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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-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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