Deep learning is a sub-field of machine learning and an aspect of artificial intelligence. To understand this more easily, understand that it is meant to emulate the learning approach that humans use to acquire certain types of knowledge. This is somewhat different from machine learning, often people get confused in this and machine learning. Deep learning uses a sequencing algorithm while machine learning uses a linear algorithm. To understand this more accurately, understand this example that if a child is identified with a flower, then he will ask again and again, is this flower?
This will be an interactive post using Google Colab notebooks. If you have not used Google Colab before, there is a quick-start tutorial at tutorialspoint. You can access the notebook at this link: Train your first DL model. First, make a copy and save it into your Drive so that you can access it and make changes. Next, make sure the runtime is set to GPU so you can make use of the free resources provided by Google.
These days we are hearing a lot about AI, but have you ever heard about EDGE AI ..? What does it mean and what is it used for? Network edge or edge, where data resides and collected. Edge computing processes data on local places like computers, IoT devices or Edge servers, here we are doing computation to a network edge which indeed reduces long-distance communication between client and server. Edge AI, where AI algorithms will locally process sensor data or signals that are created on hardware devices in less than a few milliseconds by providing real-time information. Most of the time the AI algorithms are being processed in cloud data centers with deep learning models, which consume heavy compute capacity.
Time series forecasting is an important area of machine learning. It is important because there are so many prediction problems that involve a time component. However, while the time component adds additional information, it also makes time series problems more difficult to handle compared to many other prediction tasks. Time series data, as the name indicates, differ from other types of data in the sense that the temporal aspect is important. On a positive note, this gives us additional information that can be used when building our machine learning model -- that not only the input features contain useful information, but also the changes in input/output over time.
New Delhi, September 10, 2020: As part of its ongoing efforts to promote skilling as a national priority, NASSCOM FutureSkills and Microsoft have joined hands to launch a nation-wide AI skilling initiative. The initiative aims to skill 1 million students in AI by 2021. The collaboration will provide Microsoft's AI, machine learning and data science expertise to students through easy to consume modules including live demos, hands on workshops and assignments. These introductory sessions on AI will be available for undergraduate students at no cost and will cover the basics of data science, machine learning models on Azure, and understanding of cognitive services to build intelligent solutions. The partnership with NASSCOM FutureSkills is an extension of Microsoft's global skilling initiative to help 25 million people worldwide acquire new digital skills, needed to thrive in a digital economy.
As technology has continued to advance, we've seen some pretty impressive strides in artificial intelligence. We have a virtual assistant in our pockets at all times, can integrate home technology into voice activated commands, and have even started to develop robots who can hold actual conversations with humans. While some people see this as a frightening glimpse into a robot-dominated world, others view it as a necessary step into the future. With artificial intelligence on the rise, machine learning is beginning to make AI even more identifiable. While the actual idea of machine learning has been around since the 1950s, it hasn't always played out in beneficial matters.
It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".
Grab a copy of The Elements of Statistical Learning ("the machine learning bible") and you might be a little overwhelmed by the mathematics. For example, this equation (p.34), for a cubic smoothing spline, might send shivers down your spine if math isn't your forte: In order to grasp that equation, nested firmly in the "Introductory" section of the book, you need to know function notation, sigma (summation) notation, derivatives, and Greek letters. Basically, if you haven't taken a calculus class, you're not going to be able to follow along. But, do you really need to know all of that math to grasp the fundamentals of ML? An Introduction to Statistical Learning covers much of the same material, but in a less mathematical way.
The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field. The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.
Every once in a while, a machine learning framework or library changes the landscape of the field. In this article, we'll quickly understand the concept of object detection and then dive straight into DETR and what it brings to the table. In Computer Vision, object detection is a task where we want our model to distinguish the foreground objects from the background and predict the locations and the categories for the objects present in the image. Current deep learning approaches attempt to solve the task of object detection either as a classification problem or as a regression problem or both. For example, in the RCNN algorithm, several regions of interest are identified from the input image.