Although there has been remarkable progress and impressive performance on reinforcement learning (RL) on Atari games, there are many problems with challenging characteristics that have not yet been explored in Deep Learning for RL. These include reward sparsity, abrupt context-dependent reversals of strategy and time-sensitive game play. In this paper, we present Space Fortress, a game that incorporates all these characteristics and experimentally show that the presence of any of these renders state of the art Deep RL algorithms incapable of learning. Then, we present our enhancements to an existing algorithm and show big performance increases through each enhancement through an ablation study. We discuss how each of these enhancements was able to help and also argue that appropriate transfer learning boosts performance.

GitHub and Reddit are two of the most popular platforms when it comes to data science and machine learning. The former is an awesome tool for sharing and collaborating on codes and projects while the latter is the best platform out there for engaging with data science enthusiasts from around the world. This year, we have covered the top GitHub repositories each month and from this month onwards, we will be including the top Reddit threads as well that generated the most interesting and intriguing discussions in the machine learning space. April saw some amazing python libraries being open sourced. From Deep Painterly Harmonization, a library that makes manipulated images look ultra realistic, to Swift for TensorFlow, this article covers the best from last month.

The Linux Foundation launched a Deep Learning Foundation to support as well as sustain open source innovation in regards to artificial intelligence, deep learning, and machine learning. This Organization aims at making these critical new-technologies all available to the developer as well as data scientists worldwide. LF Deep Learning Foundation is comprised of many members, they include; Amdocs, B. Yond, Tech Mahindra, AT & T, Huawei, Nokia, Univa, ZTE, Tencent among others. Their main target is to create a neutral space to enable makers and sustainers of tools as well as infrastructure to interact and coordinate their efforts so as to facilitate the broad adaption of deep-learning technologies. LF has launched the Acumos Al Project through Deep Learning Foundation to provide a platform for the discovery, development, and sharing of AI workflows and AI models.

This certification specialization is helpful to break into the world of AI. Deep Learning is the most after skill in the technology world now. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing.

What is artificial intelligence, machine learning, and deep learning mean in general? When is a rule-based approach the right solution and when do you need machine learning? What does machine learning mean for time-series data? What is the difference between supervised and unsupervised learning in this area? Thanks to Devoxx for giving us permission to post this talk.

Developer conference season is in full flow, with Facebook's F8 last week and Microsoft Build, Google I/O and Amazon Web Services holding a London Summit this week, and they all had one thing in common: the goal of making AI technology more accessible for developers on their platforms. The big three cloud vendors made a slew of announcements this week, although AWS holds back most of its good stuff for re:Invent in Las Vegas later on in the year, all centring on machine learning and AI technologies. The idea is that by simplifying complex and powerful AI technology like computer vision, natural language understanding and deep learning models, these tech giants can lock developers into their ecosystem and milk them for cash as they consume infrastructure and services. So what did each of these companies say regarding AI this week? CTO Werner Vogels said: "Our mission is to make machine learning available and put it into the hands of every developer."

Nvidia this week unveiled its newest AI breakthrough in the form of a mind-blowing computer vision technique that can'inpaint' parts of an image that have been deleted or modified. If you're thinking Photoshop already does this, think again. This is something you have to see to believe. Nvidia's researchers explain the difference between its novel method for inpainting images with deep learning and currently existing tech in a whitepaper published earlier this week: As you can see in the above video, Nvidia's technology doesn't suffer from the same problem as current market techniques for filling in missing spaces in images. There's no granular degradation or blurred edges which require fiddling with different brushes and levels of smoothness or opacity.

To learn more about how to build next-generation machine learning applications, check out the session "Building reinforcement learning applications with Ray" at the AI Conference in San Francisco, September 4-7, 2018. Hurry--best price ends June 8. We are in an empirical era for machine learning, and it's important to be able to identify tools that enable efficient experimentation with end-to-end machine learning pipelines. Organizations that are using and deploying machine learning are confronted with a plethora of options for training models and model inference, at the edge and on cloud services. To that end,MLPerf, a new set of benchmarks compiled by a growing list of industry and academic contributors,was recently announced at the recent Artificial Intelligence conference in NYC.

There has been a revolution brewing in the technology industry. An artificial intelligence (AI) method called deep learning that uses deep or multi-layer neural networks is dramatically improving computer vision and video analytics. So much so that deep learning-based computer vision can now beat human capability in rapidly identifying objects in images. With video technology all around us -- it's this deep learning that can help us process vast amount of data that has been too great to humans alone to process. Now, we at IBM are taking this technology to the next step.

The panelists are as follows: 1. Dr. Baber Ghauri •Physician Executive and Healthcare Innovator, Trinity Health 2. Dr. Esther Yu • Neuroradiologist, UCSF 3. Dr. Pratik Mukherjee • Professor of Radiology and Biomedical Imaging, Bioengineering and Therapeutic Sciences, UCSF There will be time towards the end of the panel for audience members to ask questions. The panel will be moderated by Prashant Natarajan, Senior Director of AI Applications at H2O.ai. Hope to see you there! - - Agenda 5:00 pm - 5:30pm: Live Dosa Bar 5:30pm - 7:00pm: AI in Healthcare Panel 7:00pm - 7:15pm: Audience Q&A