As one of the key members on the Demand Intelligence team that is part of AI group, you'll take ownership of the design and implementation of Course Hero's consumer-facing search. The key objective of the AI Group is to build a Semantic Knowledge Graph at Course Hero in order to help personalize bespoke learning experiences for students and help educators create unique course content. The charter for the Demand Intelligence team is to focus on delivering value to students and educators by enabling search intelligence and demand-side data products. This team will be building a Semantic Search engine using NLP and ML driven search mechanisms for the long-tail distribution and consumption of our document corpus. This also includes building user cohorts, an inference engine to infer intent, behavior, usage, trends, and economic demand curves powering content discovery and recommendations, pricing, and other applied data science initiatives.

Make big bets: In our Oxford University Course on Data Science for IoT, Jakub Langr covers GANs. Jakub has one of the best books on GANs I have seen. When I had lunch with Jakub in London – he mentioned something interesting. Three years ago .. (that's an age in AI!) he saw the potential of GANs and decided to bet his career on it by reading every paper, article etc on GANs. Three years later – he has a book on GANs and due to it he teaches at Oxford on GANs.

These questions can make you think THRICE! Machine learning and data science are being looked as the drivers of the next industrial revolution happening in the world today. This also means that there are numerous exciting startups looking for data scientists. What could be a better start for your aspiring career! However, still, getting into these roles is not easy. You obviously need to get excited about the idea, team and the vision of the company. You might also find some real difficult techincal questions on your way. The set of questions asked depend on what does the startup do. Do they build ML products? You should always find this out prior to beginning your interview preparation. To help you prepare for your next interview, I've prepared a list of 40 plausible & tricky questions which are likely to come across your way in interviews. If you can answer and understand these question, rest assured, you will give a tough fight in your job interview. Note: A key to answer these questions is to have concrete practical understanding on ML and related statistical concepts. You can get that know-how in our course'Introduction to Data Science'!

A movie montage for modern artificial intelligence might show a computer playing millions of games of chess or Go against itself to learn how to win. Now, researchers are exploring how the reinforcement learning technique that helped DeepMind's AlphaZero conquer the chess and Go could tackle an even more complex task--training a robotic knee to help amputees walk smoothly. This new application of AI based on reinforcement learning--an automated version of classic trial-and-error--has shown promise in small clinical experiments involving one able-bodied person and one amputee whose leg was cut off above the knee. Normally, human technicians spend hours working with amputees to manually adjust robotic limbs to work well with each person's style of walking. By comparison, the reinforcement learning technique automatically tuned a robotic knee, enabling the prosthetic wearers to walk smoothly on level ground within 10 minutes.

Apple's new AirPods might have been accidentally confirmed – but its own iPhone update. The latest beta version of the iOS software appears to not only suggest that new versions of the wireless earphones are coming soon, but also what one of their main features might be. The leak comes from a hidden part of the iOS 12.2 update which is currently available for developers. Inside a pop-up that is not visible without digging around is a suggestion about how those nearphones might work. While a user is setting up the option to speak to the phone's digital assistant just by saying "Hey, Siri", the message suggests that people will be able to do so just by speaking into their AirPods, rather than into their phone.

For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.

Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approaches are limited in handling diverse domains with various number of labels. The meta-learning approaches train a meta learner to predict weights of homogeneous-structured task-specific networks, requiring a uniform number of classes across tasks. The metric-learning approaches learn one task-invariant metric for all the tasks, and they fail if the tasks diverge. We propose to deal with these limitations with meta metric learning. Our meta metric learning approach consists of task-specific learners, that exploit metric learning to handle flexible labels, and a meta learner, that discovers good parameters and gradient decent to specify the metrics in task-specific learners. Thus the proposed model is able to handle unbalanced classes as well as to generate task-specific metrics. We test our approach in the `$k$-shot $N$-way' few-shot learning setting used in previous work and new realistic few-shot setting with diverse multi-domain tasks and flexible label numbers. Experiments show that our approach attains superior performances in both settings.

As online systems based on machine learning are offered to public or paid subscribers via application programming interfaces (APIs), they become vulnerable to frequent exploits and attacks. This paper studies adversarial machine learning in the practical case when there are rate limitations on API calls. The adversary launches an exploratory (inference) attack by querying the API of an online machine learning system (in particular, a classifier) with input data samples, collecting returned labels to build up the training data, and training an adversarial classifier that is functionally equivalent and statistically close to the target classifier. The exploratory attack with limited training data is shown to fail to reliably infer the target classifier of a real text classifier API that is available online to the public. In return, a generative adversarial network (GAN) based on deep learning is built to generate synthetic training data from a limited number of real training data samples, thereby extending the training data and improving the performance of the inferred classifier. The exploratory attack provides the basis to launch the causative attack (that aims to poison the training process) and evasion attack (that aims to fool the classifier into making wrong decisions) by selecting training and test data samples, respectively, based on the confidence scores obtained from the inferred classifier. These stealth attacks with small footprint (using a small number of API calls) make adversarial machine learning practical under the realistic case with limited training data available to the adversary.

Is there any truth to the notion that college instructors who implement active learning receive lower teaching evaluations? Henderson et al. present data from college physics instructors who attended a new-faculty workshop and attempted to incorporate active learning into their introductory course. Contrary to common belief, 48% of these instructors reported an increase in student evaluations, 32% reported no change, and only 20% reported a decrease in their evaluations. The authors acknowledge the limitations of the study, including the nature of self-reported data as well as changes in student evaluations over time, yet provide the overall recommendation that instructors (and institutions) should not let perceived anxiety over negative student evaluations be a reason to avoid implementing evidence-based teaching practices.

This tutorial explains matplotlib's way of making plots in simplified parts so you gain the knowledge and a clear understanding of how to build and modify full featured matplotlib plots. Matplotlib is the most popular plotting library in python. Using matplotlib, you can create pretty much any type of plot. However, as your plots get more complex, the learning curve can get steeper. The goal of this tutorial is to make you understand'how plotting with matplotlib works' and make you comfortable to build full-featured plots with matplotlib. The following piece of code is found in pretty much any python code that has matplotlib plots.