Deep learning is rapidly transforming the global financial services industry. A step ahead of machine learning, deep learning focuses on finding minute details to function. Deep learning is a type of machine learning in AI that gathers huge datasets to make machines act like humans. Due to the use of neural networks, deep learning produces optimized results. You must have observed how Facebook automatically finds your friend in an image and suggests you tag her.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Deep learning is one of the hottest topics in AI, but what is it?

We always adopt the latest technologies, and so does the industry experts. It becomes necessary for each one of us in the technology field to remain updated with the evolving technologies. Considering it as your to-do task, we are up with a course that can earn you a better salary in 2020. You all must have heard about robots working as similar to humans. But have you heard about a machine imitating a human brain?

Job Description Job Description: A cutting edge group who leads Intel's global machine learning solutions is hiring a talented senior Data Scientist in the field of Deep Learning. Our group is a competency center for machine learning big data at Intel, we deliver internal and external solutions/products that can create a competitive advantage for the company. Currently Intel develops innovative software and hardware products for the deep-learning domain, and our group handles data-science aspects of these projects. As a part of our diverse and dynamic group, you will be exposed to very exciting areas of practice, and take part in shaping the future intelligent machines. As a Data Scientist you will usually work in a project team as a key player in finding appropriate algorithmic solution to a given problem while using your machine learning and deep learning knowledge and experience.

There are multiple ways to learn data science, machine learning and deep learning concepts. You can watch videos, read articles, enroll in courses, attend meetups, among other things. I have personally learned a LOT from interacting with data science experts and industry thought leaders. Their experience in managing end-to-end machine learning and deep learning projects, their thinking when building a data science team from scratch, how they managed tough projects and overcame hurdles, etc. – we simply cannot learn all of these in any course. So, I am thrilled to present an exclusive interview with one such data science expert and industry thought leader – Dr. Sunil Kumar Vuppala!

At Deep Instinct, we pride ourselves on preventing what other cybersecurity solutions can't even detect. To make a long story short, and to paraphrase my last blog post on Machine Learning vs Deep Learning [READ], we achieve this through a combination of AI, machine learning and deep learning. Deep Learning is not a technology that is widely used by companies in production yet, with only a select few managing to move it beyond academic research, for commercial use. A greater barrier in Deep Learning is recruiting, there is a real scarcity of deep learning scientists. Even the largest technology companies (Google, Microsoft, Apple, etc), are struggling to recruit deep learning scientists, and even recruiting deep learning engineers (who lack a more in-depth scientific understanding of the subject) is an extremely challenging task.

Heather Gorr holds a Ph.D. in Materials Science Engineering from the University of Pittsburgh and a Masters and Bachelors of Science in Physics from Penn State University. Since 2013, she has supported MATLAB users in the areas of mathematics, data science, deep learning, and application deployment. Prior to joining MathWorks, she was a Research Fellow, focused on machine learning for prediction of fluid concentrations. Johanna Pingel joined the MathWorks team in 2013, specializing in Image Processing and Computer Vision applications with MATLAB. She has a M.S. degree from Rensselaer Polytechnic Institute and a B.A. degree from Carnegie Mellon University.

CHENNAI: Two startups incubated at Indian Institute of Technology Madras have joined hands with a mission to create one Lakh experts in Artificial Intelligence (AI) and Deep Learning by the year 2020. GUVI, which offers a platform for students in Tier 2,3 cities to learn in vernacular languages, is now collaborating with One Fourth Labs, a startup founded by IIT Madras Faculty which offers advanced AI courses. AI is one of the dominant technologies of this generation, which has helped machines reach human-level performance on specific tasks such as identifying faces, classifying images, playing complex strategy games, detecting anomalies in medical images and so on. There is a huge demand for AI talent in India, but the supply is limited due to a shortage of affordable courses which take students from basics to advanced topics. GUVI will be the platform partner and One Fourth Labs will be the content partner for this joint initiative.

Forecasting the performance of a deep neural network is a nightmare for every data scientist. Every month, dozens of new deep learning research algorithms are published making incredible claims about their performance. However, applying those algorithms to real world problems requires a leap of faith that the model can achieve similar levels of performance with unseen datasets. Not surprisingly, many of the research algorithms that performed incredibly well for specific datasets miserably fail when apply to different domains as a clear manifestation of the famous "No Free Lunch Theorem". Very recently, researchers from IBM's artificial intelligence(AI) lab in Zurich published a new paper proposing a method that uses neural networks to predict the performance of a new model prior to training.

This article was written by Harry Chiang, a financial analyst at I Know First. Most humans would understand, perhaps even intuitively, that when he or she runs there is a certain path of movement and way in which he or she is interacting with the environment. The athlete will follow the curvature of the track. They dictate how his or her body moves and how his or her feet must move along the rubber. A machine, however, would struggle to understand all these small details.