Cognex CGNX recently announced the acquisition of Seoul-based SUALAB, a developer of deep learning-based vision software. Although the financial terms of the acquisition have been kept under wraps, per a Pulse article the transaction price is estimated to be $168.6 million. Deep learning allows Cognex to solve the most complex vision application operations in factories faster, easier and in a cost-effective manner. The addition of SUALAB's Intellectual property and highly skillful engineering team, which specializes in deep learning, is expected to strengthen the company's product portfolio. The latest acquisition will help Cognex to reap benefits from strong prospects of the global deep learning system software market.

In my previous article Introduction to Artificial Neural Networks(ANN), we learned about various concepts related to ANN so I would recommend going through it before moving forward because here I'll be focusing on the implementation part only. In this article series, we are going to build ANN from scratch using only the numpy Python library. In this part-1, we will build a fairly easy ANN with just having 1 input layer and 1 output layer and no hidden layer. In part-2, we will build ANN with 1 input layer, 1 hidden layer, and 1 output layer. Well, there are many deep learning libraries(Keras, TensorFlow, PyTorch etc) that can be used to create a neural network in a few lines of code.

As IT organizations grow, so does the size of their codebases and the complexity of their ever-changing developer toolchain. Engineering leaders have very limited visibility into the state of their codebases, software development processes, and teams. By applying modern data science and machine learning techniques to software development, large enterprises have the opportunity to significantly improve their software delivery performance and engineering effectiveness. In the last few years, a number of large companies such as Google, Microsoft, Facebook and smaller companies such as Jetbrains and source{d} have been collaborating with academic researchers to lay the foundation for Machine Learning on Code. Machine Learning on Code (MLonCode) is a new interdisciplinary field of research related to Natural Language Processing, Programming Language Structure, and Social and History analysis such contributions graphs and commit time series.

Artificial intelligence (AI) is an exciting technology that seems to be seeing fresh breakthroughs every other day. While starting a career in AI will enable you to spend your days working with some of the most talented developers around for a great salary, it can also be highly competitive and involve a lot of behind-the-scenes work keeping yourself updated on the latest artificial intelligence software. However, the effort you put into keeping yourself ahead of the curve is likely to pay off in the form of a lucrative salary. As a machine learning engineer, you can expect to earn from $125,000 to $175,000, while some of the biggest tech companies out there are attracting talented young graduates with offers that range between $300,000 and $500,000. To put it simply, tech firms are proactively searching for top AI talent and ready to pay big money for the best candidates.

There were 2 final examinations, one being non-proctored and the other proctored. The non-proctored exam was open book, and tested your ability to look at data and the various analytical techniques, and interpret the results of the analyses. The proctored final exam was closed book and covered general concepts. This was a great overview of some of the more important topics in machine learning. I was able to get a good theoretical background in these topics, and learned the coding necessary to perform these. This is a great foundation upon which to add more advanced and in-depth use of these techniques. This course really challenged me to rethink what analytical techniques I should be learning and applying in the future, to the point that I am going to change my specialization to Artificial Intelligence and Deep Learning.

I have come across a few great resources that I wanted to share. For students taking a machine learning class (like Northwestern University's MSDS 422 Practical Machine Learning) these are great references, and a way to learn about them before, during, or after the class. This is not a comprehensive list, just a starter. There is a free online textbook, Neural Networks and Deep Learning. There is a great math visualization site called 3Blue1Brown and they have a YouTube channel.

It first introduces an example using Flask to set up an endpoint with Python, and then shows some of issues to work around when building a Keras endpoint for predictions with Flask.

Artificial intelligence (AI), an umbrella term that encompasses machine learning (ML) and deep learning (DL) technologies, promises to transform every facet of human life -- including live streaming. For tech giants like Facebook, artificial intelligence has already become commonplace. The site uses it to automatically identify users' faces each time a photo is uploaded. And as unsettling as it may sound, Facebook's facial recognition software (called DeepFace) is far more accurate than the FBI's. Because Facebook's extensive photo database grows each day, and as a result, the algorithm improves.

DeepMind says this latest iteration of AlphaStar -- AlphaStar Final -- can play a full StarCraft 2 match under "professionally approved" conditions, importantly with limits on the frequency of its actions and by viewing the world through a game camera. It plays on the official StarCraft 2 Battle.net "StarCraft has been a grand challenge for AI researchers for over 15 years, so it's hugely exciting to see this work recognized in Nature," said DeepMind cofounder and CEO Demis Hassabis. "These impressive results mark an important step forward in our mission to create intelligent systems that will accelerate scientific discovery." DeepMind's forays into competitive StarCraft play can be traced back to 2017, when the company worked with Blizzard to release an open source tool set containing anonymized match replays.

Slyce was born from a simple vision: What if you could find and buy any product you see, using the technology of visual search on a photograph? Of course, the task of visual product search is born of a more ambitious challenge: Given a query image, how do you determine the most relevant images from a universe of images and presenting them in decreasing order of relevancy? In the case of a traditional search engine, for example, the universe would be all images that are available on the internet. For a social network, it would be all images that were posted or pinned on its website (or app). In our case we elected to focus on solving visual search for retailers, narrowing the universe to be the entire catalog of products that a given retailer sells.