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Amazon announced today that it’s making its range of machine learning courses available to all developers signed up to its AWS platform for free. This program was previously available only to Amazon employees, but anyone can now take advantage of it at no charge by signing up to Amazon Web Services’ free plan. It includes 30 courses in total, with over 45 hours of course material, videos, and lab tests.

Sentiment analysis is widely applied in voice of the customer (VOC) applications. In this article, authors discuss NLP-based Sentiment Analysis based on machine learning (ML) and lexicon-based approaches using KNIME data analysis tools. Source: infoq.com

In deep reinforcement learning (RL), solving the Atari games Montezuma’s Revenge and Pitfall has been a grand challenge. These games represent a broad class of challenging, real-world problems called “hard-exploration problems,” where an agent has to learn complex tasks with very infrequent or deceptive feedback. The state-of-the-art algorithm on Montezuma’s Revenge gets an average score of 11,347, a max score of 17,500, and solved the first level at one point in one of ten tries.

Facebook AI Research (FAIR) and NYU School of Medicine’s Center for Advanced Imaging Innovation and Research (CAI²R) are sharing new open source tools and data as part of fastMRI, a joint research project to spur development of AI systems to speed MRI scans by up to 10x. Today’s releases include new AI models and baselines for this task(as described in our paper here). It also includes the first large-scale MRI data set of its kind, which can serve as a benchmark for future research.

Last Christmas, I went through the most frustrating experience as a consumer. I was doing some last minute holiday shopping and after standing in a long line, I finally reached the blessed register only to find out that my debit card was blocked. I could sense the old lady at the register judging me with her narrowed eyes. Feeling thoroughly embarrassed, I called my bank right away. To my horror, they told me that my savings account was hacked and thousands of dollars were already gone!

Radiology and DeepLearningDetecting pneumonia opacities from chest X-Ray images using deep learning. One day back in August, I was catching up with my best friend from high school who is now a radiology resident. One thing led to another, and we started talking about our interests in artificial intelligence and machine learning and its possible applications in radiology. A couple of months after our talk, I stumbled upon a Kaggle challenge hosted by the Radiological Society of North American (RSNA).

By now, all of you have probably followed deep learning research for quite a while. In 1998, LeCun et al. proposed the very simple MNIST data set of handwritten digits and showed with their LeNet-5 that we can achieve a high validation accuracy on it. The data sets subsequently proposed became more complex (e.g., ImageNet or Atari games), hence the models performing well on them became more sophisticated, i.e. complex, as well.

When I look back on the history of AWS and think about the launches that truly signify the fundamentally dynamic, on-demand nature of the cloud, two stand out in my memory: the launch of Amazon EC2 in 2006 and the concurrent launch of CloudWatch Metrics, Auto Scaling, and Elastic Load Balancing in 2009. The first launch provided access to compute power; the second made it possible to use that access to rapidly respond to changes in demand.

A decision tree is a flowchart-like structure in which each internal node represents a test on a feature (e.g. whether a coin flip comes up heads or tails), each leaf node represents a class label (decision taken after computing all features) and branches represent conjunctions of features that lead to those class labels. The paths from root to leaf represent classification rules. Below diagram illustrate the basic flow of decision tree for decision making with labels (Rain(Yes), No Rain(No)).

Uber has been one of the most active contributors to open source machine learning technologies in the last few years. While companies like Google or Facebook have focused their contributions in new deep learning stacks like TensorFlow, Caffe2 or PyTorch, the Uber engineering team has really focused on tools and best practices for building machine learning at scale in the real world. Technologies such as Michelangelo, Horovod, PyML, Pyro are some of examples of Uber’s contributions to the machine learning ecosystem.