For movie buffs, the work that the factory machines do in Charlie Chaplin's 1936 classic, Modern Times, may have seemed too futuristic for its time. Fast forward eight decades, and the colossal changes that Artificial Intelligence is catalyzing around us will most likely give the same impression to our future generations. There is one crucial difference though: while those advancements were in movies, what we are seeing today are real. A question that seems to be on everyone's mind is, What is Artificial Intelligence? The pace at which AI is moving, as well as the breadth and scope of the areas it encompasses, ensure that it is going to change our lives beyond the normal.
With evolving technologies, intelligent automation has become a top priority for many executives in 2020. Forrester predicts the industry will continue to grow from $250 million in 2016 to $12 billion in 2023. With more companies identifying and implementation the Artificial Intelligence (AI) and Machine Learning (ML), there is seen a gradual reshaping of the enterprise. Industries across the globe integrate AI and ML with businesses to enable swift changes to key processes like marketing, customer relationships and management, product development, production and distribution, quality check, order fulfilment, resource management, and much more. AI includes a wide range of technologies such as machine learning, deep learning (DL), optical character recognition (OCR), natural language processing (NLP), voice recognition, and so on, which creates intelligent automation for organizations across multiple industrial domains when combined with robotics.
Image classification is one of the most important applications of computer vision. Its applications ranges from classifying objects in self driving cars to identifying blood cells in healthcare industry, from identifying defective items in manufacturing industry to build a system that can classify persons wearing masks or not. Image Classification is used in one way or the other in all these industries. Which framework do they use? You must have read a lot about the differences between different deep learning frameworks including TensorFlow, PyTorch, Keras, and many more.
Machine vision has come a long way from the simpler days of cameras attached to frame grabber boards--all arranged along an industrial production line. While the basic concepts are the same, emerging embedded systems technologies such as Artificial Intelligence (AI), deep learning, the Internet-of-Things (IoT) and cloud computing have all opened up new possibilities for machine vision system developers. To keep pace, companies that used to only focus on box-level machine vision systems are now moving toward AI-based edge computing systems that provide all the needed interfacing for machine vision, but also add new levels of compute performance to process imaging in real-time and over remote network configurations. AI IN MACHINE VISION ADLINK Technology appears to be moving in this direction of applying deep learning and AI to machine vision. The company has a number of products, listed "preliminary" at present, that provide AI machine vision solutions. These systems are designed to be "plug and play" (PnP) so that machine vision system developers can evolve their existing applications to AI-enablement right away with no need to replace existing hardware.
If you're a data scientist who has been wanting to break into the deep learning realm, here is a great learning resource that can guide you through this journey. It's pretty much an all-inclusive resource that includes all the popular methodologies upon which deep learning depends: CNNs, RNNs, RL, GANs, and much more. The glue that makes it all work is represented by the two most popular frameworks for deep learning pratcitioners, TensorFlow and Keras. This book was a real team effort by a group of consummate professionals: Antonio Gulli (Engineering Director for the Office of the CTO at Google Cloud), Amita Kapoor (Associate Professor in the Department of Electronics at the University of Delhi), and Sujit Pal (Technology Research Director at Elsevier Labs). The resulting text, Deep Learning with TensorFlow 2 and Keras, Second Edition, is an obvious example of what happens when you enlist talented people to write a quality learning resource.
If you're a data scientist who has been wanting to break into the deep learning realm, here is a great learning resource that can guide you through this journey. It's pretty much an all-inclusive resource that includes all the popular methodologies upon which deep learning depends: CNNs, RNNs, RL, GANs, and much more. The glue that makes it all work is represented by the two most popular frameworks for deep learning pratcitioners, TensorFlow and Keras. This book was a real team effort by a group of consummate professionals: Antonio Gulli (Engineering Director for the Office of the CTO at Google Cloud), Amita Kapoor (Associate Professor in the Department of Electronics at the University of Delhi), and Sujit Pal (Technology Research Director at Elsevier Labs). The resulting text, Deep Learning with TensorFlow 2 and Keras, Second Edition, is an obvious example of what happens when you enlist talented people to write a quality learning resource. I've already recommended this book to my newbie data science students, as I enjoy providing them with good tips for ensuring their success in the field.
One of the challenges with modern machine learning systems is that they are very heavily dependent on large quantities of data to make them work well. This is especially the case with deep neural nets, where lots of layers means lots of neural connections which requires large amounts of data and training to get to the point where the system can provide results at acceptable levels of accuracy and precision. Indeed, the ultimate implementation of this massive data, massive network vision is the currently much-vaunted Open AI GPT-3, which is so large that it can predict and generate almost any text with surprising magical wizardry. However, in many ways, GPT-3 is still a big data magic trick. Indeed, Professor Luis Perez-Breva makes this exact point when he says that what we call machine learning isn't really learning at all.
GANs (Generative Adversarial Networks) are a class of models where images are translated from one distribution to another. GANs are helpful in various use-cases, for example: enhancing image quality, photograph editing, image-to-image translation, clothing translation, etc. Nowadays, many retailers, fashion industries, media, etc. are making use of GANs to improve their business and relying on algorithms to do the task. There are many forms of GAN available serving different purposes, but in this article, we will focus on CycleGAN. Here we will see its working and implementation in PyTorch. CycleGAN learns the mapping of an image from source X to a target domain Y. Assume you have an aerial image of a city and want to convert in google maps image or the landscape image into a segmented image, but you don't have the paired images available, then there is GAN for you.
Recommender Systems and Deep Learning in Python 4.6 (1,635 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. What do I mean by "recommender systems", and why are they useful? Let's look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook. Recommender systems form the very foundation of these technologies. They are why Google is the most successful technology company today.
Natural language processing (NLP) technologies are widely deployed to process rich natural language text data for search and recommender systems. Achieving high-quality search and recommendation results requires that information, such as user queries and documents, be processed and understood in an efficient and effective manner. In recent years, the rapid development of deep learning models has been proven successful for improving various NLP tasks, indicating the vast potential for further improving the accuracy of search and recommender systems. Deep learning-based NLP technologies like BERT (Bidirectional Encoder Representations from Transformers) have recently made headlines for showing significant improvements in areas such as semantic understanding when contrasted with prior NLP techniques. However, exploiting the power of BERT in search and recommender systems is a non-trivial task, due to the heavy computation cost of BERT models. In this blog post, we will introduce DeText, a state-of-the-art open source NLP framework for text understanding.