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

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-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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