Machine Learning is very powerful and many people are shifting their careers into the Machine learning field. The reason behind machine learning popularity is its power to make useless data into more meaningful data. Coursera has a wide range of Machine Learning courses. That's why I have listed the 10 Best Courses for Machine Learning on Coursera. So give your few minutes and find out Best Courses for Machine Learning on Coursera for you.

This article provides an organization of various kinds of biases that can occur in the AI pipeline starting from dataset creation and problem formulation to data analysis and evaluation. It highlights the challenges associated with the design of bias-mitigation strategies, and it outlines some best practices suggested by researchers. Finally, a set of guidelines is presented that could aid ML developers in identifying potential sources of bias, as well as avoiding the introduction of unwanted biases. The work is meant to serve as an educational resource for ML developers in handling and addressing issues related to bias in AI systems.

Carter shook her head and stared at the combined camera and microphone that surveyed the corridor. Lipcott's words seemed to float in front of her eyes. What she did next could determine not just Lipcott's future, but her own. She walked on to the corner, to a dead spot between cameras, took a deep breath, and mouthed, "Don't ask questions.

Computers have been able to quickly process 2D images for some time. Your cell phone can snap digital photographs and manipulate them in a number of ways. Much more difficult, however, is processing an image in three dimensions, and doing it in a timely manner. The mathematics are more complex, and crunching those numbers, even on a supercomputer, takes time. That's the challenge a group of scientists from the U.S. Department of Energy's (DOE) Argonne National Laboratory is working to overcome.

SqueezeNet provides a smart architecture that achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques, the authors were able to compress SqueezeNet to less than 0.5MB (510 smaller than AlexNet). What is achieved using these strategies? Intuition for the third strategy was that large activation maps (due to delayed downsampling) can lead to higher classification accuracy, with all else held equal. The building block of the SqueezeNet is the fire module.

Retailers around the country rely on a network of nearly 70 independent Coca-Cola bottlers to manufacture and ship cases of liquid refreshments. It's a finely tuned supply chain, and it ultimately serves customers well. But getting a consolidated view into the millions of paper-based billing and shipping documents was a major headache for the network–that is, until an innovative use of computer vision and NLP technology helped digitize it. Starting around 2007, Coca-Cola North America worked to "refranchise" its bottling and shipping operations, which spans 51 production facilities, 350 distribution centers, and involves more than 55,000 employees. The company says the goal of this refranchising effort is to "bring the heart of Coca-Cola back to the local bottler," which in some cases are multi-generational companies more than 100 year's old. A key player in all this is the Coca-Cola Bottler's Sales and Services Company.

Are you a highly motivated researcher with an outstanding track record in Mathematics and its Applications in Science and Engineering? We offer a position at the interface of Dynamics and Deep Learning in the Applied Analysis group of the SACS cluster within the Department of Applied Mathematics (AM) at the University of Twente (UT). The challenge: You will actively develop your mathematical profile and seek connections between fundamental mathematical theory of dynamical systems, nonlinear analysis and the rising area of deep learning for data-driven model discovery. Based on a long-standing expertise and tradition of dynamical systems theory at the UT, well embedded in the Dutch NDNS cluster, our department is looking for a mathematician with a proven expertise in the broad area of dynamical systems, nonlinear analysis or approximation theory for deep neural networks. You show great passion in applying your novel methods to computational neuroscience, inverse problems in imaging or engineering applications driven by physics-informed machine learning for example within the multi-disciplinary research contexts at the UT, like the Digital Society Institute, the Technical Medical Centre or the MESA Institute for Nanotechnology.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Even before they speak their first words, human babies develop mental models about objects and people. This is one of the key capabilities that allows us humans to learn to live socially and cooperate (or compete) with each other. But for artificial intelligence, even the most basic behavioral reasoning tasks remain a challenge. Advanced deep learning models can do complicated tasks such as detect people and objects in images, sometimes even better than humans.

According to Fujitsu, finding the optimal route to collect the space debris will save significant time and cost during mission planning. Researchers at the U.K.'s University of Glasgow, Fujitsu, and the satellite service and sustainability firm Astroscale together developed an artificial neural network (ANN)-based rapid trajectory design algorithm to address the removal of space debris. Powered by Fujitsu's Digital Annealer, the quantum-inspired system determines which debris will be collected and when, and plans the optimal route to carry out the mission to save time and money. The ANNs predicting the costs of such orbital transfers were developed with the Amazon Sagemaker toolset. Fujitsu's Ellen Devereux noted that the technology "has huge implications for optimization in space, not only when it comes to cleaning up debris, but also in-orbit servicing and more."

Today, in partnership with NVIDIA, Google Cloud announced Dataflow is bringing GPUs to the world of big data processing to unlock new possibilities. With Dataflow GPU, users can now leverage the power of NVIDIA GPUs in their machine learning inference workflows. Here we show you how to access these performance benefits with BERT. Google Cloud's Dataflow is a managed service for executing a wide variety of data processing patterns including both streaming and batch analytics. It has recently added GPU support can now accelerate machine learning inference workflows, which are running on Dataflow pipelines.