The idea of an artificial neuron is rather vague. In a CPU, a logic device is created using transistors – if the neural network was'fixed' or'hard-wired' this might also be possible, but generally the point of using a neural network is to use a'learning capability'. This means that a single neuron's response to inputs must be able to change as it learns. This is generally called the'weighting' of a neuron where it puts emphasis on different inputs to generate the desired output. This is more easily achieved in software than hardware, so neurons are usually a mathematical function to relate inputs to outputs.

When you post something on LinkedIn, chances are that an algorithm made by Rushi Bhatt's team in Bengaluru has checked if it's kosher to be on the professional network. It sounds easy but consider the complexity: LinkedIn has over 560 million members, 20 million companies, millions of job postings and it works in 24 different languages. If all its millions of users seamlessly post on the platform every day, it is because LinkedIn's algorithms, with a lot of help from humans, green-light them before the user can blink an eye. "We have to walk this fine line between freedom of expression and not letting poor content live on the site. That makes it really complicated for everybody, including humans," says Bhatt, an alum of Amazon and Yahoo with a Ph.D. in cognitive and neural systems from Boston University and degrees from the Tata Institute of Fundamental Research and what is today NIT, Surat. At its worst, a poor newsfeed can drive away users. On the other hand, a good one can keep you hooked on a platform for hours. At LinkedIn, it is the job of the "Feed AI" team to maintain fidelity. Bhatt's job is to literally keep the NSFW stuff away. It's a problem almost all major platforms with user-generated content – be it Youtube or Twitter – struggle with.

In silico modeling is a crucial milestone in modern drug design and development. Although computer-aided approaches in this field are well-studied, the application of deep learning methods in this research area is at the beginning. In this work, we present an original deep neural network (DNN) architecture named RANC (Reinforced Adversarial Neural Computer) for the de novo design of novel small-molecule organic structures based on the generative adversarial network (GAN) paradigm and reinforcement learning (RL). As a generator RANC uses a differentiable neural computer (DNC), a category of neural networks, with increased generation capabilities due to the addition of an explicit memory bank, which can mitigate common problems found in adversarial settings. The comparative results have shown that RANC trained on the SMILES string representation of the molecules outperforms its first DNN-based counterpart ORGANIC by several metrics relevant to drug discovery: the number of unique structures, passing medicinal chemistry filters (MCFs), Muegge criteria, and high QED scores.

In some of our articles in the past, we learned about different fundamentals of neural networks and deep learning. We saw how to create feed forward neural networks, CNN, and autoencoders using different libraries in R. In this article, we will start working on Keras in Python. This is a piece from my new book Hands-on Automated Machine Learning. We will build on this article and take baby steps to master Keras DL through a series of articles focused on deep learning in Python. Keras is a deep learning library, originally built on Python, that runs over TensorFlow or Theano.

What is a Recurrent Neural Network or RNN, how it works, where it can be used? This article tries to answer the above questions. It also shows a demo implementation of a RNN used for a specific purpose, but you would be able to generalise it for your needs. Python, CNN knowledge is required. CNN is required to compare why and where RNN performs better than CNN? No need to understand the math. If you want to check then go back to my earlier article to check what is a CNN.

When digital advertising company Laughlin Constable was approached by the Wisconsin State Cranberry Growers Association about a project to use deep learning to identify pests in cranberry bogs, the idea was met with skepticism. First of all, it's not the kind of project an advertising firm typically takes on. And it was also unclear if the growers had the technical foundation on which to build this kind of deep learning capability. But speaking at the Spark AI Summit in San Francisco, the company's vice president of engineering, Sam Saha, said he felt it was worth trying as a deep learning use case. "There are lots of businesses out there and they don't have the talent pool [to use AI]," he said.

Today NVIDIA made a number of announcements centered around Machine Learning software at the Computer Vision and Pattern Recognition Conference in Salt Lake City. "NVIDIA is kicking off the conference by demonstrating an early release of Apex, an open-source PyTorch extension that helps users maximize deep learning training performance on NVIDIA Volta GPUs. Inspired by state of the art mixed precision training in translational networks, sentiment analysis, and image classification, NVIDIA PyTorch developers have created tools bringing these methods to all levels of PyTorch users."

Microsoft is the latest tech giant to find itself in the crosshairs over a controversial government contract. In January, it was announced that Microsoft's Azure Government arm was working with U.S. Immigration and Customs Enforcement (ICE) to assist with'facial recognition and identification', providing access to its deep learning AI technology. Along with that, Microsoft said it was'proud to support' ICE - a section that was deleted and, ultimately, restored after it was made public, according to BuzzFeed. In January, it was announced that Microsoft's Azure Government arm was working with ICE to assist with'facial recognition and identification,' providing access to its deep learning AI tech That hasn't stopped many from calling on Microsoft employees to resign from the company or urge CEO Satya Nadella to speak about the firm's dealings with ICE. Microsoft has since responded that deleting the content was a'mistake' and issued a follow up blog post further detailing the matter. 'In response to questions we want to be clear: Microsoft is not working with U.S. Immigration and Customs Enforcement or U.S. Customs and Border Protection on any projects related to separating children from their families at the border, and contrary to some speculation, we are not aware of Azure or Azure services being used for this purpose,' the firm wrote in a statement.

Predictive analytics in data science rest on the shoulders of explanatory data analysis, which is precisely what we were discussing in our previous article – The What, Where and How of Data for Data Science. We talked about data in data science, and how business intelligence (BI) analysts use it to explain the past. In fact, everything is connected. Once the BI reports and dashboards have been prepared and insights – extracted from them – this information becomes the basis for predicting future values. And the accuracy of these predictions lies in the methods used.

State-of-the-art probabilistic planners typically apply look- ahead search and reasoning at each step to make a decision. While this approach can enable high-quality decisions, it can be computationally expensive for problems that require fast decision making. In this paper, we investigate the potential for deep learning to replace search by fast reactive policies. We focus on supervised learning of deep reactive policies for probabilistic planning problems described in RDDL. A key challenge is to explore the large design space of network architectures and training methods, which was critical to prior deep learning successes. We investigate a number of choices in this space and conduct experiments across a set of benchmark problems. Our results show that effective deep reactive policies can be learned for many benchmark problems and that leveraging the planning problem description to define the network structure can be beneficial.