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Title Page
Copyright and Credits
TensorFlow Deep Learning Projects
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Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Reviews
Recognizing traffic signs using Convnets
The dataset
The CNN network
Image preprocessing
Train the model and make predictions
Follow-up questions
Summary
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
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
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
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
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
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
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
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
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
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