Sentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed by its input sentence. In this work, we first present a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model, and then improve its entailment and paraphrasing capabilities via multi-task learning with related auxiliary tasks of entailment and paraphrase generation. Moreover, we propose a novel 'multi-level' layered soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the sentence simplification model, depending on the task's semantic versus lexico-syntactic nature. We also introduce a novel multi-armed bandit based training approach that dynamically learns how to effectively switch across tasks during multi-task learning. Experiments on multiple popular datasets demonstrate that our model outperforms competitive simplification systems in SARI and FKGL automatic metrics, and human evaluation. Further, we present several ablation analyses on alternative layer sharing methods, soft versus hard sharing, dynamic multi-armed bandit sampling approaches, and our model's learned entailment and paraphrasing skills.

Jeff Maggioncalda was recently named CEO of Coursera. I have interviewed both founders of the company, Andrew Ng and Daphne Koller, so I was curious about Maggioncalda's perspective on the company, education technology and the massive open online courses more generally, and his own background as an entrepreneur. Regarding the last point, Maggioncalda was previously the CEO of Financial Engines Inc, a company co-founded by economist and Nobel Prize winner William Sharpe and recently sold for $3 billion. During his 18 years as CEO of Financial Engines Inc, Maggioncalda had to pivot three times from his original idea before becoming a success. Financial Engines would go on to beocme the largest independent online retirement advice platform with more than $100b under management.

What Treasury and Finance professionals need to know to be competitive in the Fourth Industrial Revolution of AI, machine learning, robotic process automation (RPA), and emerging tech. We have a need for smarter and faster decision making. It stands to reason, then, that more data would create more informed decisions. Together, they are the what (data) and how (machine learning) of solving complex problems faster and smarter. While the term is certainly in market, most current technology solutions are machine learning, a subset of AI.

This blog post survey the attacks techniques that target AI (artificial intelligence) systems and how to protect against them. This post explores each of these classes of attack in turn, providing concrete examples and discussing potential mitigation techniques. This post is the fourth, and last, post in a series of four dedicated to providing a concise overview of how to use AI to build robust anti-abuse protections. The first post explained why AI is key to building robust protection that meets user expectations and increasingly sophisticated attacks. Following the natural progression of building and launching an AI-based defense system, the second post covered the challenges related to training classifiers. The third one looked at the main difficulties faced when using a classifier in production to block attacks.

Above two are intro course to deep learning. This is a Youtube channel which contains courses by Prof. Andrew Ng on various topics in deep learning. This a 14 week course, taught by Jeremy Howard. It cover most of the topics in deep learning. This course is a gentle introduction to Reinforcement Learning. It walks you through most of the topics in Reinforcement Learning in high level. This course is not taught at a basic level, so you need to be familiar with basic concepts and perhaps a little more. For more stories follow AiMantra.

Are you looking for game design and development courses? Here is the list of best game development courses, tutorials, training and certification for the individuals interested in becoming a game developer, game designer, game artist or a game programmer. Do you want to learn how to develop games? Then these Game Development Online Courses will show you the right path to get started. Building games is an innovative and technical art form.

Artificial Intelligence or Machine Learning, as it's better known as, is something that has garnered my interest over recent years. I even used the technology to design a company logo for a side hustle project. The expanding uses for Artificial Intelligence in business is fascinating and only becomes more exciting to see what is in store as the technology continues to mature. After being in the business for 25 years I have learnt and experienced first-hand the ever-changing landscape in which marketing continues to grow. We have seen a huge change in the industry over the last ten years, with the most prevalent and revolutionary change being the utter dependence of the online sphere. I have grown my business from strength to strength in the online realm, with platforms such as Twitter being a core tool.

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This course is focused on the question: How do we do matrix computations with acceptable speed and acceptable accuracy? This course was taught in the University of San Francisco's Masters of Science in Analytics program, summer 2017 (for graduate students studying to become data scientists). The course is taught in Python with Jupyter Notebooks, using libraries such as Scikit-Learn and Numpy for most lessons, as well as Numba (a library that compiles Python to C for faster performance) and PyTorch (an alternative to Numpy for the GPU) in a few lessons. Accompanying the notebooks is a playlist of lecture videos, available on YouTube. If you are ever confused by a lecture or it goes too quickly, check out the beginning of the next video, where I review concepts from the previous lecture, often explaining things from a new perspective or with different illustrations, and answer questions.

Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks.