Programming With Python
Programming With Python: 4 Manuscripts - Deep Learning With Keras, Convolutional Neural Networks In Python, Python Machine Learning, Machine Learning With Tensorflow by Frank Millstein
English | May 19, 2018 | ASIN: B07D6FJYKY | 496 pages | AZW3 | 0.45 MB
English | May 19, 2018 | ASIN: B07D6FJYKY | 496 pages | AZW3 | 0.45 MB
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