Yoshua Bengio, along with Geoffrey Hinton and Yann Lecun, is considered one of the three people most responsible for the advancement of deep learning during the 1990s, 2000s, and now. Cited 139,000 times, he has been integral to some of the biggest breakthroughs in AI over the past 3 decades.
This conversation is part of MIT 6.S099: Artificial General Intelligence. This class is free and open to everyone. Our goal is to take an engineering approach to exploring possible paths toward building human-level intelligence for a better world. Audio podcast version is available on https://lexfridman.com/ai/
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This video provides a list of YouTube videos and playlists, which you can use to improve your knowledge in the areas of machine learning and deep learning. Below are the links to each video/playlist:
David Silver’s Videos on Reinforcement Learning:
SysML 18: Michael Jordan, Perspectives and Challenges:
Deep Probabilistic Methods with PyTorch – Chris Ormandy:
Geoffrey Hinton talk “What is wrong with convolutional neural nets ?”:
NIPS 2016 – Generative Adversarial Networks – Ian Goodfellow:
Heroes of Deep Learning: Andrew Ng interviews Geoffrey Hinton:
Yann LeCun – Power & Limits of Deep Learning:
DeepMind’s Richard Sutton – The Long-term of AI & Temporal-Difference Learning:
The Future of Robotics and Artificial Intelligence (Andrew Ng, Stanford University, STAN 2011):
Using Python to Code by Voice:
Searching a image when you know the name is possible.
But if you want to search for a name if you have only image or logo then what ?
Google has a feature called google goggle.
This feature helps you detect the name of the logo.
This software works on Artificial Intelligence.
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This Machine Learning vs Deep Learning vs Artificial Intelligence video will help you understand the differences between ML, DL and AI, and how they are related to each other. The tutorial video will also cover what Machine Learning, Deep Learning and Artificial Intelligence entail, how they work with the help of examples, and whether they really are all that different.
This Machine Learning Vs Deep Learning Vs Artificial Intelligence video will explain the topics listed below:
1. Artificial Intelligence example ( 00:29 )
2. Machine Learning example ( 01:29 )
3. Deep Learning example ( 01:44 )
4. Human vs Artificial Intelligence ( 03:34 )
5. How Machine Learning works ( 06:11 )
6. How Deep Learning works ( 07:09 )
7. AI vs Machine Learning vs Deep Learning ( 12:33 )
8. AI with Machine Learning and Deep Learning ( 13:05 )
9. Real-life examples ( 15:29 )
10. Types of Artificial Intelligence ( 17:50 )
11. Types of Machine Learning ( 20:32 )
12. Comparing Machine Learning and Deep Learning ( 22:46 )
13. A glimpse into the future ( 25:46 )
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To gain in-depth knowledge of Machine Learning, Deep learning and Artificial Intelligence, Check out our Artificial Intelligence Engineer Program: https://www.simplilearn.com/artificial-intelligence-masters-program-training-course?utm_campaign=Machine-Learning-Vs-Deep-Learning-Vs-Artificial-Intelligence-9dFhZFUkzuQ&utm_medium=Tutorials&utm_source=youtube
You can also go through the Slides here: https://goo.gl/cdQ7uy
#SimplilearnMachineLearning #SimplilearnAI #SimplilearnDeepLearning #Artificialintelligence #MachineLearningTutorial
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About Simplilearn Artificial Intelligence Engineer course:
What are the learning objectives of this Artificial Intelligence Course?
By the end of this Artificial Intelligence Course, you will be able to accomplish the following:
1. Design intelligent agents to solve real-world problems which are search, games, machine learning, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, agent decision making
2. Master TensorFlow by understanding the concepts of TensorFlow, the main functions, operations and the execution pipeline
3. Acquire a deep intuition of Machine Learning models by mastering the mathematical and heuristic aspects of Machine Learning
4. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
5. Comprehend and correlate between theoretical concepts and practical aspects of Machine Learning
6. Master and comprehend advanced topics like convolutional neural networks, recurrent neural networks, training deep networks, high-level interfaces
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What skills will you learn with our Masters in Artificial Intelligence Program?
1. Learn about major applications of Artificial Intelligence across various use cases in various fields like customer service, financial services, healthcare, etc
2. Implement classical Artificial Intelligence techniques such as search algorithms, neural networks, tracking
3. Ability to apply Artificial Intelligence techniques for problem-solving and explain the limitations of current Artificial Intelligence techniques
4. Formalise a given problem in the language/framework of different AI methods such as a search problem, as a constraint satisfaction problem, as a planning problem, etc
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This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on “AI vs Machine Learning vs Deep Learning” talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
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#edureka #AIvsMLvsDL #PythonTutorial #PythonMachineLearning #PythonTraining
How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
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About the Course
Edureka’s Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
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Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain.
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“Deep Learning” is an area in artificial intelligence research and design where a “ton of breakthroughs are happening right now,” says Fast.AI co-founder Rachel Thomas. Fast.AI’s mission in the AI space is to make Deep Learning more accessible to more people. Where before, you’d need graduate-to-Ph.D.-level work in math just to understand what’s going on, now Fast.AI’s course ware makes Deep Learning techniques available to everyday coders.
In the last five to six years, Deep Learning has become a hot topic. Technically, Deep Learning is like any other kind of AI, in that it makes your computer behave intelligently. This has been around for decades. The difference is, in the last few years, it’s started to take off as sub-field of its own. Deep Learning algorithms mimic neural networks in the brain. And the technology is improving exponentially every year, exceeding human intelligence in some cases. By Deep Learning,” Fast.AI co-founders Rachel Thomas and Jeremy Howard are referring to a very specific class of algorithms applied to specific problems, for example, transcribing speech to text.
Fundamentally, Fast.AI is an education company. The course offered is free. The goal is to help companies train the smart employees they already have to utilize Deep Learning technology.
This is a talk I gave at the SF Machine Learning Meetup on 4/11/17, hosted by AWS. https://www.meetup.com/SF-Bayarea-Machine-Learning/events/238740130/
Many people claim that deep learning needs to be a highly exclusive field, saying that you must spend years studying advanced math before you even begin to attempt it. Jeremy Howard and I believed that this was just not true, so we set out to see if we could teach deep learning to coders (with no math prerequisites) in 7 part-time weeks.
Our students are now using deep learning to identify chainsaw noise in endangered rain forests, create translation resources for Pakistani languages, reduce farmer suicides in India, diagnose breast cancer, and more. We wanted to help them get results fast, so we taught them in a code-centric, application-focused way. I’ll share what we learnt about how to learn deep learning effectively, so that you can set out on your own learning journey.
This presentation took place at the Deep Learning Summit in San Francisco on 29-30 January 2015. https://www.re-work.co/events/deep-learning-sanfrancisco-2015
The elusive quest to identify and place skilled professionals has become an obsession in the talent wars of the tech industry (not to mention in schools from K though Postdoc). We will discuss the concept of continuous passive predictive (formative) assessment, applied to both learners and professionals, from kindergärtners to (future) CEOs. Building cognitive models using unstructured data and ubiquitous sensors allows the assessment not only of concept mastery, but meta-learning development as well (e.g., “Grit” and “Social-Emotional Intelligence”). Such models can then be used to predict which content will be an effective learning experience for a given learner. In massive courses, from large college lectures to MOOCs, the models can identify ad hoc cohorts for collaborative learning.
Dr. Vivienne Ming is a theoretical neuroscientist, technologist and entrepreneur. She is the co-founder and Managing Partner of Socos, a cutting-edge EdTech company which applies cognitive modeling to align education with life outcomes. Previously, Dr. Ming was Chief Scientist at Gild, an innovative startup that builds better companies by unleashing human potential in their workforce using machine learning. She is a visiting scholar at UC Berkeley’s Redwood Center for Theoretical Neuroscience pursuing her research in cognitive prosthetics. Dr. Ming also explores augmented cognition using technology like Google Glass and has been developing predictive models of diabetes and bipolar disorder. Her work and research has received extensive media attention including the New York Times, NPR, Nature, O Magazine, Forbes, and The Atlantic.
A deep learning platform enables a user to apply deep nets without building one from scratch. They come in two different forms: software platforms and full platforms.
Deep Learning TV on
A platform is a set of tools that users can build on top of. Platforms in other contexts include iOS/Android and MacOS/Windows for example. A Deep Net platform provides a set of tools that simplify the process of building a deep net for a custom application. They typically allow the user to select a particular deep net, integrate and munge data, and manage models from a UI. Some platforms also help to enhance performance when dealing with large data sets.
Ersatz Labs is an example of a full platform because it hosts your Deep Learning applications on a cloud. The platform handles the technical aspects like hardware, code, and networking; the user only needs to build and manage deep nets through a UI. In contrast, software platforms require the user to train and run the nets on their own personal hardware.
H2O.ai and Dato GraphLab are two examples of machine learning software platforms that offer Deep Nets; since they aren’t full platforms, you will need to install them on your own hardware infrastructure in order to use them.
Have you ever used any of these platforms? Please comment and share your experiences.
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In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples, eventually coming to a definitive conclusion.
The code for the TensorFlow vs Theano part of the video is here:
An article that explains the differences in more detail:
I created a Slack channel for us, sign up here:
Learn more about TF Learn here:
Learn more about TensorFlow here:
More on Keras here:
More on SciKit Learn here:
More on Caffe here:
More on Theano here:
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Artificial Intelligence in simple terms is a branch of technology that focuses on machines and them having the capability to think and make decisions like human beings. It has evolved to an extent where machines are able to recognize speech, visuals, languages and eventually make sound decisions. Moving on, Machine Learning is the subset of Artificial Intelligence that helps machines make decisions based on their previous outcomes or experiences. Moving further ahead, we have the final branch of Artificial Intelligence, Deep Machine Learning that is a subset of Machine Learning. What is Deep Learning? In Deep Learning machine trains itself with the help of deep learning algorithms and neural networks to recognize images or detect speech. Deep Machine Learning is the most advanced form of Artificial Intelligence today.
However, with all the advantages of Artificial Intelligence in advancing technology the question remains, does Artificial Intelligence help in domain names and web development?
This talk focuses on Deep Learning and the prospects it offers to the web developers in current times.
Vikramank Singh, a Data Scientist at Facebook explains to us the intricate concept of deep learning, a subset of Artificial Intelligence. He emphasizes the need for learning Artificial Intelligence and how it matters to developers for building blogs and websites, without coding. He even throws light on the role of Deep Learning and Artificial Intelligence in enhancing SEO for a website.
Furthermore, he moulds his talk to ‘Deep Learning for Browsers’ covering ConvNetJS a project created at Stanford University using deep neural networks. He shares how this project can help increase the performance of browsers and users can truly leverage Artificial Intelligence through an online medium.
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A revolution in AI is occurring thanks to progress in deep learning. How far are we towards the goal of achieving human-level AI? What are some of the main challenges ahead?
Yoshua Bengio believes that understanding the basics of AI is within every citizen’s reach. That democratizing these issues is important so that our societies can make the best collective decisions regarding the major changes AI will bring, thus making these changes beneficial and advantageous for all.
Yoshua Bengio is one of the pioneers of Deep Learning. He is the head of the Montreal Institute for Learning Algorithms (MILA), Professor at the Université de Montréal, member of the NIPS board and co-founder of Element AI. With a PhD from McGill University (1991, Computer Science) and postdocs at MIT and AT&T Bell Labs, he holds the Canada Research Chair in Statistical Learning Algorithms, is a Senior Fellow of the Canadian Institute for Advanced Research and co-directs its program focused on deep learning. He is best known for his contributions to deep learning, recurrent nets, neural language models, neural machine translation and biologically inspired machine learning.
For more information visit http://www.tedxmontreal.com
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
Yoshua Bengio talks about artificial intelligence through deep learning at TedXMontreal. http://bit.ly/2BDTHcs
For the entire video The Rise of Artificial Intelligence through Deep Learning | Yoshua Bengio | TEDxMontreal click here: https://www.youtube.com/watch?v=uawLjkSI7Mo
Artificial Neural Networks are inspired by some of the “computations” that occur in human brains—real neural networks. In the past 10 years, much progress has been made with Artificial Neural Networks and Deep Learning due to accelerated computer power (GPUs), Open Source coding libraries that are being leveraged, and in-the-moment debates and corroborations via social media. Hugo Larochelle shares his observations of what’s been made possible with the underpinnings of Deep Learning.
Hugo Larochelle is a Research Scientist at Twitter and an Assistant Professor at the Université de Sherbrooke (UdeS). Before 2011, he spent two years in the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. He obtained his Ph.D. at Université de Montréal, under the supervision of Yoshua Bengio. He is the recipient of two Google Faculty Awards. His professional involvement includes associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), member of the editorial board of the Journal of Artificial Intelligence Research (JAIR) and program chair for the International Conference on Learning Representations (ICLR) of 2015, 2016 and 2017.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
AI presents a huge opportunity for businesses to personalize and improve customer experiences and improve efficiency, but the technical complexity of AI puts it out of reach for most companies. Richard Socher explains how Salesforce is doing the heavy lifting to deliver seamless and scalable AI to its customers.
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