Former world chess champion Garry Kasparov is long overdue for telling his side of the story regarding his famous match with the IBM computer Deep Blue in May 1997. In the new book Deep Thinking, Kasparov and longtime writing partner Mig Greengard intertwine his experiences--before, during, and after the match--with a historical overview of chess-playing AI to produce a well-written, accessible book that provides food for thought about our future alongside increasingly intelligent machines. Many in the chess community, who may buy the book for insight into the match's outcome, will be surprised to see a side of Kasparov that the general public has not seen before--a man who has mellowed over time. Those in the artificial-intelligence and technology communities may buy this book because of the intriguing tag line "Where machine intelligence ends and human creativity begins."
The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
What Advice Would You Give Your Younger Data Scientist Self? https://t.co/IVjNdrhdZC Why the'boring' part of #DataScience is actually the most interesting https://t.co/r8uR6fIgTj What Advice Would You Give Your Younger Data Scientist Self? https://t.co/IVjNdrhdZC Why the'boring' part of #DataScience is actually the most interesting https://t.co/r8uR6fIgTj What Advice Would You Give Your Younger Data Scientist Self? https://t.co/IVjNdrhdZC Why the'boring' part of #DataScience is actually the most interesting https://t.co/r8uR6fIgTj
The author prefers pseudocode and text explanations of algorithms to equations, and when he does use equations they use clear, commonly understandable notation rather than the terse greek alphabet soup preferred by many of the more mathematically oriented authors. It should be pointed out that about 10% of the text of this book is devoted simply as a user manual for an open source MLA package called Weka. One area that the text does not cover (and, for many software engineers this is not a fault) is some of the mathematics behind some of the algorithms the author proposes. I'm not saying this is bad, because if you're a good software engineer the first thing you'll do it look for an existing implementation that you can alter to fit your needs, so he's right.
Fascination with digital computers intensified during the 1950s, and the so-called "thinking machines" began to influence theories about the human mind. It took only a few decades after Shannon wrote his paper for engineers to build a computer that could play chess brilliantly. Even though it was the first time a machine had beaten a world champion in a formal match, to computer scientists and chess masters alike the outcome wasn't much of a surprise. Whereas most software programs apply rules to data, machine-learning algorithms do the reverse: they distill rules from data, and then apply those rules to make judgments about new situations.
Those who run regularly or who have experienced the endorphin euphoria known as "runner's high", can experience the same heady feeling reading Joanna Goodman's "Robots in Law: How Artificial Intelligence is Transforming Legal Services" (Ark, 2016). The hope was that like Nike running apps, "Robots" would provide her with the tools and insights she needed to understand the AI legal tech hype, and intelligently speak to the topic with fellow colleagues in legal innovation. While the Twitterati debate gets granular rather quickly with varying definitions of the terms artificial intelligence, lawyer and robot, the takeaway is consistent with the "and" versus "or" conundrum – none of, robot (traditional AI), lawyer (human intelligence), or a robot lawyer (augmented AI) provide a perfect solution or path forward. Goodman showcases LISA as an example of AI augmentation since the tool leaves more complex issues to human lawyers, but misses the opportunity to explore further with insights on the critical limitations of the App.
In machine learning, training is very essential, as it is what helps the machine learning algorithms to learn and show an improvement next time from their experience. This book helps you understand how to employ the concept of neural networks in machine learning. You will also learn more about the backpropagation algorithm and how it helps the machine learning systems to learn and show an improvement based on experience. This book guides you on how to create a machine learning system in Python.
He is also an Affiliate Associate Professor in Department of Electrical and Computer Engineering at Concordia University, Canada. Prior to joining Enjoyor Inc. in 2012, he held positions with Huawei Technologies, the China Academy of Telecommunication Technology, the Chinese University of Hong Kong, the Hong Kong University of Science and Technology, and Concordia University. His current research interests include signal processing, neural networks, intelligent systems, and wireless communications. Swamy is currently a Research Professor and holder of the Concordia Tier I Research Chair Signal Processing in the Department of Electrical and Computer Engineering, Concordia University, where he was Dean of the Faculty of Engineering and Computer Science from 1977 to 1993 and the founding Chair of the EE department.
This book is your practical guide towards novice to master in machine learning with Python in six steps. This book is also helpful for current Machine Learning practitioners to learn the advanced topics such as Hyperparameter tuning, various ensemble techniques, Natural Language Processing (NLP), deep learning, and basics of reinforcement learning. Each topic has two parts, the first part will cover the theoretical concepts and the second part will cover practical implementation with different Python packages. The traditional approach of math to machine learning i.e., learning all the mathematic then understanding how to implement them to solve problems need a great deal of time/effort which has proven to be not efficient for working professionals looking to switch careers.
In this book, scikit-learn which is the best open source machine learning library is introduced for businessman or businesswoman and used in Python for big data analysis. Three examples are illustrated how to use ensemble machine learning for big data analysis. In the first example, rules between poker hands (five cards) and their rankings can be trained using ensemble machine learning. The illustrated ensemble machine learning includes AdaBoost, Bagging, ExtraTrees, GradientBoosting, and RandomForest.