“…What you will learn Train parameterised quantum circuits as generative models that excel on NISQ hardware Solve hard optimisation problems Apply quantum boosting to financial applications Learn how the variational quantum eigensolver and the quantum approximate optimisation algorithms work Analyse the latest algorithms from quantum kernels to quantum semidefinite programming Apply quantum neural networks to credit approvals Who this book is for This book is for Quants and developers, data scientists, researchers, and students in quantitative finance. …”

“…In addition to delving into the fundamental concepts, Barton and Henry address sample big data and machine learning use cases in different industries and present demos featuring the most common tools (such as Hadoop, TensorFlow, Matlab/Octave, R, and Python) in various fields used by data scientists and researchers. At the conclusion of this video course, you will be armed with knowledge and application skills required to become proficient in articulating big data analytics and machine learning principles and possibilities. …”

“…Mark Kadrich is President and CEO of The Security Consortium, which performs in-depth testing and evaluation of security products and vendors. As Senior Scientist for Sygate Technologies, he was responsible for developing corporate policies, understanding security trends, managing government certification programs, and evangelization. …”

“…Lovins , environmentalist, Chairman and Chief Scientist of Rocky Mountain Institute Professor Wangari Maathai , environmental and political activist, Nobel Peace Prize Winner Ricardo Navarro , founder and director of the Salvadoran Centre for Appropriate Technology (CESTA), winner of the prestigious Goldman prize Dr Vandana Shiva , physicist, environmental activist and author Jeffrey Swartz , CEO of Timberland Worldwide Sir Crispin Tickell , diplomat, academic, environmentalist, au..…”

“…Taken together, their essays reveal a distinctive mindset and organizational culture, animated by a deeply held commitment to the hard work of progress. IBM engineers and scientists invented many of the building blocks of modern information technology, including the memory chip, the disk drive, the scanning tunneling microscope (essential to nanotechnology) and even new fields of mathematics. …”

“…A growing interest in plant biostimulants from industries and scientists was demonstrated by the high number of published peer-reviewed articles, conferences, workshops, and symposia in the past ten years. …”

“…Includes problems as well as an accompanying website containing solutions, software tools and interactive flash animations (<a href="http://wiley.teletraffic.pl/">http://wiley.teletraffic.pl</a>) This book will be an invaluable guide for professional engineers (radio planning engineers, optimization engineers, transmission engineers, core network engineers, Service Management engineers) working in the areas of mobile wireless networks technology, not only in optimization process, but also in profitability assessment of newly implemented services (i.e. in NPV - Net Present Value analysis), and researchers and scientists. Advanced students in the fields of mobile communications networks and systems will also find this book insightful…”

“…Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA What you will learn Discover how GANs work and the advantages and challenges of working with them Control the output of GANs with the help of conditional GANs, using embedding and space manipulation Apply GANs to computer vision, natural language processing (NLP), and audio processing Understand how to implement progressive growing of GANs Use GANs for image synthesis and speech enhancement Explore the future of GANs in visual and sonic arts Implement pix2pixHD to turn semantic label maps into photorealistic images Who this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a mix of theory and hands-on co..…”

“…What you will learn Study feature selection and the feature engineering process Assess performance and error trade-offs for linear regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector Machines (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is for Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. …”

“…What you will learn Build shallow neural network prediction models Prevent models from overfitting the data to improve generalizability Explore techniques for finding the best hyperparameters for deep learning models Create NLP models using Keras and TensorFlow in R Use deep learning for computer vision tasks Implement deep learning tasks, such as NLP, recommendation systems, and autoencoders Who this book is for This second edition of R Deep Learning Essentials is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. …”

“…What you will learn Get well-versed in blockchain basics and AI methodologies Understand the significance of data collection and cleaning in AI modeling Discover the application of analytics in cryptocurrency trading Get to grips with open, permissioned, and private blockchains Explore the DIApp design pattern and its merit in digital solutions Find out how LSTM and ARIMA can be applied in crypto trading Use the DIApp design pattern to build a sample contact tracing application Get started with building your own DIApps across various domains Who this book is for This book is for blockchain and AI architects, developers, data scientists, data engineers, and evangelists who want to harness the power of artificial intelligence in blockchain applications. …”

“…What you will learn Gain useful insights into different machine and deep learning models Build and deploy robust deep learning systems to production Train machine and deep learning models with diverse infrastructure specifications Scale AI apps without dealing with the complexity of managing the underlying infrastructure Monitor and Manage AI experiments efficiently Create AI apps using AWS pre-trained AI services Who this book is for This book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. …”

“…What you will learn Use OpenAI Gym as a framework to implement RL environments Find out how to define and implement reward function Explore Markov chain, Markov decision process, and the Bellman equation Distinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning Understand the multi-armed bandit problem and explore various strategies to solve it Build a deep Q model network for playing the video game Breakout Who this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. …”

“…What you will learn Explore the Go ecosystem of libraries and communities for deep learning Get to grips with Neural Networks, their history, and how they work Design and implement Deep Neural Networks in Go Get a strong foundation of concepts such as Backpropagation and Momentum Build Variational Autoencoders and Restricted Boltzmann Machines using Go Build models with CUDA and benchmark CPU and GPU models Who this book is for This book is for data scientists, machine learning engineers, and AI developers who want to build state-of-the-art deep learning models using Go. …”

“…What you will learn Import, clean, and explore data to perform preliminary analysis using powerful Python packages Identify and transform erroneous data using different data wrangling techniques Explore the use of multiple regression to describe non-linear relationships Discover hypothesis testing and explore techniques of time-series analysis Understand and interpret results obtained from graphical analysis Build, train, and optimize predictive models to estimate results Perform complex EDA techniques on open source datasets Who this book is for This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. …”

“…What you will learn Grasp the fundamentals of AI, ML, and AWS Explore the AWS command line, its interface, and its applications Import and export data to Amazon S3 Perform topic modeling on a set of documents to analyze common themes Develop a custom chatbot to get the latest stock market quotes Create a personal call center and connect it to the chatbot Who this book is for If you are a machine learning enthusiast, data scientist, or programmer who wants to explore AWS's artificial intelligence and machine learning capabilities, this book is for you. …”

“…What you will learn Access and manipulate pixels in OpenCV using BGR and grayscale images Create histograms to better understand image content Use contours for shape analysis, object detection, and recognition Track objects in videos using a variety of trackers available in OpenCV Discover how to apply face recognition tasks using computer vision techniques Visualize 3D objects in point clouds and polygon meshes using Open3D Who this book is for If you are a researcher, developer, or data scientist looking to automate everyday tasks using computer vision, this workshop is for you. …”

“…What you will learn Understand the importance of data visualization in data science Implement NumPy and pandas operations on real-life datasets Create captivating data visualizations using plotting libraries Use advanced techniques to plot geospatial data on a map Integrate interactive visualizations to a webpage Visualize stock prices with Bokeh and analyze Airbnb data with Matplotlib Who this book is for The Data Visualization Workshop is for beginners who want to learn data visualization, as well as developers and data scientists who are looking to enrich their practical data science skills. …”

“…What you will learn Pre-process data to make it ready to use for machine learning Create data visualizations with Matplotlib Use scikit-learn to perform dimension reduction using principal component analysis (PCA) Solve classification and regression problems Get predictions using the XGBoost library Process images and create machine learning models to decode them Process human language for prediction and classification Use TensorBoard to monitor training metrics in real time Find the best hyperparameters for your model with AutoML Who this book is for Data Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to m..…”

“…What you will learn Automate commercial bank pricing with reinforcement learning Perform technical analysis using convolutional layers in Keras Use natural language processing (NLP) for predicting market responses and visualizing them using graph databases Deploy a robot advisor to manage your personal finances via Open Bank API Sense market needs using sentiment analysis for algorithmic marketing Explore AI adoption in banking using practical examples Understand how to obtain financial data from commercial, open, and internal sources Who this book is for This is one of the most useful artificial intelligence books for machine learning engineers, data engineers, and data scientists working in the fin..…”