; Application of word embeddings; Weight tying; Further reading; Summary; Chapter 4: Generating Text with a Recurrent Neural Net; Need for RNN; A dataset for natural language; Simple recurrent network; LSTM network; Gated recurrent network; Metrics for natural language performance; Training loss comparison; Example of predictions; Applications of RNN; Related articles; Summary; Chapter 5: Analyzing Sentiment with a Bidirectional LSTM; Installing and configuring Keras |
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Programming with KerasSemEval 2013 dataset; Preprocessing text data; Designing the architecture for the model; Vector representations of words; Sentence representation using bi-LSTM; Outputting probabilities with the softmax classifier; Compiling and training the model; Evaluating the model; Saving and loading the model; Running the example; Further reading; Summary; Chapter 6: Locating with Spatial Transformer Networks; MNIST CNN model with Lasagne; A localization network; Recurrent neural net applied to images; Unsupervised learning with co-localization; Region-based localization networks |
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Further readingSummary; Chapter 7: Classifying Images with Residual Networks; Natural image datasets; Batch normalization; Global average pooling; Residual connections; Stochastic depth; Dense connections; Multi-GPU; Data augmentation; Further reading; Summary; Chapter 8: Translating and Explaining with Encoding-decoding Networks; Sequence-to-sequence networks for natural language processing; Seq2seq for translation; Seq2seq for chatbots; Improving efficiency of sequence-to-sequence network; Deconvolutions for images; Multimodal deep learning; Further reading; Summary |
| Summary |
Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models. About This Book* Learn Theano basics and evaluate your mathematical expressions faster and in an efficient manner* Learn the design patterns of deep neural architectures to build efficient and powerful networks on your datasets* Apply your knowledge to concrete fields such as image classification, object detection, chatbots, machine translation, reinforcement agents, or generative models. Who This Book Is ForThis book is indented to provide a full overview of deep learning. From the beginner in deep learning and artificial intelligence, to the data scientist who wants to become familiar with Theano and its supporting libraries, or have an extended understanding of deep neural nets. Some basic skills in Python programming and computer science will help, as well as skills in elementary algebra and calculus. What You Will Learn* Get familiar with Theano and deep learning* Provide examples in supervised, unsupervised, generative, or reinforcement learning.* Discover the main principles for designing efficient deep learning nets: convolutions, residual connections, and recurrent connections.* Use Theano on real-world computer vision datasets, such as for digit classification and image classification.* Extend the use of Theano to natural language processing tasks, for chatbots or machine translation* Cover artificial intelligence-driven strategies to enable a robot to solve games or learn from an environment* Generate synthetic data that looks real with generative modeling* Become familiar with Lasagne and Keras, two frameworks built on top of TheanoIn DetailThis book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU. The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy. The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets. Style and approachIt is an easy-to-follow example book that teaches you how to perform fast, efficient computations in Python. Starting with the very basics-NumPy, installing Theano, this book will take you to the smooth journey of implementing Theano for advanced computations for machine learning and deep learning |
| Notes |
Chapter 9: Selecting Relevant Inputs or Memories with the Mechanism of Attention |
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Print version record |
| Subject |
Python
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Neural networks
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Machine learning.
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Machine learning
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| Form |
Electronic book
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| ISBN |
9781786463050 |
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1786463059 |
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