TensorFlow Deep Learning Projects : 10 Real-World Projects on Computer Vision, Machine Translation, Chatbots, and Reinforcement Learning.

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 code -- Some 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 GANs -- The 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 Systems -- A 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 embeddings -- Testing 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 -- Chapter 10: Video Games by Reinforcement Learning -- The game legacy -- The OpenAI version -- Installing OpenAI on Linux (Ubuntu 14.04 or 16.04) -- Lunar Lander in OpenAI Gym -- Exploring reinforcement learning through deep learning -- Tricks and tips for deep Q-learning -- Understanding the limitations of deep Q-learning -- Starting the project -- Defining the AI brain -- Creating memory for experience replay -- Creating the agent -- Specifying the environment -- Running the reinforcement learning process -- Acknowledgements -- Summary -- Other Books You May Enjoy.

Index.

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 classification, develop recommendation systems, and more.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.