Java Deep Learning Projects Book pdf free download

Java Deep Learning Projects By Md. Rezaul Karim

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. Download Java Deep Learning Projects.

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 Deeplearning, Spark ML, and ranks and you’ll be able to use their features to build and deploy projects on distributed computing environments.

(26.6 MB)

Table of contents :
Title Page……Page 2
Copyright and Credits……Page 3
Java Deep Learning Projects……Page 4
Packt Upsell……Page 5
Why subscribe?……Page 6
PacktPub.com……Page 7
Contributors……Page 8
About the author……Page 9
About the reviewer……Page 10
Packt is searching for authors like you……Page 11
Preface……Page 25
Who this book is for……Page 26
What this book covers……Page 27
To get the most out of this book……Page 29
Download the example code files……Page 31
Download the color images……Page 32
Conventions used……Page 33
Get in touch……Page 34
Reviews……Page 35
Getting Started with Deep Learning……Page 36
A soft introduction to ML……Page 37
Working principles of ML algorithms……Page 38
Supervised learning……Page 41
Unsupervised learning……Page 43
Reinforcement learning……Page 45
Putting ML tasks altogether……Page 46
Delving into deep learning……Page 47
How did DL take ML into next level?……Page 48
Artificial Neural Networks……Page 52
Biological neurons……Page 53
A brief history of ANNs……Page 55
How does an ANN learn?……Page 57
ANNs and the backpropagation algorithm……Page 58
Forward and backward passes……Page 59
Weights and biases……Page 60
Weight optimization……Page 63
Activation functions……Page 65
Neural network architectures……Page 67
Deep neural networks……Page 68
Multilayer Perceptron……Page 70
Deep belief networks……Page 71
Autoencoders……Page 73
Convolutional neural networks……Page 74
Recurrent neural networks ……Page 76
Emergent architectures……Page 77
Residual neural networks……Page 78
Generative adversarial networks……Page 79
Capsule networks……Page 80
DL frameworks and cloud platforms……Page 81
Deep learning frameworks……Page 82
Cloud-based platforms for DL……Page 85
Deep learning from a disaster – Titanic survival prediction……Page 86
Problem description……Page 87
Configuring the programming environment……Page 90
Feature engineering and input dataset preparation……Page 92
Training MLP classifier ……Page 99
Evaluating the MLP classifier……Page 101
Frequently asked questions (FAQs)……Page 105
Summary……Page 106
Answers to FAQs……Page 107
Cancer Types Prediction Using Recurrent Type Networks……Page 113
Deep learning in cancer genomics……Page 114
Cancer genomics dataset description……Page 117
Preparing programming environment……Page 123
Titanic survival revisited with DL4J……Page 126
Multilayer perceptron network construction……Page 129
Hidden layer 1……Page 131
Hidden layer 2……Page 132
Output layer……Page 133
Network training……Page 135
Evaluating the model……Page 137
Cancer type prediction using an LSTM network……Page 140
Dataset preparation for training……Page 141
Recurrent and LSTM networks……Page 145
Dataset preparation……Page 150
LSTM network construction……Page 151
Network training……Page 153
Evaluating the model……Page 154
Frequently asked questions (FAQs)……Page 156
Summary……Page 157
Answers to questions……Page 158
Multi-Label Image Classification Using Convolutional Neural Networks……Page 166
Image classification and drawbacks of DNNs……Page 167
CNN architecture……Page 169
Convolutional operations……Page 171
Pooling and padding operations……Page 172
Fully connected layer (dense layer)……Page 175
Multi-label image classification using CNNs……Page 176
Problem description……Page 177
Description of the dataset……Page 179
Removing invalid images……Page 181
Workflow of the overall project……Page 182
Image preprocessing……Page 185
Extracting image metadata……Page 191
Image feature extraction……Page 193
Preparing the ND4J dataset……Page 200
Training, evaluating, and saving the trained CNN models……Page 202
Network construction……Page 203
Scoring the model……Page 208
Submission file generation……Page 209
Wrapping everything up by executing the main() method……Page 211
Frequently asked questions (FAQs)……Page 213
Summary……Page 214
Answers to questions……Page 215
Sentiment Analysis Using Word2Vec and LSTM Network……Page 221
Sentiment analysis is a challenging task……Page 223
Using Word2Vec for neural word embeddings……Page 227
Datasets and pre-trained model description……Page 229
Large Movie Review dataset for training and testing……Page 230
Folder structure of the dataset……Page 231
Description of the sentiment labeled dataset……Page 233
Word2Vec pre-trained model……Page 234
Sentiment analysis using Word2Vec and LSTM……Page 235
Preparing the train and test set using the Word2Vec model……Page 236
Network construction, training, and saving the model……Page 244
Restoring the trained model and evaluating it on the test set……Page 247
Making predictions on sample review texts……Page 249
Frequently asked questions (FAQs)……Page 253
Summary……Page 254
Answers to questions……Page 255
Transfer Learning for Image Classification……Page 263
Image classification with pretrained VGG16……Page 264
DL4J and transfer learning……Page 266
Developing an image classifier using transfer learning……Page 267
Dataset collection and description……Page 268
Architecture choice and adoption……Page 269
Train and test set preparation……Page 274
Network training and evaluation……Page 276
Restoring the trained model and inferencing……Page 279
Making simple inferencing……Page 281
Frequently asked questions (FAQs)……Page 284
Summary……Page 285
Answers to questions……Page 286
Real-Time Object Detection using YOLO, JavaCV, and DL4J……Page 288
Object detection from images and videos……Page 289
Object classification, localization, and detection……Page 290
Convolutional Sliding Window (CSW)……Page 294
Object detection from videos……Page 298
You Only Look Once (YOLO)……Page 300
Developing a real-time object detection project……Page 305
Step 1 – Loading a pre-trained YOLO model……Page 306
Step 2 – Generating frames from video clips……Page 309
Step 3 – Feeding generated frames into Tiny YOLO model……Page 312
Step 4 – Object detection from image frames……Page 314
Step 5 – Non-max suppression in case of more than one bounding box……Page 316
Step 6 – wrapping up everything and running the application……Page 319
Frequently asked questions (FAQs)……Page 323
Summary……Page 324
Answers to questions……Page 325
Stock Price Prediction Using LSTM Network……Page 326
State-of-the-art automated stock trading……Page 327
Developing a stock price predictive model……Page 331
Data collection and exploratory analysis……Page 333
Preparing the training and test sets……Page 339
LSTM network construction……Page 347
Network training, and saving the trained model……Page 350
Restoring the saved model for inferencing……Page 353
Evaluating the model……Page 354
Frequently asked questions (FAQs)……Page 362
Summary……Page 363
Answers to questions……Page 364
Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks……Page 366
Distributed deep learning across multiple GPUs……Page 367
Distributed training on GPUs with DL4J……Page 369
Video classification using convolutional – LSTM……Page 372
UCF101 – action recognition dataset……Page 373
Preprocessing and feature engineering……Page 375
Solving the encoding problem……Page 376
Data processing workflow……Page 378
Simple UI for checking video frames……Page 383
Preparing training and test sets……Page 385
Network creation and training……Page 387
Performance evaluation……Page 391
Distributed training on AWS deep learning AMI 9.0……Page 393
Frequently asked questions (FAQs)……Page 403
Summary……Page 404
Answers to questions……Page 405
Playing GridWorld Game Using Deep Reinforcement Learning……Page 407
Notation, policy, and utility for RL……Page 408
Notations in reinforcement learning……Page 409
Policy……Page 411
Utility……Page 412
Neural Q-learning……Page 413
Introduction to QLearning……Page 414
Neural networks as a Q-function……Page 416
Developing a GridWorld game using a deep Q-network……Page 419
Generating the grid……Page 421
Calculating agent and goal positions……Page 423
Calculating the action mask……Page 425
Providing guidance action……Page 426
Calculating the reward……Page 428
Flattening input for the input layer……Page 429
Network construction and training……Page 430
Playing the GridWorld game……Page 438
Frequently asked questions (FAQs)……Page 441
Summary……Page 442
Answers to questions……Page 443
Developing Movie Recommendation Systems Using Factorization Machines……Page 447
Recommendation systems……Page 448
Recommendation approaches……Page 449
Collaborative filtering approaches……Page 450
Content-based filtering approaches……Page 452
Hybrid recommender systems……Page 453
Model-based collaborative filtering……Page 454
The utility matrix……Page 455
The cold-start problem in collaborative-filtering approaches……Page 457
Factorization machines in recommender systems……Page 458
Developing a movie recommender system using FMs……Page 461
Dataset description and exploratory analysis……Page 463
Movie rating prediction……Page 470
Converting the dataset into LibFM format……Page 471
Training and test set preparation……Page 476
Movie rating prediction……Page 479
Which one makes more sense ;– ranking or rating?……Page 491
Frequently asked questions (FAQs)……Page 501
Summary……Page 502
Answers to questions……Page 503
Discussion, Current Trends, and Outlook……Page 507
Discussion and outlook……Page 508
Discussion on the completed projects……Page 509
Titanic survival prediction using MLP and LSTM networks……Page 510
Cancer type prediction using recurrent type networks……Page 511
Image classification using convolutional neural networks……Page 512
Sentiment analysis using Word2Vec and the LSTM network……Page 513
Image classification using transfer learning……Page 514
Real-time object detection using YOLO, JavaCV, and DL4J……Page 515
Stock price prediction using LSTM network……Page 516
Distributed deep learning – video classification using a convolutional-LSTM network……Page 517
Using deep reinforcement learning for GridWorld……Page 518
Movie recommender system using factorization machines……Page 519
Current trends and outlook……Page 520
Current trends……Page 521
Outlook on emergent DL architectures……Page 522
Residual neural networks……Page 523
GANs……Page 524
Capsule networks (CapsNet)……Page 525
Semantic image segmentation……Page 527
Deep learning for clustering analysis……Page 528
Frequently asked questions (FAQs)……Page 530
Answers to questions……Page 531
Other Books You May Enjoy……Page 533
Leave a review – let other readers know what you think……Page 535