This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

Never mind having robots deliver pizza -- if MIT and QCRI researchers have their way, the automatons will make your pizza as well. They've developed a neural network, PizzaGAN (Generative Adversarial Network), that learns how to make pizza using pictures. After training on thousands of synthetic and real pizza pictures, the AI knows not only how to identify individual toppings, but how to distinguish their layers and the order in which they need to appear. From there, the system can create step-by-step guides for making pizza using only one example photo as the starting point. The result is a system that isn't perfect (it's better at ordering synethetic pizza images than real ones), but it's still reasonably accurate. The scientists found that PizzaGAN could determine the right order 88 percent of the time, albeit using pizzas with just two toppings.

Modern Natural Language Processing course is designed for anyone who wants to grow or start a new career and gain a strong background in NLP. Nowadays, the industry is becoming more and more in need of NLP solutions. Chatbots and online automation, language modeling, event extraction, fraud detection on huge contracts are only a few examples of what is demanded today. Learning NLP is key to bring real solutions to the present and future needs. Throughout this course, we will leverage the huge amount of speech and text data available online, and we will explore the main 3 and most powerful NLP applications, that will give you the power to successfully approach any real-world challenge.

It highlighted the Sultanate's infrastructure readiness for Artificial Intelligence (AI) as an embodiment of Oman's Vision 2040, and the digital transformation strategy announced by the Ministry of Transport, Communications and Information Technology, which aim at promoting and accelerating the digital transformation of the government sectors and services in Oman in line with the global technical development. Above 30 ministries, authorities and establishments participated in the workshop. The workshop targeting all government entities came within the framework of ODP's national efforts that aim at contributing to accelerating the Sultanate digital transformation, through utilising the Artificial Intelligence applications (AI) and the cost-effective and efficient support available through Oman Data Park's Nebula AI infrastructure which is powered by Nvidia and hosted in Oman. The delivered content of the workshop included an AI transformation roadmap for government entities, their cyber security solutions, a framework for creating an AI project within the public sector and highlighted the action of the national programme for adopting Artificial Intelligence and Advanced Technologies in the Sultanate. The holding of the workshop comes in light of the Sultanate's interest in accelerating the adoption of Artificial Intelligence, while the Sultanate has designated a National Programme for Artificial Intelligence and Advanced Technologies in the year 2020, within the Ministry of Transport, Communications, and Information Technology (MTCIT).

One of the co-founders, Martin Nisser, a PhD student from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), explains the digital literacy and self-efficacy focused objectives: "Some of the women haven't had the opportunity to work with a computer for 25 years, and aren't yet accustomed to using the internet. We're working with them to build their capabilities with these modern tools in order to prepare them for life outside," says Nisser. Even for the students who became incarcerated more recently, it can be difficult to keep up with the fast pace of technological advances, since technical programs in correctional facilities are few and far-between. This scarcity of preparatory programs undoubtedly contributes to high and rising recidivism rates: More often than not, those who are released from prison eventually return. While working at TEJI, Nisser had a fortuitous meeting with his two co-founders, Marisa Gaetz (a PhD student from MIT's Department of Mathematics) and Emily Harburg (co-founder of Brave Initiatives, a nonprofit that develops coding bootcamps for young women).

This is Supervised machine learning full course. It covers all basic concepts from Python, Pandas, Django, Ajax and Scikit Learn. The course start on Jupyter notebook where different operations will performed on data. The end goal of this course is to teach how to deploy machine learning model on Django Python web framework. Actually, that is the purpose of machine learning.

The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems so that they are capable of learning (and improving) continuously, leveraging data on one task to improve performance on another, and doing so in a computationally sustainable way. Performers on this program developed systems capable of performing a diverse range of functions, including autonomous driving, real-time strategy, and drone simulation. These systems featured a diverse range of characteristics (e.g., task structure, lifetime duration), and an immediate challenge faced by the program's testing and evaluation team was measuring system performance across these different settings. This document, developed in close collaboration with DARPA and the program performers, outlines a formalism for constructing and characterizing the performance of agents performing lifelong learning scenarios. In Section 2, we introduce the general form of a lifelong learning scenario.

Temporal sentence grounding in videos (TSGV), a.k.a., natural language video localization (NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that semantically corresponds to a language query from an untrimmed video. Connecting computer vision and natural language, TSGV has drawn significant attention from researchers in both communities. This survey attempts to provide a summary of fundamental concepts in TSGV and current research status, as well as future research directions. As the background, we present a common structure of functional components in TSGV, in a tutorial style: from feature extraction from raw video and language query, to answer prediction of the target moment. Then we review the techniques for multimodal understanding and interaction, which is the key focus of TSGV for effective alignment between the two modalities. We construct a taxonomy of TSGV techniques and elaborate methods in different categories with their strengths and weaknesses. Lastly, we discuss issues with the current TSGV research and share our insights about promising research directions.

Group or cluster structure on explanatory variables in machine learning problems is a very general phenomenon, which has attracted broad interest from practitioners and theoreticians alike. In this work we contribute an approach to learning under such group structure, that does not require prior information on the group identities. Our paradigm is motivated by the Laplacian geometry of an underlying network with a related community structure, and proceeds by directly incorporating this into a penalty that is effectively computed via a heat flow-based local network dynamics. In fact, we demonstrate a procedure to construct such a network based on the available data. Notably, we dispense with computationally intensive pre-processing involving clustering of variables, spectral or otherwise. Our technique is underpinned by rigorous theorems that guarantee its effective performance and provide bounds on its sample complexity. In particular, in a wide range of settings, it provably suffices to run the heat flow dynamics for time that is only logarithmic in the problem dimensions. We explore in detail the interfaces of our approach with key statistical physics models in network science, such as the Gaussian Free Field and the Stochastic Block Model. We validate our approach by successful applications to real-world data from a wide array of application domains, including computer science, genetics, climatology and economics. Our work raises the possibility of applying similar diffusion-based techniques to classical learning tasks, exploiting the interplay between geometric, dynamical and stochastic structures underlying the data.

Familiarity with basic statistics and mathematical notation is helpful. An Introduction to Statistical Learning is one of the best introductory textbooks on classical machine learning techniques such as linear regression. It was the first machine learning book I've bought and has given me a great foundation. The explanations are held on a high level, so you don't need advanced math skills. Every chapter comes with code examples and labs in R. It is a great book to work through cover-to-cover. Get "An Introduction to Statistical Learning" on Amazon