Java Deep Learning Projects - Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs.

Description

True PDF (not conversion). Build and deploy powerful neural network models using the latest Java deep learning libraries Key Features * Understand DL with Java by implementing real-world projects * Master implementations of various ANN models and build your own DL systems * Develop applications using NLP, image classification, RL, and GPU processing --- Book Description Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you'll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems. --- What you will learn - Master deep learning and neural network architectures - Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs - Train ML agents to learn from data using deep reinforcement learning - Use factorization machines for advanced movie recommendations - Train DL models on distributed GPUs for faster deep learning with Spark and DL4J - Ease your learning experience through 69 FAQs --- Who This Book Is For If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required. True PDF (not conversion). Title Page......Page 2 Copyright and Credits......Page 3 Packt Upsell......Page 4 Contributors......Page 5 Table of Contents......Page 7 Preface......Page 13 Chapter 1: Getting Started with Deep Learning......Page 20 Working principles of ML algorithms......Page 21 Supervised learning......Page 23 Unsupervised learning......Page 25 Reinforcement learning......Page 26 Putting ML tasks altogether......Page 27 How did DL take ML into next level?......Page 28 Biological neurons......Page 31 A brief history of ANNs......Page 32 How does an ANN learn?......Page 33 Forward and backward passes......Page 34 Weights and biases......Page 35 Weight optimization......Page 37 Activation functions......Page 38 Deep neural networks......Page 40 Deep belief networks......Page 41 Autoencoders......Page 43 Convolutional neural networks......Page 44 Recurrent neural networks ......Page 45 Residual neural networks......Page 46 Capsule networks......Page 47 Deep learning frameworks......Page 48 Cloud-based platforms for DL......Page 50 Problem description......Page 51 Configuring the programming environment......Page 53 Feature engineering and input dataset preparation......Page 54 Training MLP classifier ......Page 63 Evaluating the MLP classifier......Page 64 Summary......Page 68 Answers to FAQs......Page 69 Chapter 2: Cancer Types Prediction Using Recurrent Type Networks......Page 75 Deep learning in cancer genomics......Page 76 Cancer genomics dataset description......Page 78 Preparing programming environment......Page 84 Titanic survival revisited with DL4J......Page 87 Multilayer perceptron network construction......Page 90 Output layer......Page 92 Network training......Page 93 Evaluating the model......Page 95 Dataset preparation for training......Page 98 Recurrent and LSTM networks......Page 102 LSTM network construction......Page 107 Network training......Page 110 Evaluating the model......Page 111 Summary......Page 112 Answers to questions......Page 113 Chapter 3: Multi-Label Image Classification Using Convolutional Neural Networks......Page 121 Image classification and drawbacks of DNNs......Page 122 CNN architecture......Page 123 Convolutional operations......Page 125 Pooling and padding operations......Page 126 Problem description......Page 129 Description of the dataset......Page 130 Removing invalid images......Page 131 Workflow of the overall project......Page 132 Image preprocessing......Page 134 Extracting image metadata......Page 141 Image feature extraction......Page 142 Preparing the ND4J dataset......Page 149 Training, evaluating, and saving the trained CNN models......Page 150 Network construction......Page 151 Scoring the model......Page 156 Submission file generation......Page 157 Wrapping everything up by executing the main() method......Page 159 Summary......Page 161 Answers to questions......Page 162 Chapter 4: Sentiment Analysis Using Word2Vec and LSTM Network......Page 167 Sentiment analysis is a challenging task......Page 168 Using Word2Vec for neural word embeddings......Page 170 Folder structure of the dataset......Page 172 Word2Vec pre-trained model......Page 174 Sentiment analysis using Word2Vec and LSTM......Page 175 Preparing the train and test set using the Word2Vec model......Page 176 Network construction, training, and saving the model......Page 184 Restoring the trained model and evaluating it on the test set......Page 186 Making predictions on sample review texts......Page 188 Summary......Page 192 Answers to questions......Page 193 Image classification with pretrained VGG16......Page 200 DL4J and transfer learning......Page 202 Developing an image classifier using transfer learning......Page 203 Dataset collection and description......Page 204 Architecture choice and adoption......Page 205 Train and test set preparation......Page 211 Network training and evaluation......Page 212 Restoring the trained model and inferencing......Page 215 Making simple inferencing......Page 216 Summary......Page 219 Answers to questions......Page 220 Object detection from images and videos......Page 222 Object classification, localization, and detection......Page 223 Convolutional Sliding Window (CSW)......Page 226 Object detection from videos......Page 229 You Only Look Once (YOLO)......Page 231 Step 1 – Loading a pre-trained YOLO model......Page 236 Step 2 – Generating frames from video clips......Page 238 Step 3 – Feeding generated frames into Tiny YOLO model......Page 241 Step 4 – Object detection from image frames......Page 242 Step 5 – Non-max suppression in case of more than one bounding box......Page 243 Step 6 – wrapping up everything and running the application......Page 246 Frequently asked questions (FAQs)......Page 249 Answers to questions......Page 250 Chapter 7: Stock Price Prediction Using LSTM Network......Page 252 State-of-the-art automated stock trading......Page 253 Developing a stock price predictive model......Page 256 Data collection and exploratory analysis......Page 258 Preparing the training and test sets......Page 262 LSTM network construction......Page 270 Network training, and saving the trained model......Page 273 Restoring the saved model for inferencing......Page 275 Evaluating the model......Page 276 Summary......Page 285 Answers to questions......Page 286 Chapter 8: Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks......Page 288 Distributed deep learning across multiple GPUs......Page 289 Distributed training on GPUs with DL4J......Page 290 UCF101 – action recognition dataset......Page 293 Solving the encoding problem......Page 295 Data processing workflow......Page 297 Simple UI for checking video frames......Page 301 Preparing training and test sets......Page 303 Network creation and training......Page 305 Performance evaluation......Page 309 Distributed training on AWS deep learning AMI 9.0......Page 311 Summary......Page 322 Answers to questions......Page 323 Notation, policy, and utility for RL......Page 325 Notations in reinforcement learning......Page 326 Policy......Page 328 Introduction to QLearning......Page 329 Neural networks as a Q-function......Page 330 Developing a GridWorld game using a deep Q-network......Page 333 Generating the grid......Page 334 Calculating agent and goal positions......Page 336 Calculating the action mask......Page 337 Providing guidance action......Page 338 Flattening input for the input layer......Page 339 Network construction and training......Page 340 Playing the GridWorld game......Page 347 Summary......Page 350 Answers to questions......Page 351 Chapter 10: Developing Movie Recommendation Systems Using Factorization Machines......Page 354 Collaborative filtering approaches......Page 355 Model-based collaborative filtering......Page 357 The utility matrix......Page 358 The cold-start problem in collaborative-filtering approaches......Page 359 Factorization machines in recommender systems......Page 360 Developing a movie recommender system using FMs......Page 363 Dataset description and exploratory analysis......Page 364 Converting the dataset into LibFM format......Page 371 Training and test set preparation......Page 375 Movie rating prediction......Page 378 Which one makes more sense ;– ranking or rating?......Page 389 Summary......Page 400 Answers to questions......Page 401 Chapter 11: Discussion, Current Trends, and Outlook......Page 405 Titanic survival prediction using MLP and LSTM networks......Page 406 Image classification using convolutional neural networks......Page 407 Image classification using transfer learning......Page 408 Stock price prediction using LSTM network......Page 409 Using deep reinforcement learning for GridWorld......Page 410 Current trends......Page 411 Residual neural networks......Page 412 GANs......Page 413 Capsule networks (CapsNet)......Page 414 Deep learning for clustering analysis......Page 415 Frequently asked questions (FAQs)......Page 416 Answers to questions......Page 417 Other Books You May Enjoy......Page 419 Index......Page 422