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Python Deep Learning Cookbook: Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python
Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide
Key FeaturesPractical recipes on training different neural network models and tuning them for optimal performanceUse Python frameworks like TensorFlow, Caffe, Keras, Theano for Natural Language Processing, Computer Vision, and moreA hands-on guide covering the common as well as the not so common problems in deep learning using PythonBook DescriptionDeep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics.
The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
What you will learnImplement different neural network models in PythonSelect the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and KerasApply tips and tricks related to neural networks internals, to boost learning performancesConsolidate machine learning principles and apply them in the deep learning fieldReuse and adapt Python code snippets to everyday problemsEvaluate the cost/benefits and performance implication of each discussed solutionTable of ContentsProgramming Environment, GPU Computing, and Cloud SolutionsFeedforward NetworksConvolutional Neural Networks (CNN)Recurrent and Recursive Neural NetworksReinforcement LearningGenerative Adversarial NetworksComputer VisionNatural Language ProcessingSpeech Recognition and Video AnalysisTime Series and Structured DataGame Playing Agents and RoboticsHyperparameter Selection and TuningNetworks InternalsPretrained Models
Key FeaturesPractical recipes on training different neural network models and tuning them for optimal performanceUse Python frameworks like TensorFlow, Caffe, Keras, Theano for Natural Language Processing, Computer Vision, and moreA hands-on guide covering the common as well as the not so common problems in deep learning using PythonBook DescriptionDeep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics.
The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
What you will learnImplement different neural network models in PythonSelect the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and KerasApply tips and tricks related to neural networks internals, to boost learning performancesConsolidate machine learning principles and apply them in the deep learning fieldReuse and adapt Python code snippets to everyday problemsEvaluate the cost/benefits and performance implication of each discussed solutionTable of ContentsProgramming Environment, GPU Computing, and Cloud SolutionsFeedforward NetworksConvolutional Neural Networks (CNN)Recurrent and Recursive Neural NetworksReinforcement LearningGenerative Adversarial NetworksComputer VisionNatural Language ProcessingSpeech Recognition and Video AnalysisTime Series and Structured DataGame Playing Agents and RoboticsHyperparameter Selection and TuningNetworks InternalsPretrained Models
332 pages, Kindle Edition
Published October 27, 2017
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May 1, 2018Location: GG5 IRC
Accession No: DL029806
Accession No: DL029806
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