This page is a collection of MIT courses and lectures on deep learning, deep reinforcement learning, autonomous vehicles, and artificial intelligence taught by Lex Fridman. New lectures will be up in January. I am teaching 3 courses this January. There will be a lecture every day at 3-4:30pm for 4 weeks (Mon, Jan 7 to Fri, Feb 1). Location is room 54-100 (directions).

Entrepreneurs all know that Artificial Intelligence and Machine Learning are hot topics of focus among startups.

Over the last 10 years, the world has witnessed quantum leaps in the technology space; especially in artificial intelligence. For example, compared to the initial days of speech recognition, today smartphones have turned into virtual assistants. With all these advancements in technology coming into the market, people are gaining more interest in the technology behind -- like machine learning and deep learning. As interest and the excitement surrounding DL continues to grow, experts are still wondering if DL will make a great impact on AI or if it's just a hyped craze which will disappear in the near future. According to research and advisory firm, a technology progression flows through a life cycle.

One of the biggest news subjects in the past few years has been artificial intelligence. We have read about how Google's DeepMind beat the world's best player at Go, which is thought of as the most complex game humans have created; witnessed how IBM's Watson beat humans in a debate; and taken part in a wide-ranging discussion of how A.I. applications will replace most of today's human jobs in the years ahead. Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come. Early rule-based A.I. applications were used by financial institutions for loan applications, but once the exponential growth of processing power reached an A.I. tipping point, and we all started using the Internet and social media, A.I. had enough power and data (the fuel of A.I.) to enable smartphones, chatbots, autonomous vehicles and far more. As I advise the leadership of many leading companies, governments and institutions around the world, I have found we all have different definitions of and understandings about A.I., machine learning and other related topics. If we don't have common definitions for and understanding of what we are talking about, it's likely we will create an increasing number of problems going forward.

WASHINGTON D.C. [USA]: According to a recent study, a new artificial intelligence technology can accurately identify rare genetic disorders using a photograph of a patient's face. Named DeepGestalt, the AI technology outperformed clinicians in identifying a range of syndromes in three trials and could add value in personalised care, CNN reported. The study was published in the journal Nature Medicine. According to the study, eight per cent of the population has disease with key genetic components and many may have recognisable facial features. The study further adds that the technology could identify, for example, Angelman syndrome, a disorder affecting the nervous system with characteristic features such as a wide mouth with widely spaced teeth etc. Speaking about it, Yaron Gurovich, the chief technology officer at FDNA and lead researcher of the study said, "It demonstrates how one can successfully apply state of the art algorithms, such as deep learning, to a challenging field where the available data is small, unbalanced in terms of available patients per condition, and where the need to support a large amount of conditions is great."

There has been a steady rise in ML-powered AI application in industry sectors like preventive healthcare, banking, finance, and media. We have trained professionals who can provide ML based solution of various business problems as good as any.

Check out more details about Amazon AI Conclave at - https://amzn.to/2AxfR28. Welcome address by Navdeep Manaktala, Head - BD, Amazon Internet Services Pvt. Amazon AI Conclave is a free event for business leaders, data scientists, engineers and developers to learn about Amazon's machine learning services, and real-world use cases developed by our customers. This program helps you understand how to build smart, customer-centric, scalable solutions in the cloud and on the edge using Amazon AI, AWS IoT, Machine Learning and AWS Deep Learning.

Over the last few years, Deep Learning has proven itself to be the game-changer. This area of data science is the only one responsible for the advancements in machine learning and artificial intelligence. From academic researches to self-driving cars, Deep Learning is found in all possible aspects nowadays. Deep Learning is a complex and a vast field that consists of several components. It cannot be mastered in a day and hence it will take several months if you want to dig deeper into this field.

This course is about deep learning fundamentals and convolutional neural networks. Convolutional neural networks are one of the most successful deep learning approaches: self-driving cars rely heavily on this algorithm. First you will learn about densly connected neural networks and its problems. The next chapter are about convolutional neural networks: theory as well as implementation in Java with the deeplearning4j library. The last chapters are about recurrent neural networks and the applications!

In my opinion RBMs have one of the easiest architectures of all neural networks. As it can be seen in Fig.1. The absence of an output layer is apparent. But as it can be seen later an output layer wont be needed since the predictions are made differently as in regular feedforward neural networks. Energy is a term that may not be associated with deep learning in the first place.