Machine learning with TensorFlow

• 作者: Shukla, Nishant,
• 其他作者: Fricklas, Ken,
• 出版:
• 稽核項: xx, 251 pages :illustrations ;25 cm.
• 標題: TensorFlow (Electronic resource) , Machine learning. , Artificial intelligence.
• ISBN: 1617293873 , 9781617293870
• 附註: Includes index. Machine generated contents note: pt. 1 YOUR MACHINE-LEARNING RIG -- 1.A machine-learning odyssey -- 1.1.Machine-learning fundamentals -- Parameters -- Learning and inference -- 1.2.Data representation and features -- 1.3.Distance metrics -- 1.4.Types of learning -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- 1.5.TensorFlow -- 1.6.Overview of future chapters -- 1.7.Summary -- 2.TensorFlow essentials -- 2.1.Ensuring that TensorFlow works -- 2.2.Representing tensors -- 2.3.Creating operators -- 2.4.Executing operators with sessions -- Understanding code as a graph -- Setting session configurations -- 2.5.Writing code in Jupyter -- 2.6.Using variables -- 2.7.Saving and loading variables -- 2.8.Visualizing data using TensorBoard -- Implementing a moving average -- Visualizing the moving average -- 2.9.Summary -- pt. 2 CORE LEARNING ALGORITHMS -- 3.Linear regression and beyond -- 3.1.Formal notation Note continued: How do you know the regression algorithm is working'? -- 3.2.Linear regression -- 3.3.Polynomial model -- 3.4.Regularization -- 3.5.Application of linear regression -- 3.6.Summary -- 4.A gentle introduction to classification -- 4.1.Formal notation -- 4.2.Measuring performance -- Accuracy -- Precision and recall -- Receiver operating characteristic curve -- 4.3.Using linear regression for classification -- 4.4.Using logistic regression -- Solving one-dimensional logistic regression -- Solving two-dimensional logistic regression -- 4.5.Multiclass classifier -- One-versus-all -- One-versus-one -- Softmax regression -- 4.6.Application of classification -- 4.7.Summary -- 5.Automatically clustering data -- 5.1.Traversing files in TensorFlow -- 5.2.Extracting features from audio -- 5.3.K-means clustering -- 5.4.Audio segmentation -- 5.5.Clustering using a self-organizing map -- 5.6.Application of clustering -- 5.7.Summary -- 6.Hidden Markov models Note continued: 6.1.Example of a not-so-interpretable model -- 6.2.Markov model -- 6.3.Hidden Markov model -- 6.4.Forward algorithm -- 6.5.Viterbi decoding -- 6.6.Uses of hidden Markov models -- Modeling a video -- Modeling DNA -- Modeling an image -- 6.7.Application of hidden Markov models -- 6.8.Summary -- pt. 3 THE NEURAL NETWORK PARADIGM -- 7.A peek into autoencoders -- 7.1.Neural networks -- 7.2.Autoencoders -- 7.3.Batch training -- 7.4.Working with images -- 7.5.Application of autoencoders -- 7.6.Summary -- 8.Reinforcement learning -- 8.1.Formal notions -- Policy -- Utility -- 8.2.Applying reinforcement learning -- 8.3.Implementing reinforcement learning -- 8.4.Exploring other applications of reinforcement learning -- 8.5.Summary -- 9.Convolutional neural networks -- 9.1.Drawback of neural networks -- 9.2.Convolutional neural networks -- 9.3.Preparing the image -- Generating filters -- Convolving using filters -- Max pooling Note continued: 9.4.Implementing a convolutional neural network in TensorFlow -- Measuring performance -- Training the classifier -- 9.5.Tips and tricks to improve performance -- 9.6.Application of convolutional neural networks -- 9.7.Summary -- 10.Recurrent neural networks -- 10.1.Contextual information -- 10.2.Introduction to recurrent neural networks -- 10.3.Implementing a recurrent neural network -- 10.4.A predictive model for time-series data -- 10.5.Application of recurrent neural networks -- 10.6.Summary -- 11.Sequence-to-sequence models for chatbots -- 11.1.Building on classification and RNNs -- 11.2.Seq2seq architecture -- 11.3.Vector representation of symbols -- 11.4.Putting it all together -- 11.5.Gathering dialogue data -- 11.6.Summary -- 12.Utility landscape -- 12.1.Preference model -- 12.2.Image embedding -- 12.3.Ranking images -- 12.4.Summary -- 12.5.What's next?.
• 摘要: Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own.
• 系統號: 005217555
• 資料類型: 圖書
• 讀者標籤: 需登入
• 引用網址: 複製連結
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