Seldon Core is a machine learning platform that helps your data science team deploy models into production. It provides an open-source data science stack that runs within a Kubernetes Cluster. You can use Seldon to deploy machine learning and deep learning models into production on-premise or in the cloud (e.g. Seldon supports models built with TensorFlow, Keras, Vowpal Wabbit, XGBoost, Gensim and any other model-building tool -- it even supports models built with commercial tools and services where the model is exportable. Seldon is used by some of the world's most innovative organisations -- it's the perfect machine learning deployment platform for start-ups and can scale to meet the demands of large enterprises.

In the current artificial intelligence (AI) media storm, the deep learning hype is especially strong. Let's explore how these terms relate and why they're so hot right now. First, deep learning is not synonymous with AI or even machine learning. Artificial Intelligence is a broad field which aims to "automate cognitive processes." Machine learning is a subfield of AI that aims to automatically develop programs (called models) purely from exposure to training data.

"Artificial intelligence is revolutionising the car. Autonomous driving, zero-emission mobility and digital networking are virtually impossible without advances in AI and deep learning," said CEO of Volkswagen Herbert Diess.

It's a common nightmare for programmers to come in late to a project or organization and then have to make sense of a complex "spaghetti mess" of code created over the previous 10 years -- a technical debt that takes huge resources in time and money to clean up. Ten years of technical debt is an all-too common headache: Decades of debt were at the root of the Y2K COBOL nightmare. MySpace struggled famously for years with a crippling tower of technical debt. And today, both fast-growing startups and long-standing companies have to deal with legacy code on an ongoing basis in their engineering organizations and beyond. But none of this compares to the billions of years of "technical debt" in biology.

I always am seeking out arguments against my present beliefs (or models of reality). Gary Marcus has a new essay titled "Deep Learning: A Critical Appraisal" where he points out all the many flaws of Deep Learning. Marcus has a vested interest in seeing Deep Learning fail, after all, he wrote a book in 2001, which he still is very proud of, that disparaged the nascent Artificial Neural Network research back then. Marcus is very motivated to point out the lack of success of neural networks at every opportunity. His latest essay is one in his many attempts to claim higher understanding by criticism.

Today I'm pleased to announce the finalized details to an event I've been working on behind the scenes for quite some time: Keep reading to learn why you should attend PyImageConf. PyImageConf 2018 will take place on August 26-28th in San Francisco, CA at the Regency Hyatt. Figure 1: PyImageConf 2018 speakers include Adrian Rosebrock, François Chollet, Katherine Scott, Davis King, Satya Mallick, Joseph Howse, Adam Geitgey, Jeff Bass, and more. PyImageConf has put together the biggest names in computer vision, deep learning, and OpenCV education to give you the best possible live, hands-on training and lectures. Each speaker is respectively known for their writing, teaching, online courses, and contributions to open source projects. If this sounds like you, rest assured, this conference will be well worth your investment of time, finances, and travel.

In my earlier articles, I had discussed about about application of Big data for gathering Insights on green revolution and witnessed about a research work on supply chain management using big data analytics on agriculture. Incrementally, got an opportunity to implement data science methodology (a game theory approach) to make the results of SCM as an incentive compatible one. However, in this article I am trying to discuss about a large scale digital image processing obtained using time-series photographs of agricultural fields and sensor data for parameters, that should be done parallely with the help of Big Data Analytics such that the result of this work can facilitate SCM process exponentially. We are focusing on using deep learning and machine learning techniques for identifying patterns for making predictins and decision making on large-scale stored / near real-time data sets. By this, we can identify the crop type, quality, maturity period for harvesting, early identification of bugs and diseases, soil quality attributes, early identification of need for soil nourishments etc., on a larger farms.

The most powerful approach in AI, deep learning, is gaining a new capability: a sense of uncertainty. Researchers at Uber and Google are working on modifications to the two most popular deep-learning frameworks that will enable them to handle probability. This will provide a way for the smartest AI programs to measure their confidence in a prediction or a decision--essentially, to know when they should doubt themselves. Deep learning, which involves feeding example data to a large and powerful neural network, has been an enormous success over the past few years, enabling machines to recognize objects in images or transcribe speech almost perfectly. But it requires lots of training data and computing power, and it can be surprisingly brittle.

Deploying valuable, measurable, and scalable IoT initiatives is top of mind for fortune 500 businesses. Andrew will discuss where AI in Industrial IoT is heading, how to apply the significant advances in GPU deep learning to perform deep learning training, develop an end to end AI infrastructure for your training and inference needs as you deploy AI in industrial IoT industry, and implement AI software applications to increase users' productivity.

The most powerful approach in AI, deep learning, is gaining a new capability: a sense of uncertainty. Researchers at Uber and Google are working on modifications to the two most popular deep-learning frameworks that will enable them to handle probability. This will provide a way for the smartest AI programs to measure their confidence in a prediction or a decision--essentially, to know when they should doubt themselves. Deep learning, which involves feeding example data to a large and powerful neural network, has been an enormous success over the past few years, enabling machines to recognize objects in images or transcribe speech almost perfectly. But it requires lots of training data and computing power, and it can be surprisingly brittle.