Cargando…
Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras /
This book includes 9 projects on building smart and practical AI-based systems. These projects cover solutions to different domain-specific problems in healthcare, e-commerce and more. With this book, you will apply different machine learning and deep learning techniques and learn how to build your...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt,
[2019]
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Foundations of Artificial Intelligence Based Systems; Neural networks; Neural activation units; Linear activation units; Sigmoid activation units; The hyperbolic tangent activation function; Rectified linear unit (ReLU); The softmax activation unit; The backpropagation method of training neural networks; Convolutional neural networks; Recurrent neural networks (RNNs); Long short-term memory (LSTM) cells; Generative adversarial networks; Reinforcement learning; Q-learning
- Deep Q-learning Transfer learning; Restricted Boltzmann machines; Autoencoders ; Summary; Chapter 2: Transfer Learning; Technical requirements; Introduction to transfer learning; Transfer learning and detecting diabetic retinopathy; The diabetic retinopathy dataset ; Formulating the loss function; Taking class imbalances into account; Preprocessing the images ; Additional data generation using affine transformation; Rotation ; Translation; Scaling ; Reflection; Additional image generation through affine transformation; Network architecture ; The VGG16 transfer learning network
- The InceptionV3 transfer learning networkThe ResNet50 transfer learning network; The optimizer and initial learning rate; Cross-validation; Model checkpoints based on validation log loss ; Python implementation of the training process; Dynamic mini batch creation during training ; Results from the categorical classification; Inference at testing time ; Performing regression instead of categorical classification ; Using the keras sequential utils as generator ; Summary; Chapter 3: Neural Machine Translation; Technical requirements; Rule-based machine translation; The analysis phase
- Lexical transfer phase Generation phase ; Statistical machine-learning systems; Language model ; Perplexity for language models; Translation model; Neural machine translation; The encoder-decoder model; Inference using the encoder-decoder model; Implementing a sequence-to-sequence neural translation machine; Processing the input data; Defining a model for neural machine translation; Loss function for the neural translation machine; Training the model; Building the inference model; Word vector embeddings; Embeddings layer; Implementing the embeddings-based NMT; Summary
- Chapter 4: Style Transfer in Fashion Industry using GANsTechnical requirements; DiscoGAN; CycleGAN; Learning to generate natural handbags from sketched outlines; Preprocess the Images; The generators of the DiscoGAN; The discriminators of the DiscoGAN; Building the network and defining the cost functions; Building the training process; Important parameter values for GAN training; Invoking the training; Monitoring the generator and the discriminator loss ; Sample images generated by DiscoGAN; Summary; Chapter 5: Video Captioning Application; Technical requirements