“…David Huntley is Research Scientist with the Geological Survey of Canada and Open Learning Faculty at Thompson Rivers University, British Columbia. …”

“…Lecia Barker, NCWIT Senior Research Scientist and Associate Professor at University of Colorado Boulder. …”

“…By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.What you will learn* Write Python scripts to extract features from graphs* Distinguish between the main graph representation learning techniques* Learn how to extract data from social networks, financial transaction systems, for text analysis, and more* Implement the main unsupervised and supervised graph embedding techniques* Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more* Deploy and scale out your application seamlesslyWho this book is forThis book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. …”

“…It is also a useful resource for web developers, data scientists, and analysts who want to improve their efficiency with the help of prompting. …”

“…What you will learn Understand the core components of blockchain transactions and blocks Identify reliable sources of on-chain and off-chain data to build robust datasets Understand key Web3 business questions and how data science can offer solutions Build your skills to create and query NFT- and DeFi-specific datasets Implement a machine learning toolbox with real-world use cases in the Web3 space Who this book is for This book is designed for data professionals--data analysts, data scientists, or data engineers-- and business professionals, aiming to acquire the skills for extracting data from the Web3 ecosystem, as it demonstrates how to effectively leverage data tools for in-depth analysis of blockchain transactional data. …”

“…What you will learn Implement tf.keras APIs in TF 2.0 to build, train, and deploy production-grade models Build models with Keras integration and eager execution Explore distribution strategies to run models on GPUs and TPUs Perform what-if analysis with TensorBoard across a variety of models Discover Vision Kit, Voice Kit, and the Edge TPU for model deployments Build complex input data pipelines for ingesting large training datasets Who this book is for If you're a data scientist, machine learning practitioner, deep learning researcher, or AI enthusiast who wants to migrate code to TensorFlow 2.0 and explore the latest features of TensorFlow 2.0, this book is for you. …”

“…In addition to this, you'll learn how to deploy models and improve their performance with ease.By the end of this book, you'll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.What you will learn* Explore various AI techniques to build smart IoT solutions from scratch* Use machine learning and deep learning techniques to build smart voice recognition and facial detection systems* Gain insights into IoT data using algorithms and implement them in projects* Perform anomaly detection for time series data and other types of IoT data* Implement embedded systems learning techniques for machine learning on small devices* Apply pre-trained machine learning models to an edge device* Deploy machine learning models to web apps and mobile using TensorFlow.js and JavaWho this book is forIf you're an IoT practitioner looking to incorporate AI techniques to build smart IoT solutions without having to trawl through a lot of AI theory, this AI IoT book is for you. Data scientists and AI developers who want to build IoT-focused AI solutions will also find this book useful. …”

“…What you will learn Understand the importance of data literacy and how to communicate effectively using data Find out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysis Wrangle data and create DataFrames using pandas Produce charts and data visualizations using time-series datasets Discover relationships and how to join data together using SQL Use NLP techniques to work with unstructured data to create sentiment analysis models Discover patterns in real-world datasets that provide accurate insights Who this book is for This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. …”

“…By the end of this TensorFlow book, you’ll have learned the patterns needed for TensorFlow Enterprise model development, data pipelines, training, and deployment.What you will learnDiscover how to set up a GCP TensorFlow Enterprise cloud instance and environmentHandle and format raw data that can be consumed by the TensorFlow model training processDevelop ML models and leverage prebuilt models using the TensorFlow Enterprise APIUse distributed training strategies and implement hyperparameter tuning to scale and improve your model training experimentsScale the training process by using GPU and TPU clustersAdopt the latest model optimization techniques and deployment methodologies to improve model efficiencyWho this book is forThis book is for data scientists, machine learning developers or engineers, and cloud practitioners who want to learn and implement various services and features offered by TensorFlow Enterprise from scratch. …”

“…Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: "Data analytics should first and foremost be about action and value. …”

“…By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch.What you will learnBuild deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras APIImplement state-of-the-art deep reinforcement learning algorithms using minimal codeBuild, train, and package deep RL agents for cryptocurrency and stock tradingDeploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud servicesSpeed up agent development using distributed DNN model trainingExplore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)Who this book is forThe book is for machine learning application developers, AI and applied AI researchers, data scientists, deep learning practitioners, and students with a basic understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using TensorFlow 2.x…”

“…By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.What you will learnImplement data preprocessing steps and optimize model hyperparametersDelve into representational learning with adversarial autoencodersUse active learning, recommenders, knowledge embedding, and SAT solversGet to grips with probabilistic modeling with TensorFlow probabilityRun object detection, text-to-speech conversion, and text and music generationApply swarm algorithms, multi-agent systems, and graph networksGo from proof of concept to production by deploying models as microservicesUnderstand how to use modern AI in practiceWho this book is forThis AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. …”

“…It is a key tool to learn for the analyst and data scientists alike. Its simplicity and drag and drop interface make it a breeze to use! …”

“…Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data…”

“…By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learnImplement text and music generating models using PyTorchBuild a deep Q-network (DQN) model in PyTorchExport universal PyTorch models using Open Neural Network Exchange (ONNX)Become well-versed with rapid prototyping using PyTorch with fast.aiPerform neural architecture search effectively using AutoMLEasily interpret machine learning (ML) models written in PyTorch using CaptumDesign ResNets, LSTMs, Transformers, and more using PyTorchFind out how to use PyTorch for distributed training using the torch.distributed APIWho this book is forThis book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. …”

“…What you will learn Create interactive plots with Makie, Plots, Jupyter, and Pluto Create standard statistical plots and visualize clustering results Plot geographically distributed and biological data Visualize graphs and networks using GraphRecipes and GraphPlots Find out how to draw and animate objects with Javis, Plots, and Makie Define plot themes to reuse plot visual aspect customizations Arrange plots using Plots, Makie, and Gadfly layout systems Define new plot types and determine how Plots and Makie show objects Who this book is for Data analysts looking to explore Julia's data visualization capabilities will find this book helpful, along with scientists and academics who want to generate and communicate knowledge and improve their teaching material. …”

“…What you will learn Develop stunning visualizations to explain complex data with clarity Build interactive dashboards to drive actionable user insights Explore Data Model capabilities and interlink data from various sources Create and use calculations to solve problems and enrich your analytics Enable smart decision-making with data clustering, distribution, and forecasting Extend Tableau's native functionality with extensions, scripts, and AI through CRM Analytics (formerly Einstein Analytics) Leverage Tableau Prep Builder's amazing capabilities for data cleaning and structuring Share your data stories to build a culture of trust and action Who this book is for This Tableau book is for aspiring BI developers and data analysts, data scientists, researchers, and anyone else who wants to gain a deeper understanding of data through Tableau. …”

“…Bryant's first visit to Great Britain came at the height of his poetic and journalistic fame in 1845, bringing him into cordial intimacy with members of Parliament, scientists, journalists, artists, and writers. In detailed letters to his wife, published here for the first time, he describes the pleasures he took in breakfasting with the literary patron Samuel Rogers and the American minister Edward Everett, boating on the Thames with artists and with diarist Henry Crabb Robinson, spending an evening in the home of Leigh Hunt, and calling on the Wordsworths at Rydal Mount as well as in the distinctions paid him at a rally of the Anti-Corn-Law League in Covent Garden Theatre, and at the annual meeting in Cambridge of the British Association for the Advancement of Science. …”

“…Software engineering students, developers, data scientists, and anyone who is transitioning into NLP and is interested in learning about practical approaches to solving problems with Flair will find this book useful. …”

“…This book is best suited for GIS developers, GIS analysts, data analysts, and data scientists looking to enhance their solutions with geospatial data for cloud-centric applications. …”