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Python for Machine Learning
Using clear explanations and step-by-step tutorial lessons, you will learn the underlying mechanics of the Python language, the tools in its ecosystem, tips and tricks, and much more.
Building Transformer Models with Attention
If you have been around long enough, you should notice that your search engine can understand human language much better than in previous years. The game changer was the attention mechanism. It is not an easy topic to explain, and it is sad to see someone consider that as secret magic. If we know more about attention and understand the problem it solves, we can decide if it fits into our project and be more comfortable using it. If you are interested in natural language processing and want to tap into the most advanced technique in deep learning for NLP, this new Ebook—in the friendly Machine Learning Mastery style that you’re used to—is all you need. Using clear explanations and step-by-step tutorial lessons, you will learn how attention can get the job done and why we build transformer models to tackle the sequence data. You will also create your own transformer model that translates sentences from one language to another.
Calculus for Machine Learning
Calculus seems to be obscure, but it is everywhere. In machine learning, while we rarely write code on differentiation or integration, the algorithms we use have theoretical roots in calculus. If you ever wondered how to understand the calculus part when you listen to people explaining the theory behind a machine learning algorithm, this new Ebook, in the friendly Machine Learning Mastery style that you’re used to, is all you need. Using clear explanations and step-by-step tutorial lessons, you will understand the concept of calculus, how it is relates to machine learning, what it can help us on, and much more.
A Glass Half Full
Trade has played a critical role in global poverty reduction. In harnessing the potential of trade, some of the most successful countries have developed strong trade relationships with their neighbors. However, many South Asian countries have trade regimes that often offset the positive impact of geography and proximity. This report documents systematically the gaps between current and potential trade in South Asia and addresses important specific barriers that have held trade back. These barriers include tariffs and paratariffs, real and perceived nontariff barriers, connectivity costs, and the broader trust deficit. This policy-focused report unpacks these critical barriers to effective tr...
Human-Computer Interaction: Design and Development Approaches
This four-volume set LNCS 6761-6764 constitutes the refereed proceedings of the 14th International Conference on Human-Computer Interaction, HCII 2011, held in Orlando, FL, USA in July 2011, jointly with 8 other thematically similar conferences. The revised papers presented were carefully reviewed and selected from numerous submissions. The papers accepted for presentation thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The papers of this first volume are organized in topical sections on HCI design, model-based and patterns-based design and development, cognitive, psychological and behavioural issues in HCI, development methods, algorithms, tools and environments, and image processing and retrieval in HCI.
Automated Machine Learning
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Neural Connectomics Challenge
This book illustrates the thrust of the scientific community to use machine learning concepts for tackling a complex problem: given time series of neuronal spontaneous activity, which is the underlying connectivity between the neurons in the network? The contributing authors also develop tools for the advancement of neuroscience through machine learning techniques, with a focus on the major open problems in neuroscience. While the techniques have been developed for a specific application, they address the more general problem of network reconstruction from observational time series, a problem of interest in a wide variety of domains, including econometrics, epidemiology, and climatology, to cite only a few. divThe book is designed for the mathematics, physics and computer science communities that carry out research in neuroscience problems. The content is also suitable for the machine learning community because it exemplifies how to approach the same problem from different perspectives./divdivbr/divdivbr
Human-Computer Interaction: Towards Mobile and Intelligent Interaction Environments
This four-volume set LNCS 6761-6764 constitutes the refereed proceedings of the 14th International Conference on Human-Computer Interaction, HCII 2011, held in Orlando, FL, USA in July 2011, jointly with 8 other thematically similar conferences. The revised papers presented were carefully reviewed and selected from numerous submissions. The papers accepted for presentation thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The papers of this volume are organized in topical sections on mobile interaction, interaction in intelligent environments, orientation and navigation, in-vehicle interaction, social and environmental issues in HCI, and emotions in HCI.
Cause Effect Pairs in Machine Learning
This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the st...
Soft Computing for Knowledge Discovery
Knowledge discovery is an area of computer science that attempts to uncover interesting and useful patterns in data that permit a computer to perform a task autonomously or assist a human in performing a task more efficiently. Soft Computing for Knowledge Discovery provides a self-contained and systematic exposition of the key theory and algorithms that form the core of knowledge discovery from a soft computing perspective. It focuses on knowledge representation, machine learning, and the key methodologies that make up the fabric of soft computing - fuzzy set theory, fuzzy logic, evolutionary computing, and various theories of probability (e.g. naïve Bayes and Bayesian networks, Dempster-Sh...
