Deep learning pipeline : building a deep learning model with TensorFlow /

Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and r...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: El-Amir, Hisham
Otros Autores: Hamdy, Mahmoud
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berkeley, CA : Apress LP, ©2020.
Temas:
Machine learning.
Apprentissage automatique.
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 1 0 |a Deep learning pipeline :  |b building a deep learning model with TensorFlow /  |c Hisham El-Amir, Mahmoud Hamdy. 
260 |a Berkeley, CA :  |b Apress LP,  |c ©2020. 
300 |a 1 online resource (563 pages) 
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505 0 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
500 |a Measures of Center for Ordinal 
500 |a Includes index. 
520 |a Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets. You'll also develop a deep learning project by preparing data, choosing the model that fits that data, and debugging your model to get the best fit to data all using Tensorflow techniques. Enhance your skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution or entering a Kaggle contest, Deep Learning Pipeline is for you! 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Machine learning. 
650 6 |a Apprentissage automatique. 
650 7 |a Machine learning  |2 fast 
700 1 |a Hamdy, Mahmoud. 
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