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Free 500 page + book on Applications of Deep Neural Networks

Applications of Deep Neural Networks is a free 500 + page book by Jeff Heaton

The contents are as below

The download link is at the bottom of the page

Introduction                                               

 Python Preliminaries

  • Assignments                                         
  • Your Instructor: Jeff Heaton                                
  • Course Resources                                       
  • What is Deep Learning                                   
  • What is Machine Learning                                  
  • Regression                                           
  • Classification                                         
  • Beyond Classification and Regression                            
  • What are Neural Networks                                 
  • Why Deep Learning?                                     
  • Python for Deep Learning                                  
  • Software Installation                                     
  • Python Introduction                                     
  • Jupyter Notebooks                                      
  • Python Versions                                       
  • Module Assignment                                     
  • Introduction to Python                                  
  • Python Lists, Dictionaries, Sets and JSON                       
  • Lists and Tuples                                       
  • Sets                                              
  • Maps/Dictionaries/Hash Tables                               
  • More Advanced Lists                                     
  • An Introduction to JSON                                  
  • File Handling                                       
  • Read a CSV File                                       
  • Read (stream) a Large CSV File                              
  • Read a Text File                                       
  • Read an Image                                        
  • Functions, Lambdas, and Map/Reduce                         
  • Map                                              
  • Filter                                             
  • Lambda                                            
  • Reduce                                            

Python for Machine Learning

  • Introduction to Pandas                                  
  • Missing Values                                        
  • Dealing with Outliers                                    
  • Dropping Fields                                       
  • Concatenating Rows and Columns                             
  • Training and Validation                                   
  • Converting a Dataframe to a Matrix                            
  • Saving a Dataframe to CSV                                 
  • Saving a Dataframe to Pickle                                
  • Module Assignment                                     
  • Categorical and Continuous Values                           
  • Encoding Continuous Values                                
  • Encoding Categorical Values as Dummies                         
  • Target Encoding for Categoricals                              
  • Encoding Categorical Values as Ordinal                          
  • Grouping, Sorting, and Shuffling                             
  • Shuffling a Dataset                                      
  • Sorting a Data Set                                      
  • Grouping a Data Set                                     
  • Apply and Map                                      
  • Using Map with Dataframes                                 
  • Using Apply with Dataframes                                
  • Feature Engineering with Apply and Map                         
  • Feature Engineering                                    
  • Calculated Fields                                       
  • Google API Keys                                       
  • Other Examples: Dealing with Addresses                         

 Introduction to TensorFlow

  • Deep Learning and Neural Network Introduction                    
  • Classification or Regression                                 
  • Neurons and Layers                                     
  • Types of Neurons                                       
  • Input and Output Neurons                                 
  • Hidden Neurons                                       
  • Bias Neurons                                         
  • Context Neurons                                       
  • Other Neuron Types                                     
  • Why are Bias Neurons Needed?                               
  • Modern Activation Functions                                
  • Linear Activation Function                                 
  • Rectified Linear Units (ReLU)                               
  • Softmax Activation Function                                
  • Classic Activation Functions                                
  • Step Activation Function                                  
  • Sigmoid Activation Function                                
  • Hyperbolic Tangent Activation Function                          
  • Why ReLU?                                         
  • Module Assignment                                     
  • Introduction to Tensorflow and Keras                          
  • Why TensorFlow                                       
  • Deep Learning Tools                                     
  • Using TensorFlow Directly                                 
  • TensorFlow Linear Algebra Examples                           
  • TensorFlow Mandelbrot Set Example                           
  • Introduction to Keras                                    
  • Simple TensorFlow Regression: MPG                           
  • Introduction to Neural Network Hyperparameters                    
  • Controlling the Amount of Output                             
  • Regression Prediction                                    
  • Simple TensorFlow Classification: Iris                           
  • Saving and Loading a Keras Neural Network                      
  • Early Stopping in Keras to Prevent Overfitting                     
  • Early Stopping with Classification                             
  • Early Stopping with Regression                               
  • Extracting Weights and Manual Network Calculation                 
  • Weight Initialization                                     
  • Manual Neural Network Calculation                            

 Training for Tabular Data

  • Encoding a Feature Vector for Keras Deep Learning                  
  • Generate X and Y for a Classification Neural Network                  
  • Generate X and Y for a Regression Neural Network                   
  • Module Assignment                                     
  • Multiclass Classification with ROC and AUC                      
  • Binary Classification and ROC Charts                           
  • ROC Chart Example                                     
  • Multiclass Classification Error Metrics                           
  • Calculate Classification Accuracy                              
  • Calculate Classification Log Loss                              
  • Keras Regression for Deep Neural Networks with RMSE               
  • Mean Square Error                                      
  • Root Mean Square Error                                  
  • Lift Chart                                           
  • Training Neural Networks                                 
  • Classic Backpropagation                                   
  • Momentum Backpropagation                                
  • Batch and Online Backpropagation                             
  • Stochastic Gradient Descent                                 
  • Other Techniques                                       
  • ADAM Update                                        
  • Methods Compared                                     
  • Specifying the Update Rule in Tensorflow                         
  • Error Calculation from Scratch                              
  • Regression                                           
  • Classification                                         

 Regularization and Dropout

  • Introduction to Regularization: Ridge and Lasso                    
  • L and L Regularization                                  
  • Linear Regression                                       
  • L (Lasso) Regularization                                  
  • L (Ridge) Regularization                                  
  • ElasticNet Regularization                                  
  • Using K-Fold Cross-validation with Keras                        
  • Regression vs Classification K-Fold Cross-Validation                   
  • Out-of-Sample Regression Predictions with K-Fold Cross-Validation          
  • Classification with Stratified K-Fold Cross-Validation                  
  • Training with both a Cross-Validation and a Holdout Set                
  • L and L Regularization to Decrease Overfitting                   
  • Drop Out for Keras to Decrease Overfitting                       
  • Benchmarking Regularization Techniques                        
  • Additional Reading on Hyperparameter Tuning                      
  • Bootstrapping for Regression                                
  • Bootstrapping for Classification                               
  • Benchmarking                                        

 Convolutional Neural Networks (CNN) for Computer Vision

  • Image Processing in Python                               
  • Creating Images (from pixels) in Python                          
  • Transform Images in Python (at the pixel level)                     
  • Standardize Images                                      
  • Adding Noise to an Image                                  
  • Module Assignment                                     
  • Keras Neural Networks for Digits and Fashion MNIST                
  • Computer Vision                                       
  • Computer Vision Data Sets                                 
  • MNIST Digits Data Set                                   
  • MNIST Fashion Data Set                                  
  • CIFAR Data Set                                       
  • Other Resources                                       
  • Convolutional Neural Networks (CNNs)                          
  • Convolution Layers                                      
  • Max Pooling Layers                                     
  • TensorFlow with CNNs                                   
  • Access to Data Sets - DIGITS                                
  • Display the Digits                                      
  • Training/Fitting CNN - DIGITS                              
  • Evaluate Accuracy - DIGITS                                
  • MNIST Fashion                                        
  • Display the Apparel                                     
  • Training/Fitting CNN - Fashion                              
  • Implementing a ResNet in Keras                             
  • Keras Sequence vs Functional Model API                         
  • CIFAR Dataset                                        
  • ResNet V                                          
  • CONTENTS ix
  • ResNet V                                          
  • Using Your Own Images with Keras                           
  • Recognizing Multiple Images with Darknet                       
  • How Does DarkNet/YOLO Work?                             
  • Using YOLO in Python                                   
  • Installing YoloV-TF                                    
  • Transfering Weights                                     
  • Running DarkFlow (YOLO)                                 
  • Module Assignment                                     

 Generative Adversarial Networks

  • Introduction to GANS for Image and Data Generation                
  • Implementing DCGANs in Keras                            
  • Face Generation with StyleGAN and Python                      
  • Keras Sequence vs Functional Model API                         
  • Generating High Rez GAN Faces with Google CoLab                  
  • Run StyleGan From Command Line                           
  • Run StyleGAN From Python Code                            
  • Examining the Latent Vector                                
  • GANS for Semi-Supervised Training in Keras                      
  • Semi-Supervised Classification Training                          
  • Semi-Supervised Regression Training                            
  • Application of Semi-Supervised Regression                        
  • An Overview of GAN Research                              
  • Select Projects                                        

 Kaggle Data Sets

  • Introduction to Kaggle                                  
  • Kaggle Ranks                                         
  • Typical Kaggle Competition                                 
  • How Kaggle Competition Scoring                              
  • Preparing a Kaggle Submission                               
  • Select Kaggle Competitions                                 
  • Module Assignment                                     
  • Building Ensembles with Scikit-Learn and Keras                    
  • Evaluating Feature Importance                               
  • Classification and Input Perturbation Ranking                      
  • Regression and Input Perturbation Ranking                        
  • Biological Response with Neural Network                         
  • What Features/Columns are Important                          
  • Neural Network Ensemble                                  
  • Architecting Network: Hyperparameters                        
  • Number of Hidden Layers and Neuron Counts                       
  • Activation Functions                                     
  • Advanced Activation Functions                               
  • Regularization: L, L, Dropout                              
  • Batch Normalization                                     
  • Training Parameters                                     
  • Experimenting with Hyperparameters                           
  • x CONTENTS
  • Bayesian Hyperparameter Optimization for Keras                   
  • Current Semester’s Kaggle                                
  • Iris as a Kaggle Competition                                
  • MPG as a Kaggle Competition (Regression)                        
  • Module Assignment                                     

 Transfer Learning

  • Introduction to Keras Transfer Learning                        
  • Transfer Learning Example                                 
  • Module Assignment                                     
  • Popular Pretrained Neural Networks for Keras                     
  • DenseNet                                           
  • InceptionResNetV and InceptionV                            
  • MobileNet                                           
  • MobileNetV                                         
  • NASNet                                            
  • ResNet, ResNetV, ResNeXt                                
  • VGG and VGG                                     
  • Xception                                           
  • Transfer Learning for Computer Vision and Keras                   
  • Transfer                                            
  • Transfer Learning for Languages and Keras                       
  • Transfer Learning for Keras Feature Engineering                    

 Time Series in Keras

  • Time Series Data Encoding                               
  • Module Assignment                                     
  • Programming LSTM with Keras and TensorFlow                   
  • Understanding LSTM                                    
  • Simple TensorFlow LSTM Example                            
  • Sun Spots Example                                     
  • Further Reading for LSTM                                 
  • Text Generation with LSTM                              
  • Additional Information                                   
  • Character-Level Text Generation                              
  • Image Captioning with Keras and TensorFlow                    
  • Needed Data                                         
  • Running Locally                                       
  • Clean/Build Dataset From Flickrk                            
  • Choosing a Computer Vision Neural Network to Transfer                
  • Creating the Training Set                                  
  • Using a Data Generator                                   
  • Loading Glove Embeddings                                 
  • Building the Neural Network                                
  • Train the Neural Network                                  
  • Generating Captions                                     
  • Evaluate Performance on Test Data from Flickerk                    
  • Evaluate Performance on My Photos                            
  • Module Assignment                                     
  • CONTENTS xi
  • Temporal CNN in Keras and TensorFlow                       
  • Sun Spots Example - CNN                                 

 Natural Language Processing and Speech Recognition

  • Getting Started with Spacy in Python                         
  • Installing Spacy                                       
  • Tokenization                                         
  • Sentence Diagramming                                    
  • Stop Words                                          
  • WordVec and Text Classification                           
  • Suggested Software for WordVec                              
  • What are Embedding Layers in Keras                         
  • Simple Embedding Layer Example                             
  • Transferring An Embedding                                 
  • Training an Embedding                                   
  • Natural Language Processing with Spacy and Keras                 
  • Word-Level Text Generation                                
  • Learning English from Scratch with Keras and TensorFlow             
  • Imports and Utility Functions                                
  • Getting the Data                                       
  • Building the Vocabulary                                   
  • Building the Training and Test Data                            
  • Compile the Neural Network                                
  • Train the Neural Network                                  
  • Evaluate Accuracy                                      
  • Adhoc Query                                         

 Reinforcement Learning

  • Introduction to the OpenAI Gym                            
  • OpenAI Gym Leaderboard                                 
  • Looking at Gym Environments                               
  • Render OpenAI Gym Environments from CoLab                     
  • Introduction to Q-Learning                               
  • Introducing the Mountain Car                               
  • Programmed Car                                       
  • Reinforcement Learning                                   
  • Running and Observing the Agent                             
  • Inspecting the Q-Table                                    
  • Keras Q-Learning in the OpenAI Gym                         
  • DQN and the Cart-Pole Problem                              
  • Hyperparameters                                       
  • Environment                                         
  • Agent                                             
  • Policies                                            
  • Metrics and Evaluation                                   
  • Replay Buffer                                         
  • Data Collection                                        
  • Training the agent                                      
  • Visualization                                         
  • KS-Statistic                                          
  • Detecting Drift between Training and Testing Datasets by Training          
  • Using a Keras Deep Neural Network with a Web Application            
  • Converting Keras to CoreML                                
  • Creating an IOS CoreML Application                           
  • More Reading                                         
  • Other Neural Network Techniques
  • What is AutoML                                     
  • AutoML from your Local Computer                            
  • AutoML from Google Cloud                                 
  • A Simple AutoML System                                  
  • Running My Sample AutoML Program                          
  • Using Denoising AutoEncoders in Keras                        
  • Function Approximation                                   
  • Multi-Output Regression                                  
  • Simple Autoencoder                                     
  • Autoencode (single image)                                  
  • Standardize Images                                      
  • Image Autoencoder (multi-image)                             
  • Adding Noise to an Image                                  
  • Denoising Autoencoder                                   
  • Anomaly Detection in Keras                              
  • Read in KDD Data Set                                  
  • Preprocessing                                         
  • Training the Autoencoder                                  
  • Detecting an Anomaly                                    
  • Training an Intrusion Detection System with KDD                 
  • Read in Raw KDD- Dataset                               
  • Analyzing a Dataset                                     
  • Encode the feature vector                                  
  • Train the Neural Network                                  
  • New Technologies                                     
  • Neural Structured Learning (NSL)                             
  • Bert, AlBert, and Other NLP Technologies                        
  • Explainability Frameworks                               

 

Advanced/Other Topics

  • Flask and Deep Learning Web Services                        
  • Flask Hello World                                      
  • MPG Flask                                          
  • Flask MPG Client                                      
  • Images and Web Services                                  
  • Part : Deploying a Model to AWS                               
  • Train Model (optionally, outside of AWS)                         
  • Next Step: Convert Model (must use AWS SageMaker Notebook)           
  • Step Set up                                         
  • Step Load the Keras model using the JSON and weights file              
  • Step Export the Keras model to the TensorFlow ProtoBuf format (must use AWS
  • SageMaker Notebook)                                    
  • Step Convert TensorFlow model to a SageMaker readable format (must use AWS
  • SageMaker Notebook)                                    
  • Tar the entire directory and upload to S                         
  • Step Deploy the trained model (must use AWS SageMaker Notebook)         
  • Test Model Deployment (optionally, outside of AWS)              
  • Call the end point                                      
  • Additional Reading                                      
  • Using a Keras Deep Neural Network with a Web Application            
  • When to Retrain Your Neural Network                        
  • Preprocessing the Sberbank Russian Housing Market Data               
  • KS-Statistic                                          
  • Detecting Drift between Training and Testing Datasets by Training          
  • Using a Keras Deep Neural Network with a Web Application            
  • Converting Keras to CoreML                                
  • Creating an IOS CoreML Application                           
  • More Reading                                         
  • Other Neural Network Techniques
  • What is AutoML                                     
  • AutoML from your Local Computer                            
  • AutoML from Google Cloud                                 
  • A Simple AutoML System                                  
  • Running My Sample AutoML Program                          
  • Using Denoising AutoEncoders in Keras                        
  • Function Approximation                                   
  • Multi-Output Regression                                  
  • Simple Autoencoder                                     
  • Autoencode (single image)                                  
  • Standardize Images                                      
  • Image Autoencoder (multi-image)                             
  • Adding Noise to an Image                                  
  • Denoising Autoencoder                                   
  • Anomaly Detection in Keras                              
  • Read in KDD Data Set                                  
  • Preprocessing                                         
  • Training the Autoencoder                                  
  • Detecting an Anomaly                                    
  • Training an Intrusion Detection System with KDD                 
  • Read in Raw KDD- Dataset                               
  • Analyzing a Dataset                                     
  • Encode the feature vector                                  
  • Train the Neural Network                                  
  • New Technologies                                     
  • Neural Structured Learning (NSL)                             
  • Bert, AlBert, and Other NLP Technologies                        
  • Explainability Frameworks                                 

 

Book download link HERE

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