Understand, design, and create cognitive applications using Watson's suite of APIs.
Develop your skills and work with IBM Watson APIs to build efficient and powerful cognitive apps
Learn how to build smart apps to carry out different sets of activities using real-world use cases
Get well versed with the best practices of IBM Watson and implement them in your daily work
Cognitive computing is rapidly infusing every aspect of our lives riding on three important fields: data science, machine learning (ML), and artificial intelligence (AI). It allows computing systems to learn and keep on improving as the amount of data in the system grows.
This book introduces readers to a whole new paradigm of computing – a paradigm that is totally different from the conventional computing of the Information Age. You will learn the concepts of ML, deep learning (DL), neural networks, and AI through the set of APIs provided by IBM Watson. This
book will help you build your own applications to understand, plan, and solve problems, and analyze them as per your needs. You will learn about various domains of cognitive computing, such as NLP, voice processing, computer vision, emotion analytics, and conversational systems, using different IBM
From this, the reader will learn what ML is, and what goes on in the background to make computers "do their magic," as well as where these concepts have been applied. Having achieved this, the readers will then be able to embark on their journey of learning, researching, and applying the
concept in their respective fields.
What you will learn
Get well versed with the APIs provided by IBM Watson on IBM Cloud
Learn ML, AI, cognitive computing, and neural network principles
Implement smart applications in fields such as healthcare, entertainment, security, and more
Understand unstructured content using cognitive metadata with the help of Natural Language Understanding
Use Watson's APIs to create real-life applications to realize their capabilities
Delve into various domains of cognitive computing, such as media analytics, embedded deep learning, computer vision, and more
Who this book is for
This book is for beginners and novices; having some knowledge about artificial intelligence and deep learning is an advantage, but not a prerequisite to benefit from this book. We explain the concept of deep learning and artificial intelligence through the set of tools IBM Watson provides.
Your perfect companion to prepare for and pass the CompTIA Project+ PK0-004 exam
Manage project changes and deliver desired project outcomes
Gain confidence in passing the PK0-004 exam with the help of practice questions
Obtain insight from J. Ashley Hunt, an accomplished subject matter expert
The CompTIA Project+ exam is designed for IT professionals who want to improve their career trajectory by gaining certification in project management specific to their industry. This guide covers everything necessary to pass the current iteration of the Project+ PK0-004 exam.
The CompTIA Project+ Certification Guide starts by covering project initiation best practices, including an understanding of organizational structures, team roles, and responsibilities. You'll then study best practices for developing a project charter and the scope of work to produce deliverables
necessary to obtain formal approval of the end result. The ability to monitor your project work and make changes as necessary to bring performance back in line with the plan is the difference between a successful and unsuccessful project. The concluding chapters of the book provide best practices to
help keep an eye on your projects and close them out successfully. The guide also includes practice questions created to mirror the exam experience and help solidify your understanding of core project management concepts.
By the end of this book, you will be able to develop creative solutions for complex issues faced in project management.
What you will learn
Develop a project charter and define team roles and responsibilities
Plan the project scope, schedule, budget, and risks
Process change requests and work with procurement documents
Close a formal project or phase and get an overview of Agile Project Management principles
Create a work breakdown structure (WBS) and dictionary
Discover best practices for identifying, analyzing, and responding to risk
Gain important exam information and discover the next steps
Who this book is for
The CompTIA Project+ Certification Guide is for entry-level project managers who are looking for a common language and best practices in the IT project management space as well as a certification to excel in their career.
This volume serves to recognize the uniqueness of the moment; the number of new users of e-services worldwide will double during 2015-2018 (moving from 2 billion users mostly living in the developed nations to an additional 2 billion users mostly living in developing nations). This radical embrace
of new e-service technologies will substantially improve the quality of lives for most residents globally. A profound happening occurring now! The new technologies combine rapidly delivering of a multitude of services at extremely low cost to adopters now having extremely low incomes relative to
residents living in developed nations. Adoption of e-service among residents in developing nations ends the debate as to whether or not marketing to the "bottom of the pyramid" is possible. The more relevant issues focus on describing and explaining e-service adoption processes in
developing nations. How are these processes being implemented? What obstacles had to be overcome in achieving these adoptions? How were these obstacles overcome? Read this volume for research providing useful answers to these questions.
Volume 23B includes two chapters covering problems and implementations of solutions in e-services adoption processes in developing nations. The first documents the unequal access and ICT usage, which is known as digital divide, to be one of the major obstacles to the implementation of e-government
systems. This research investigates the digital divide and its direct impact on e-government system success of local governments in Indonesia as well as indirect impact through the mediation role of trust. To achieve a comprehensive understanding of digital divide, this study introduced a new type
of digital divide, the innovativeness divide. It provides details for successful policy formulation to improve e-government readiness. The second explores what needs to be done to enable consumers to adopt e-services by airlines in developing nations. It includes new theory and empirical evidence
from both qualitative and quantitative studies in response to this issue. Exciting and useful chapters for executives and researchers seeking knowledge and theory of how to influence e-service adoptions in developing nations!
Combine popular machine learning techniques to create ensemble models using Python
Implement ensemble models using algorithms such as random forests and AdaBoost
Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model
Explore real-world data sets and practical examples coded in scikit-learn and Keras
Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model.
With its hands-on approach, you'll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as
classification and regression. Furthermore, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make
predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models.
By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.
What you will learn
Implement ensemble methods to generate models with high accuracy
Overcome challenges such as bias and variance
Explore machine learning algorithms to evaluate model performance
Understand how to construct, evaluate, and apply ensemble models
Analyze tweets in real time using Twitter's streaming API
Use Keras to build an ensemble of neural networks for the MovieLens dataset
Who this book is for
This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book.
Sudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani, Yang Wenzhuo
Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries
Your entry point into the world of artificial intelligence using the power of Python
An example-rich guide to master various RL and DRL algorithms
Explore the power of modern Python libraries to gain confidence in building self-trained applications
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.
The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including
image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement
algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL.
By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems.
This Learning Path includes content from the following Packt products:
Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran
Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani
What you will learn
Train an agent to walk using OpenAI Gym and TensorFlow
Solve multi-armed-bandit problems using various algorithms
Build intelligent agents using the DRQN algorithm to play the Doom game
Teach your agent to play Connect4 using AlphaGo Zero
Defeat Atari arcade games using the value iteration method
Discover how to deal with discrete and continuous action spaces in various environments
Who this book is for
If you're an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected.
Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii Tyshchenko
In today’s data-driven world, more sophisticated algorithms for data processing are in high demand, mainly when the data cannot be handled with the help of traditional techniques. Self-learning and adaptive algorithms are now widely used by such leading giants that as Google, Tesla, Microsoft,
and Facebook in their projects and applications.
In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. Including research relevant to
those studying cybernetics, applied mathematics, statistics, engineering, and bioinformatics who are working in the areas of machine learning, artificial intelligence, complex system modeling and analysis, neural networks, and optimization, this is an ideal read for anyone interested in learning
more about the fascinating new developments in machine learning.
Volume 22 includes two main chapters in both Part A and B. It appears in two parts because all chapters offer great depth in coverage of core issues senior executives must address for long-term survival of the firm: business intelligence, knowledge management, and understanding of the systems
dynamics of interfirm behavior. In the first main chapter of Part A Azizah Ahmad demonstrates that high-performing firms must achieve useful on-going business intelligence (BI). Ahmad shows how plans are designed and implemented for viable BI operations. The main contribution of the study is the
identification of the firm's internal resources of BI governance that influences successful BI deployment. In the second chapter Md Nuruzzaman shows how country risk, different political actions from the government, and bureaucratic behavior influence the activities in industry supply-chains in
emerging markets. The outcomes of the study are useful for various stakeholders of the Bangladeshi RMG industry sector ranging from the government to various private organizations. The applications of this study are extendable through further adaptation in other industries and various geographic
The first chapter in this book examines the relationships between absorptive capacity and effective knowledge management through the analysis of quantitative data drawn from managers and employees in residential aged care organizations in Western Australia. The author, Michael Preece, defines
absorptive capacity as the ability of an organization to use prior knowledge to recognize the value of new knowledge from external sources, assimilate this new knowledge, and apply it to the benefit of the organization. He provides valuable training in how service organizations go about transforming
new knowledge into effective actionable business plans. The second chapter by Mohammad Shamsuddoha provides an application of system dynamics modelling in firms in the poultry industry in Bangladesh. This chapter offers deep knowledge of the "fifth discipline" and beyond. Shamsuddoha uses
Vensim, a simulation-based software package, to build a simulation model with appropriate equations, formulae, and connectivity to replicate the real-life operation and outcome in a simulation environment. He also provides the in-depth knowledge necessary to learn to truly understand the fifth
This book is an investigation of the Swedish microchipping phenomenon and seeks to explain why, despite its many negative connotations in an international context, microchipping is relatively popular in Sweden. The author maps out the movement, examines its key drivers, and delves further to
discover why Swedes generally have a high trust in technology, and show little resistance to testing it.
The Swedish case is studied from the three main themes of surveillance, science fiction and transhumanism, and is built around interviews with Swedes who have embraced the technology. The arguments for and against microchipping are contextualised culturally and explained against a background of the
long established Swedish relationship with advanced technology, and with their unique level of trust in the government. The book will be of interest to undergraduate and graduate students in digital culture related disciplines.