Deep learning pipeline : building a deep learning model with TensorFlow /: building a deep learning model with TensorFlow. (©2020)

• Record Type: Book
• Further Information: Note: Hisham El-Amir, Mahmoud Hamdy.
• Other Names: El-Amir, Hisham; Hamdy, Mahmoud
• Contents: Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Introduction -- Part I: Introduction -- Chapter 1: A Gentle Introduction -- Information Theory, Probability Theory, and Decision Theory -- Information Theory -- Probability Theory -- Decision Theory -- Introduction to Machine Learning -- Predictive Analytics and Its Connection with Machine learning -- Machine Learning Approaches -- Supervised Learning -- Unsupervised Learning -- Semisupervised Learning -- Checkpoint -- Reinforcement Learning -- From Machine Learning to Deep Learning Lets' See What Some Heroes of Machine Learning Say About the Field -- Connections Between Machine Learning and Deep Learning -- Difference Between ML and DL -- In Machine Learning -- In Deep Learning -- What Have We Learned Here? -- Why Should We Learn About Deep Learning (Advantages of Deep learning)? -- Disadvantages of Deep Learning (Cost of Greatness) -- Introduction to Deep Learning -- Machine Learning Mathematical Notations -- Summary -- Chapter 2: Setting Up Your Environment -- Background -- Python 2 vs. Python 3 -- Installing Python -- Python Packages -- IPython -- Installing IPython Jupyter -- Installing Jupyter -- What Is an ipynb File? -- Packages Used in the Book -- NumPy -- SciPy -- Pandas -- Matplotlib -- NLTK -- Scikit-learn -- Gensim -- TensorFlow -- Installing on Mac or Linux distributions -- Installing on Windows -- Keras -- Summary -- Chapter 3: A Tour Through the Deep Learning Pipeline -- Deep Learning Approaches -- What Is Deep Learning -- Biological Deep Learning -- What Are Neural Networks Architectures? -- Deep Learning Pipeline -- Define and Prepare Problem -- Summarize and Understand Data -- Process and Prepare Data -- Evaluate Algorithms -- Improve Results Fast Preview of the TensorFlow Pipeline -- Tensors-the Main Data Structure -- First Session -- Data Flow Graphs -- Tensor Properties -- Tensor Rank -- Tensor Shape -- Summary -- Chapter 4: Build Your First Toy TensorFlow app -- Basic Development of TensorFlow -- Hello World with TensorFlow -- Simple Iterations -- Prepare the Input Data -- Doing the Gradients -- Linear Regression -- Why Linear Regression? -- What Is Linear Regression? -- Dataset Description -- Full Source Code -- XOR Implementation Using TensorFlow -- Full Source Code -- Summary -- Part II: Data -- Chapter 5: Defining Data Defining Data -- Why Should You Read This Chapter? -- Structured, Semistructured, and Unstructured Data -- Tidy Data -- Divide and Conquer -- Tabular Data -- Quantitative​ vs. ​Qualitative​ Data -- Example-the Titanic -- Divide and Conquer -- Making a Checkpoint -- The Four Levels of Data -- Measure of Center -- The Nominal Level -- Mathematical Operations Allowed for Nominal -- Measures of Center for Nominal -- What Does It Mean to be a Nominal Level Type? -- The Ordinal Level -- Examples of Being Ordinal -- What Data Is Like at the Ordinal Level -- Mathematical Operations Allowed for Ordinal
• Publisher Details: Berkeley, CA : Apress LP
• Publication Date: 2020
• Copyright Date: 2020
• Extent: 1 online resource (563 pages)
• Subjects: 006.3/1; Machine learning; Machine learning; Electronic books
• Languages: English
• ISBNs: 9781484253496; 1484253493
• Related ISBNs: 9781484253489; 1484253485
• Notes: Note: Print version record.
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