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TensorFlow Deep Learning Projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning.
This book is your guide to master deep learning with TensorFlow, with the help of 10 real-world projects. You will train high-performance models in TensorFlow to generate captions for images automatically, predict stocks' performance, create intelligent chatbots, perform large-scale text classi...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Recognizing traffic signs using Convnets; The dataset; The CNN network; Image preprocessing; Train the model and make predictions; Follow-up questions; Summary; Chapter 2: Annotating Images with Object Detection API; The Microsoft common objects in context; The TensorFlow object detection API; Grasping the basics of R-CNN, R-FCN and SSD models; Presenting our project plan; Setting up an environment suitable for the project; Protobuf compilation; Windows installation; Unix installation.
- Provisioning of the project codeSome simple applications; Real-time webcam detection; Acknowledgements; Summary; Chapter 3: Caption Generation for Images; What is caption generation?; Exploring image captioning datasets; Downloading the dataset; Converting words into embeddings; Image captioning approaches; Conditional random field; Recurrent neural network on convolution neural network; Caption ranking; Dense captioning; RNN captioning; Multimodal captioning; Attention-based captioning; Implementing a caption generation model; Summary; Chapter 4: Building GANs for Conditional Image Creation.
- Introducing GANsThe key is in the adversarial approach; A cambrian explosion; DCGANs; Conditional GANs; The project; Dataset class; CGAN class; Putting CGAN to work on some examples; MNIST; Zalando MNIST; EMNIST; Reusing the trained CGANs; Resorting to Amazon Web Service; Acknowledgements; Summary; Chapter 5: Stock Price Prediction with LSTM; Input datasets
- cosine and stock price; Format the dataset; Using regression to predict the future prices of a stock; Long short-term memory
- LSTM 101; Stock price prediction with LSTM; Possible follow
- up questions; Summary.
- Chapter 6: Create and Train Machine Translation SystemsA walkthrough of the architecture; Preprocessing of the corpora; Training the machine translator; Test and translate; Home assignments; Summary; Chapter 7: Train and Set up a Chatbot, Able to Discuss Like a Human; Introduction to the project; The input corpus; Creating the training dataset; Training the chatbot; Chatbox API; Home assignments; Summary; Chapter 8: Detecting Duplicate Quora Questions; Presenting the dataset; Starting with basic feature engineering; Creating fuzzy features; Resorting to TF-IDF and SVD features.
- Mapping with Word2vec embeddingsTesting machine learning models; Building a TensorFlow model; Processing before deep neural networks; Deep neural networks building blocks; Designing the learning architecture; Summary; Chapter 9: Building a TensorFlow Recommender System; Recommender systems; Matrix factorization for recommender systems; Dataset preparation and baseline; Matrix factorization; Implicit feedback datasets; SGD-based matrix factorization; Bayesian personalized ranking; RNN for recommender systems; Data preparation and baseline; RNN recommender system in TensorFlow; Summary.