Human-in-the-Loop Machine Learning
Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process.
Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster.
Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You'll find best pra ...
AI and Machine Learning for On-Device Development
AI is nothing without somewhere to run it. Now that mobile devices have become the primary computing device for most people, it's essential that mobile developers add AI to their toolbox. This insightful book is your guide to creating and running models on popular mobile platforms such as iOS and Android.
Laurence Moroney, lead AI advocate at Google, offers an introduction to machine learning techniques and tools, then walks you through writing Android and iOS apps powered by common ML models like computer vision and text recognition, using tools such as ML Kit, TensorFlow Lite, and Core ML. If you're a mobile developer, this book will help you take advantage of the ML revolution today.
Explore the options for implementing ML and AI on mobile devices; Create ML models for iOS and Android; Write ML Kit and TensorFlow Lite apps for iOS and Android, and Core ML/Create ML apps for iOS; Choose the best techniques and tools for your use case, such as cloud-based versus on-device infere ...
Quantum Machine Learning: An Applied Approach
Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research.
The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost.
Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qisk ...
AI for Healthcare with Keras and Tensorflow 2.0
Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries.
This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you c ...
TensorFlow 2 Pocket Reference
This easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself.
When and why would you feed training data as using NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases.
Understand best practices in TensorFlow model patterns and ML workflows; Use code snippets as templates in building TensorFlow models and workflows; Save development time by integrating prebuilt models in TensorFlow Hub; Make informed design choices about data ingestion, training paradigms, model saving, and inferencing; Address common scenari ...
Practical Machine Learning for Computer Vision
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.
Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.
You'll learn how to: Design ML architecture for computer vision tasks; Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropri ...
Automated Machine Learning with Microsoft Azure
Automated Machine Learning with Microsoft Azure helps you to build high-performing, accurate machine learning models in record time. It allows anyone to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. With a series of clicks on a guided user interface (GUI), novices and seasoned data scientists alike can train and deploy machine learning solutions to production with ease.
This book will teach you how to use Azure AutoML with both the GUI as well as the AzureML Python software development kit (SDK) in a careful, step-by-step way. First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train ...
Quantum Machine Learning with Python
Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This book explains Quantum Computing, which leverages the Quantum mechanical properties sub-atomic particles. It also examines Quantum machine learning, which can help solve some of the most challenging problems in forecasting, financial modeling, genomics, cybersecurity, supply chain logistics, cryptography among others.
You'll start by reviewing the fundamental concepts of Quantum Computing, such as Dirac Notations, Qubits, and Bell state, followed by postulates and mathematical foundations of Quantum Computing. Once the foundation base is set, you'll delve deep into Quantum based algorithms including Quantum Fourier transform, phase estimation, and HHL (Harrow-Hassidim-Lloyd) among others.
You'll then be introduced to Quantum machine learning and Quantum deep learning-based algorithms, ...
Beginning Machine Learning in the Browser
After conquering the fundamentals, you'll dig into the wilderness of ML. Employ the ML and Processing (P5) libraries for Human Gait analysis. Building up Gait recognition with themes, you'll come to understand a variety of ML implementation issu ...
You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js.
- Explore tensors, the most fundamental structure of machine learning;
- Convert data into tensors and back with a real-world example;
- Combine AI with the web using TensorFlow.js; ...
Racket Programming the Fun Way
At last, a lively guided tour through all the features, functions, and applications of the Racket programming language. You'll learn a variety of coding paradigms, including iterative, object oriented, and logic programming; create interactive graphics, draw diagrams, and solve puzzles as you explore Racket through fun computer science topics - from statistical analysis to search algorithms, the Turing machine, and more.
Early chapters cover basic Racket concepts like data types, syntax, variables, strings, and formatted output. You'll learn how to perform math in Racket's rich numerical environment, and use programming constructs in different problem domains (like coding solutions to the Tower of Hanoi puzzle). Later, you'll play with plotting, grapple with graphics, and visualize data. Then, you'll escape the confines of the command line to produce animations, interactive games, and a card trick program that'll dazzle your friends.
You'll learn how tot:
- Use DrRacket, an inte ...