Book description
Implement machine learning and deep learning methodologies to build smart, cognitive AI projects using Python
Key Features
- A go-to guide to help you master AI algorithms and concepts
- 8 real-world projects tackling different challenges in healthcare, e-commerce, and surveillance
- Use TensorFlow, Keras, and other Python libraries to implement smart AI applications
Book Description
This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python.
The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI.
By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle.
What you will learn
- Build an intelligent machine translation system using seq-2-seq neural translation machines
- Create AI applications using GAN and deploy smart mobile apps using TensorFlow
- Translate videos into text using CNN and RNN
- Implement smart AI Chatbots, and integrate and extend them in several domains
- Create smart reinforcement, learning-based applications using Q-Learning
- Break and generate CAPTCHA using Deep Learning and Adversarial Learning
Who this book is for
This book is intended for data scientists, machine learning professionals, and deep learning practitioners who are ready to extend their knowledge and potential in AI. If you want to build real-life smart systems to play a crucial role in every complex domain, then this book is what you need. Knowledge of Python programming and a familiarity with basic machine learning and deep learning concepts are expected to help you get the most out of the book
Table of contents
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Foundations of Artificial Intelligence Based Systems
-
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
- Network architecture
- The optimizer and initial learning rate
- Cross-validation
- Model checkpoints based on validation log loss
- Python implementation of the training process
- Results from the categorical classification
- Inference at testing time
- Performing regression instead of categorical classification
- Using the keras sequential utils as generator
- Summary
- Neural Machine Translation
-
Style Transfer in Fashion Industry using GANs
- Technical 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
-
Video Captioning Application
- Technical requirements
- CNNs and LSTMs in video captioning
- A sequence-to-sequence video-captioning system
- Data for the video-captioning system
- Processing video images to create CNN features
- Processing the labelled captions of the video
- Building the train and test dataset
- Building the model
- Creating a word vocabulary for the captions
- Training the model
- Training results
- Inference with unseen test videos
- Summary
-
The Intelligent Recommender System
- Technical requirements
- What is a recommender system?
- Latent factorization-based recommendation system
- Deep learning for latent factor collaborative filtering
- SVD++
- Restricted Boltzmann machines for recommendation
- Contrastive divergence
- Collaborative filtering using RBMs
- Collaborative filtering implementation using RBM
- Inference using the trained RBM
- Summary
-
Mobile App for Movie Review Sentiment Analysis
- Technical requirements
- Building an Android mobile app using TensorFlow mobile
- Movie review rating in an Android app
- Preprocessing the movie review text
- Building the model
- Training the model
- Freezing the model to a protobuf format
- Creating a word-to-token dictionary for inference
- App interface page design
- The core logic of the Android app
- Testing the mobile app
- Summary
-
Conversational AI Chatbots for Customer Service
- Technical requirements
- Chatbot architecture
- A sequence-to-sequence model using an LSTM
- Building a sequence-to-sequence model
-
Customer support on Twitter
- Creating data for training the chatbot
- Tokenizing the text into word indices
- Replacing anonymized screen names
- Defining the model
- Loss function for training the model
- Training the model
- Generating output responses from the model
- Putting it all together
- Invoking the training
- Results of inference on some input tweets
- Summary
-
Autonomous Self-Driving Car Through Reinforcement Learning
- Technical requirements
- Markov decision process
- Learning the Q value function
- Deep Q learning
- Formulating the cost function
- Double deep Q learning
- Implementing an autonomous self-driving car
- Discretizing actions for deep Q learning
- Implementing the Double Deep Q network
- Designing the agent
- The environment for the self-driving car
- Putting it all together
- Results from the training
- Summary
- CAPTCHA from a Deep-Learning Perspective
- Other Books You May Enjoy
Product information
- Title: Intelligent Projects Using Python
- Author(s):
- Release date: January 2019
- Publisher(s): Packt Publishing
- ISBN: 9781788996921
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