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

Millstein, Frank

ISBN 10: 1719443718 / ISBN 13: 9781719443715

Published by CreateSpace Independent Publishing Platform, 2018

About this Item

Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Acceptable condition! Any other included accessories are also in Acceptable condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear such as cover tears discoloration, staining, marks, scuffs, etc. All pages intact.

Seller Inventory # GWSVV.1719443718.A

Bibliographic Details

• Title: Programming With Python: 4 Manuscripts - Deep Learning With Keras, Convolutional Neural Networks In Python, Python Machine Learning, Machine Learning With Tensorflow
• Publisher: CreateSpace Independent Publishing Platform
• Publication Date: 2018
• Language: English
• ISBN 10: 1719443718
• ISBN 13: 9781719443715
• Binding: Soft cover
• Condition: acceptable

About this title

Synopsis

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

Here Is A Preview Of What You’ll Learn Here…

• 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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