Hands On Machine Learning with Scikit Learn and TensorFlow 1st Edition by Aurélien Géron ISBN 9781491962299

Hands On Machine Learning with Scikit Learn and TensorFlow 1st Edition by Aurélien Géron – Ebook PDF Instant Download/Delivery: 9781491962299
Full download Hands On Machine Learning with Scikit Learn and TensorFlow 1st Edition after payment

Product details:
ISBN 10: 
ISBN 13: 9781491962299
Author: Aurélien Géron

Hands On Machine Learning with Scikit Learn and TensorFlow 1st Edition Table of contents:

Part I: The Fundamentals of Machine Learning

1. The Machine Learning Landscape

   • What is Machine Learning?

   • Why Use Machine Learning?

   • Types of Machine Learning Systems

   • Main Challenges of Machine Learning

   • Testing and Validating

2. End-to-End Machine Learning Project

   • Working with Real Data

   • Look at the Big Picture

   • Get the Data

   • Discover and Visualize the Data

   • Prepare the Data for Machine Learning Algorithms

   • Select and Train a Model

   • Fine-Tune Your Model

   • Launch, Monitor, and Maintain Your System

3. Classification

   • Training a Binary Classifier

   • Performance Measures

   • Multiclass Classification

   • Multilabel Classification

4. Training Models

   • Linear Regression

   • Gradient Descent

   • Polynomial Regression

   • Regularized Linear Models

   • Logistic Regression

5. Support Vector Machines

   • Linear SVM Classification

   • Nonlinear SVM Classification

   • SVM Regression

   • Under the Hood

6. Decision Trees

   • Training and Visualizing a Decision Tree

   • Making Predictions

   • Estimating Class Probabilities

   • The CART Training Algorithm

   • Regularization Hyperparameters

   • Regression

   • Instability

7. Ensemble Learning and Random Forests

   • Voting Classifiers

   • Bagging and Pasting

   • Random Forests

   • Boosting

   • Stacking

8. Dimensionality Reduction

   • The Curse of Dimensionality

   • PCA (Principal Component Analysis)

   • Kernel PCA

   • Other Techniques

Part II: Neural Networks and Deep Learning

9. Up and Running with TensorFlow

   • Installing TensorFlow

   • Creating Your First Graph

   • Managing Graphs

   • Linear Regression with TensorFlow

   • Implementing Gradient Descent

   • Using TensorBoard for Visualization

10. Introduction to Artificial Neural Networks

    • Biological to Artificial Neurons

    • Training a Multi-Layer Perceptron (MLP) with TensorFlow

    • Fine-Tuning Neural Network Hyperparameters

11. Training Deep Neural Networks

    • Vanishing and Exploding Gradients

    • Reusing Pretrained Layers

    • Faster Optimizers

    • Regularization Techniques

12. Distributing TensorFlow

    • Multiple Devices on a Single Machine

    • Distributing Across Multiple Servers

    • Parallelizing Neural Networks

13. Convolutional Neural Networks (CNNs)

    • The Architecture of the Visual Cortex

    • Convolutional Layer

    • Pooling Layer

    • CNN Architectures

14. Recurrent Neural Networks (RNNs)

    • Recurrent Neurons

    • Training RNNs

    • LSTM Cells

    • GRU Cells

    • Natural Language Processing

15. Autoencoders

    • Encoder-Decoder Architecture

    • Denoising Autoencoders

    • Variational Autoencoders

People also search for Hands On Machine Learning with Scikit Learn and TensorFlow 1st Edition:

    
hands on machine learning with scikit learn and pytorch
    
hands on machine learning with scikit learn and tensorflow reddit
    
hands on machine learning with scikit learn and tensorflow notes
    
hands on network machine learning with scikit learn and graspologic
    
hands-on machine learning with scikit-learn reddit

Tags: Aurélien Géron, Hands On, Machine Learning, Scikit, TensorFlow