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

Frank Millstein

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

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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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商品描述(中文翻譯)

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使用Python進行編程 - 4本書籍合集!!


使用Keras進行深度學習


以下是您將在此書中學到的預覽內容...



  • 深度學習和機器學習的區別

  • 深度神經網絡

  • 卷積神經網絡

  • 使用Keras構建深度學習模型

  • 多層感知器網絡模型

  • 激活函數

  • 使用MNIST進行手寫識別

  • 解決多類別分類問題

  • 循環神經網絡和序列分類

  • 以及更多...


使用Python進行卷積神經網絡


以下是您將在本書中學到的預覽內容...



  • 卷積神經網絡的結構

  • 卷積神經網絡的工作原理

  • 卷積神經網絡的應用

  • 卷積運算符的重要性

  • 不同的卷積神經網絡層及其重要性

  • 空間參數的排列

  • 何時以及如何使用步幅和零填充

  • 參數共享的方法

  • 矩陣乘法及其重要性

  • 池化和全連接層

  • 介紹非線性relu激活函數

  • 使用反向傳播訓練卷積神經網絡模型

  • 應用dropout的方法和原因

  • 卷積神經網絡模型的訓練過程

  • 如何構建卷積神經網絡

  • 生成預測和計算損失函數

  • 如何訓練和評估您的MNIST分類器

  • 如何構建一個簡單的圖像分類卷積神經網絡

  • 以及更多更多！


Python機器學習


以下是您將在本書中學到的預覽內容...



  • 機器學習技術的基礎知識

  • 不同的機器學習算法

  • 基本的機器學習應用及其重要性

  • 在Python中開始使用機器學習，安裝和啟動SciPy

  • 加載數據和導入不同的庫

  • 數據摘要和數據可視化

  • 評估機器學習模型並進行預測

  • 最常用的機器學習算法，線性和邏輯回歸，決策樹支持向量機，k最近鄰算法，隨機森林

  • 解決多分類問題

  • 使用Matplotlib進行數據可視化，使用Pandas和Scikit-learn進行數據轉換

  • 解決多標籤分類問題

  • 以及更多更多！


使用TensorFlow進行機器學習


以下是您將在本書中學到的預覽內容...



  • 什麼是機器學習

  • 機器學習的主要用途和好處

  • 如何開始使用TensorFlow，安裝和加載數據

  • 數據流圖和基本的TensorFlow表達式

  • 如何定義您的數據流圖以及如何使用TensorBoard進行數據可視化

  • 主要的TensorFlow操作和構建張量

  • 如何使用不同的技術進行數據轉換

  • 如何使用TensorFlow數據集框架構建高性能數據管道

  • 如何創建TensorFlow迭代器

  • 使用one-hot轉換創建MNIST分類器


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