Programming With Python: 4 Manuscripts - Deep Learning With Keras, Convolutional Neural Networks In Python, Python Machine Learning, Machine Learning With Tensorflow


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Programming With Python - 4 BOOK BUNDLE!!




Deep Learning with Keras



Here Is a Preview of What You’ll Learn Here…




  • The difference between deep learning and machine learning

  • Deep neural networks

  • Convolutional neural networks

  • Building deep learning models with Keras

  • Multi-layer perceptron network models

  • Activation functions

  • Handwritten recognition using MNIST

  • Solving multi-class classification problems

  • Recurrent neural networks and sequence classification

  • And much more...


Convolutional Neural Networks in Python



Here Is a Preview of What You’ll Learn In This Book…



  • Convolutional neural networks structure

  • How convolutional neural networks actually work

  • Convolutional neural networks applications

  • The importance of convolution operator

  • Different convolutional neural networks layers and their importance

  • Arrangement of spatial parameters

  • How and when to use stride and zero-padding

  • Method of parameter sharing

  • Matrix multiplication and its importance

  • Pooling and dense layers

  • Introducing non-linearity relu activation function

  • How to train your convolutional neural network models using backpropagation

  • How and why to apply dropout

  • CNN model training process

  • How to build a convolutional neural network

  • Generating predictions and calculating loss functions

  • How to train and evaluate your MNIST classifier

  • How to build a simple image classification CNN

  • And much, much more!



Python Machine Learning




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  • Basics behind machine learning techniques

  • Different machine learning algorithms

  • Fundamental machine learning applications and their importance

  • Getting started with machine learning in Python, installing and starting SciPy

  • Loading data and importing different libraries

  • Data summarization and data visualization

  • Evaluation of machine learning models and making predictions

  • Most commonly used machine learning algorithms, linear and logistic regression, decision trees support vector machines, k-nearest neighbors, random forests

  • Solving multi-clasisfication problems

  • Data visualization with Matplotlib and data transformation with Pandas and Scikit-learn

  • Solving multi-label classification problems

  • And much, much more...



Machine Learning With TensorFlow



Here Is a Preview of What You’ll Learn Here…



  • What is machine learning

  • Main uses and benefits of machine learning

  • How to get started with TensorFlow, installing and loading data

  • Data flow graphs and basic TensorFlow expressions

  • How to define your data flow graphs and how to use TensorBoard for data visualization

  • Main TensorFlow operations and building tensors

  • How to perform data transformation using different techniques

  • How to build high performance data pipelines using TensorFlow Dataset framework

  • How to create TensorFlow iterators

  • Creating MNIST classifiers with one-hot transformation



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    AuthorFrank Millstein
    BindingKindle Edition
    FormatKindle eBook
    LanguageEnglish
    Language TypePublished
    Product GroupeBooks
    Publication Date2018-05-19
    Release Date2018-05-19
    Sales Rank58152

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