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Beginning Application Development with TensorFlow and Keras : Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications
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| 자료유형 | 단행본 |
|---|---|
| 서명/저자사항 | Beginning Application Development with TensorFlow and Keras : Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications. |
| 개인저자 | Capelo, Luis. |
| 발행사항 | Birmingham: Packt Publishing, 2018. |
| 형태사항 | 1 online resource (148 pages). |
| 기타형태 저록 | Print version: Capelo, Luis. Beginning Application Development with TensorFlow and Keras : Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications. Birmingham : Packt Publishing, 짤2018 |
| ISBN | 9781789539226 1789539226 |
| 내용주기 | B11423_Cover_Hires; C09584_Book_Ebook; Preface; Introduction to Neural Networks and Deep Learning; What are Neural Networks?; Successful Applications; Why Do Neural Networks Work So Well?; Limitations of Deep Learning; Common Components and Operations of Neural Networks; Configuring a Deep Learning Environment; Software Components for Deep Learning; Python 3; TensorFlow; Keras; TensorBoard; Jupyter Notebooks, Pandas, and NumPy; Activity 1 -- Verifying Software Components; Exploring a Trained Neural Network; MNIST Dataset; Activity 2 -- Exploring a Trained Neural Network; Summary. Model ArchitectureChoosing the Right Model Architecture; Common Architectures; Convolutional Neural Networks; Recurrent Neural Networks; Generative Adversarial Networks; Deep Reinforcement Learning; Data Normalization; Z-score; Point-Relative Normalization; Maximum and Minimum Normalization; Structuring Your Problem; Activity 3 -- Exploring the Bitcoin Dataset and Preparing Data for Model; Using Keras as a TensorFlow Interface; Model Components; Activity 4 -- Creating a TensorFlow Model Using Keras; From Data Preparation to Modeling; Training a Neural Network; Reshaping Time-Series Data. Making PredictionsOverfitting; Activity 5 -- Assembling a Deep Learning System; Summary; Model Evaluation and Optimization; Model Evaluation; Problem Categories; Loss Functions, Accuracy, and Error Rates; Using TensorBoard; Implementing Model Evaluation Metrics; Evaluating the Bitcoin Model; Overfitting; Model Predictions; Interpreting Predictions; Activity 6 -- Creating an Active Training Environment; Hyperparameter Optimization; Layers and Nodes -- Adding More Layers; Adding More Nodes; Layers and Nodes -- Implementation; Epochs; Epochs -- Implementation; Activation Functions; Linear (Identity). Hyperbolic Tangent (Tanh)Rectified Linear Unit; Activation Functions -- Implementation; Regularization Strategies; L2 Regularization; Dropout; Regularization Strategies -- Implementation; Optimization Results; Activity 7 -- Optimizing a Deep Learning Model; Summary; Productization; Handling New Data; Separating Data and Model; Data Component; Model Component; Dealing with New Data; Re-Training an Old Model; Training a New Model; Activity 8 -- Dealing with New Data; Deploying a Model as a Web Application; Application Architecture and Technologies; Deploying and Using Cryptonic. Activity 9 -- Deploying a Deep Learning ApplicationSummary; Index. |
| 요약 | By the end of the course, you'll build a Bitcoin application that predicts the future price, based on historic, and freely available information. |
| 일반주제명 | Application software --Development. Computers --Data processing. COMPUTERS / Data Processing. COMPUTERS / Software Development & Engineering / General. Computers --Programming --General. Computers --Programming --Algorithms. Computer programming --software development. Algorithms & data structures. Computers --Programming Languages --Python. Programming & scripting languages: general. Application software --Development. Computers --Data processing. |
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