This paper presents an approach for automation of interpretable feature selection for Internet Of Things Analytics (IoTA) using machine learning (ML) techniques. Authors have conducted a survey over different people involved in different IoTA based application development tasks. The survey reveals that feature selection is the most time consuming and niche skill demanding part of the entire workflow. This paper shows how feature selection is successfully automated without sacrificing the decision making accuracy and thereby reducing the project completion time and cost of hiring expensive resources. Several pattern recognition principles and state of art (SoA) ML techniques are followed to design the overall approach for the proposed automation. Three data sets are considered to establish the proof-of-concept. Experimental results show that the proposed automation is able to reduce the time for feature selection to $2$ days instead of $4-6$ months which would have been required in absence of the automation. This reduction in time is achieved without any sacrifice in the accuracy of the decision making process. Proposed method is also compared against Multi Layer Perceptron (MLP) model as most of the state of the art works on IoTA uses MLP based Deep Learning. Moreover the feature selection method is compared against SoA feature reduction technique namely Principal Component Analysis (PCA) and its variants. The results obtained show that the proposed method is effective.

For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than one percent of our agent's interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any that have been previously learned from human feedback.

Many real-world tasks involve multiple agents with partial observability and limited communication. Learning is challenging in these settings due to local viewpoints of agents, which perceive the world as non-stationary due to concurrently-exploring teammates. Approaches that learn specialized policies for individual tasks face problems when applied to the real world: not only do agents have to learn and store distinct policies for each task, but in practice identities of tasks are often non-observable, making these approaches inapplicable. This paper formalizes and addresses the problem of multi-task multi-agent reinforcement learning under partial observability. We introduce a decentralized single-task learning approach that is robust to concurrent interactions of teammates, and present an approach for distilling single-task policies into a unified policy that performs well across multiple related tasks, without explicit provision of task identity.

The most difficult part of getting started with TensorFlow isn't deep learning, it's putting together hundreds of API calls into a cohesive model.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. There are many reasons why the mathematics of Machine Learning is important and I'll highlight some of them below: What Level of Maths Do You Need?

What are Convolutional Neural Networks and why are they important? Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. In Figure 1 above, a ConvNet is able to recognize scenes and the system is able to suggest relevant tags such as'bridge', 'railway' and'tennis' while Figure 2 shows an example of ConvNets being used for recognizing everyday objects, humans and animals. Lately, ConvNets have been effective in several Natural Language Processing tasks (such as sentence classification) as well. ConvNets, therefore, are an important tool for most machine learning practitioners today.

SO THE deal struck over patient data between the Royal Free Foundation NHS Trust and AI pioneer DeepMind "failed to comply with" the law. That's the long-awaited verdict of the regulator charged with upholding UK data protection rules. The Information Commissioner's Office (ICO) said the trust, part of the UK's national health service, erred in four ways. It did not examine the privacy implications of the agreement closely enough. It failed to tell patients about the deal or offer an opt-out before handing over their records.

As part of its continued push into the AI sector, Google has just revealed that it has purchased a new deep learning startup. The Indian-based Halli Labs are the latest addition to Google's Next Billion Users team, joining the world-leading tech company less than two months after the startup's first public appearance. The young company has described its mission statement at Google as "to help get more technology and information into more people's hands around the world." Halli announced the news itself in a brief post on Medium, and Caesar Sengupta, a VP at Google, confirmed the purchase shortly afterwards on Twitter. Welcome @Pankaj and the team at @halli_labs to Google.

I'm trying to investigate the ways in which FPGAs differ to GPUs for the purpose of deep learning. I understand this is a complex question and not necessarily easy to answer in one go, however what I'm looking for are the key differences between the two technologies for this domain. Ideally, it would be nice to see the benefits and cons of both technologies with regards to Deep Learning. In addition, from my understanding, and please correct me if I'm wrong, is that with FPGAs, those undertaking a DL project would need someone who is able to configure the FPGA according to the type of project they want through the use of languages such as Verilog or VDHL. Furthermore, if they want to change the type of DL project they want to do, they have to reconfigure the FPGA to follow suit.

The data for your sequence prediction problem probably needs to be scaled when training a neural network, such as a Long Short-Term Memory recurrent neural network. When a network is fit on unscaled data that has a range of values (e.g. In this tutorial, you will discover how to normalize and standardize your sequence prediction data and how to decide which to use for your input and output variables. How to Scale Data for Long Short-Term Memory Networks in Python Photo by Mathias Appel, some rights reserved. There are two types of scaling of your series that you may want to consider: normalization and standardization.