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Bibliografische Details

Titel: Programming With Python: 4 Manuscripts - Deep Learning With Keras, Convolutional Neural Networks In Python, Python Machine Learning, Machine Learning With Tensorflow
Verlag: CreateSpace Independent Publishing Platform
Ver�ffentlichungsdatum: 2018
Sprache: Englisch
ISBN-10: 1719443718
ISBN-13: 9781719443715
Bindung: Softcover
Zustand: acceptable

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Inhaltsangabe

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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Rese�a del editor

!! Special 2-In-1 Deal - Buy The Paperback Version And Get The Ebook For FREE !!

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

Get this book bundle NOW and SAVE money!

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