Transfer learning is becoming the de facto solution for vision and text encoders in the front-end processing of machine learning solutions. Utilizing vast amounts of knowledge in pre-trained models and subsequent fine-tuning allows achieving better performance in domains where labeled data is limited. In this paper, we analyze the efficiency of transfer learning in visual reasoning by introducing a new model (SAMNet) and testing it on two datasets: COG and CLEVR. Our new model achieves state-of-the-art accuracy on COG and shows significantly better generalization capabilities compared to the baseline. We also formalize a taxonomy of transfer learning for visual reasoning around three axes: feature, temporal, and reasoning transfer. Based on extensive experimentation of transfer learning on each of the two datasets, we show the performance of the new model along each axis.

Contextualized representations trained over large raw text data have given remarkable improvements for NLP tasks including question answering and reading comprehension. There have been works showing that syntactic, semantic and word sense knowledge are contained in such representations, which explains why they benefit such tasks. However, relatively little work has been done investigating commonsense knowledge contained in contextualized representations, which is crucial for human question answering and reading comprehension. We study the commonsense ability of GPT, BERT, XLNet, and RoBERTa by testing them on seven challenging benchmarks, finding that language modeling and its variants are effective objectives for promoting models' commonsense ability while bidirectional context and larger training set are bonuses. We additionally find that current models do poorly on tasks require more necessary inference steps. Finally, we test the robustness of models by making dual test cases, which are correlated so that the correct prediction of one sample should lead to correct prediction of the other. Interestingly, the models show confusion on these test cases, which suggests that they learn commonsense at the surface rather than the deep level. We release a test set, named CA Ts publicly, for future research. Introduction Contextualized representations trained over large-scale text data have given remarkable improvements to a wide range of NLP tasks, including natural language inference (Bowman et al. 2015), question answering (Rajpurkar, Jia, and Liang 2018) and reading comprehension (Lai et al. 2017). Giving new state-of-the-art results that approach or surpass human performance on several benchmark datasets, it is an interesting question what types of knowledge are learned in pre-trained contextualized representations in order to better understand how they benefit the NLP problems above. Intuitively, such knowledge is at least as useful as semantic and syntactic knowledge in natural language inference, reading comprehension and coreference resolution.

Autonomous driving promises to transform road transport. Multi-vehicle and multi-lane scenarios, however, present unique challenges due to constrained navigation and unpredictable vehicle interactions. Learning-based methods---such as deep reinforcement learning---are emerging as a promising approach to automatically design intelligent driving policies that can cope with these challenges. Yet, the process of safely learning multi-vehicle driving behaviours is hard: while collisions---and their near-avoidance---are essential to the learning process, directly executing immature policies on autonomous vehicles raises considerable safety concerns. In this article, we present a safe and efficient framework that enables the learning of driving policies for autonomous vehicles operating in a shared workspace, where the absence of collisions cannot be guaranteed. Key to our learning procedure is a sim2real approach that uses real-world online policy adaptation in a mixed-reality setup, where other vehicles and static obstacles exist in the virtual domain. This allows us to perform safe learning by simulating (and learning from) collisions between the learning agent(s) and other objects in virtual reality. Our results demonstrate that, after only a few runs in mixed-reality, collisions are significantly reduced.

Learning a new language can be difficult, especially if you approach it the wrong way. Instead of cracking open a textbook or settling in for stuffy video lessons, why not gamify your learning experience? There are several apps that offer to teach you a new language, but few offer speech recognition software and a brand new augmented reality (AR) feature to help drive the lessons home. Much like the supplementary language learning app Memrise, this new app offers a simple and fun way to learn over 33 languages. Tap or click here to learn about Memrise.

The first book is posted on data science central here, and the community group is here. Please join the community so you can also access the other'In a weekend' books It is also associated with a diverse range of people including Golf (Ben Hogan), Shaolin Monks, Benjamin Franklin etc. This means we don't need any installation (it's completely web-based) We will guide you through two end-to-end machine learning problems that can be taken over one weekend. We will introduce you to important machine learning concepts, such as machine learning workflow, defining the problem statement, pre-processing and understanding our data, building baseline and more sophisticated models, and evaluating models. We will also introduce to keep machine learning libraries in python and demonstrate code that can be used on your own problems.

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you'll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.

'This is a very timely, comprehensive and well written book in what is now one of the most dynamic and impactful areas of modern applied mathematics. Data science is rapidly taking center stage in our society. The subject cannot be ignored, either by domain scientists or by researchers in applied mathematics who intend to develop algorithms that the community will use. The book by Brunton and Kutz is an excellent text for a beginning graduate student, or even for a more advanced researcher interested in this field. The main theme seems to be applied optimization.

At QCon SF, Daniel Situnayake presented "Machine learning on mobile and edge devices with TensorFlow Lite". TensorFlow Lite is a production-ready, cross-platform framework for deploying ML on mobile devices and embedded systems, and was the main topic of the presentation. The key takeaways from it included understanding and getting started with TensorFlow Lite, and on-device machine learning on various devices – specifically microcontrollers and optimizing the performance of machine learning models. Situnayake, developer advocate for TensorFlow Lite at Google, began the presentation by explaining what machine learning is. Traditionally a developer feeds rules and data into an application and output answers, while with machine learning the developer or data scientists' feeds in the answers and data and the output are rules, which can be applied in the future.

Like a lot of working parents, when I'm walking my daughters to school or listening to them recount their days at the dinner table, one question is often on my mind: What should I be doing to prepare them for the world they'll enter as adults? When my daughters and their peers enter the workforce in 10 years, the global economy will be even more competitive, automated and technology-driven than it is today. Computing will be faster and cheaper. Artificial intelligence will be even more powerful, complemented by sensors everywhere in our environments--making it impossible to distinguish between "online" and "offline." Our greatest challenges, from climate change to economic inequality to privacy, will be even more acute.

A linguistics company is using AI to shorten the time it takes to learn a new language. It takes about 200 hours, using traditional methods, to gain basic proficiency in a new language. This AI-powered platform claims it can teach from beginner to fluency in just a few months – through once-daily 20 minute lessons. Learning a new language is hard. Some people seem to pick up new dialects with ease, but for the rest of us it's a trudge through rote memorization.