English | May 19, 2018 | ASIN: B07D6FJYKY | 496 pages | AZW3 | 0.45 MB

Deep Learning with Keras

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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

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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

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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