At Ibotta we train a lot of machine learning models. They make predictions for millions of users as they interact with our mobile app. While we do much of our data processing with Spark, our preferred machine learning framework is scikit-learn. As compute gets cheaper and time to market for machine learning solutions becomes more critical, we've explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution.

ELLIS is a not-for-profit organization whose goal is to establish a network of European research labs on Machine Learning and Intelligent Systems. ELLIS' aspiration is to become the leading open science AI organization in the world. The ELLIS sites are key towards reaching this goal as they will provide unprecedented flexibility and financial support to a selected group of researchers in machine learning - and related fields - to spark economic and societal innovation. The long-term vision is to build a joint European laboratory similar to the European Molecular Biology Lab (EMBL) as described in the original open letter (https://ellis.eu/letter). ELLIS sites are a key element towards achieving this goal as they will implement as much of the ELLIS vision as possible.

Do you want to make an impact? We are driving the adoption of robotic process automation technology in leading Fortune 1000 and other companies across more than 90 countries. At Automation Anywhere, we are passionate in our belief that software bots can free people to create, think, discover, and ultimately build great companies. With our Digital Workforce platform, we are contributing to build a Digital Workforce, 3M strong, by 2020. Our platform includes Robotic Process Automation, cognitive technologies and analytics and is adopted by over 1,400 of the world's leading enterprises and many are calling it one of the most essential and disruptive technologies in the market today.

Five years ago, Hyderabad resident Tulasi Mathi was forced to quit her job as a maths teacher due to health issues and the birth of her two children. But today, the 29-year-old does data labelling and makes up to Rs 15,000 a month. The money isn't much but it's more than she made as a teacher, and enough to pay her kids' school fees and her own expenses.She chanced on data labelling work through a YouTube video in 2017. Today, she scans videos and marks and labels objects encountered by self-driving cars. Her output is used to train artificial intelligence algorithms powering such cars. All Mathi knows is that it makes her life easier.

Understand the concepts and intuition of Machine Learning algorithms Build Machine Learning models within minutes Choose the correct Machine Learning Algorithm using the cheatsheet Deploy production grade Machine Learning algorithms Deploy Machine Learning webservices in the simplest form possible including excel Bring in great value to business you manage Basic Math is good enough. This course does not require background in Data Science. Will be great if you have one. Free or paid subscription to Microsoft Azure is required. It may ask for Phone and/or Credit Card for verification Machine Learning is one of the hottest and top paying skills.

Producing content for Massive Open Online Course (MOOC) platforms like Coursera and EdX might be academically rewarding (and potentially lucrative), but it's time-consuming -- particularly where videos are involved. Professional-level lecture clips require not only a veritable studio's worth of equipment, but significant resources to transfer, edit, and upload footage of each lesson. That's why research scientists formerly at Udacity, an online learning platform with over 150 courses, are investigating a machine learning framework that automatically generates lecture videos from audio narration alone. They claim in a preprint paper ("LumièreNet: Lecture Video Synthesis from Audio") on Arxiv.org that their AI system -- LumièreNet -- can synthesize footage of any length by directly mapping between audio and corresponding visuals. "In current video production pipeline, an AI machinery which semi (or fully) automates lecture video production at scale would be highly valuable to enable agile video content development (rather than reshooting each new video)," wrote the paper's coauthors.

BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). BERT is pre-trained on two auxiliary tasks: Masked Language Model and Next Sentence Prediction. In this paper we introduce a new pre-training task inspired by reading comprehension and an effort to avoid encoding general knowledge in the transformer network itself. We find significant and consistent improvements over both BERT-BASE and BERT-LARGE on multiple reading comprehension (MRC) and paraphrasing datasets. Specifically, our proposed model has strong empirical evidence as it obtains SOTA results on Natural Questions, a new benchmark MRC dataset, outperforming BERT-LARGE by 3 F1 points on short answer prediction. We also establish a new SOTA in HotpotQA, improving answer prediction F1 by 4 F1 points and supporting fact prediction by 1 F1 point. Moreover, we show that our pre-training approach is particularly effective when training data is limited, improving the learning curve by a large amount.

We propose self-teaching networks to improve the generalization capacity of deep neural networks. The idea is to generate soft supervision labels using the output layer for training the lower layers of the network. During the network training, we seek an auxiliary loss that drives the lower layer to mimic the behavior of the output layer. The connection between the two network layers through the auxiliary loss can help the gradient flow, which works similar to the residual networks. Furthermore, the auxiliary loss also works as a regularizer, which improves the generalization capacity of the network. We evaluated the self-teaching network with deep recurrent neural networks on speech recognition tasks, where we trained the acoustic model using 30 thousand hours of data. We tested the acoustic model using data collected from 4 scenarios. We show that the self-teaching network can achieve consistent improvements and outperform existing methods such as label smoothing and confidence penalization.

-- In this paper we propose a deep neural network model with an encoder-decoder architecture that translates images of math formulas into their LaTeX markup sequences. The enc oder is a convolutional neural network (CNN) that transforms images into a group of feature maps. To better capture the spatia l relationships of math symbols, the feature maps are augmented with 2D positional encoding before being unfolded into a vector. The d ecoder is a stacked bidirectional long short-term memory (LSTM) model integrated with the soft attention mechanism, which works as a language model to translate the encoder output into a sequence of LaTeX tokens. The neural network is trained in two steps. The first step is token-level training using the Maximum-Like lihood Estimation (MLE) as the objective function. At comp letion of the token-level training, the sequence-level training objective function is employed to optimize the overall model based on the policy gradient algorithm from reinforcement learning. Our design a lso overcomes the exposure bias problem by closing the feedback l oop in the decoder during sequence-level training, i.e., feedi ng in the predicted token instead of the ground truth token at every time step. The model is trained and evaluated on the IM2LATEX-100K dataset and shows state-of-the-art performance on both sequence-based and image-based evaluation metrics. Math formulas often carry the most significant tech nical substances in many science, technology, engineering and math (STEM) fields. Being able to extract the math formulas from digital documents and translate them into markup la nguages is very useful for a wide range of information retriev al tasks. Portable Document Format (PDF) is the de facto standard publication format, which makes document distributi on very easy and reliable.

Non-Bayesian social learning theory provides a framework that models distributed inference for a group of agents interacting over a social network. In this framework, each agent iteratively forms and communicates beliefs about an unknown state of the world with their neighbors using a learning rule. Existing approaches assume agents have access to precise statistical models (in the form of likelihoods) for the state of the world. However in many situations, such models must be learned from finite data. We propose a social learning rule that takes into account uncertainty in the statistical models using second-order probabilities. Therefore, beliefs derived from uncertain models are sensitive to the amount of past evidence collected for each hypothesis. We characterize how well the hypotheses can be tested on a social network, as consistent or not with the state of the world. We explicitly show the dependency of the generated beliefs with respect to the amount of prior evidence. Moreover, as the amount of prior evidence goes to infinity, learning occurs and is consistent with traditional social learning theory.