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

by Frank Millstein

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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


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-clasisfica

Kindle Edition

Published May 19, 2018