Artificial intelligence is emerging now and many companies are adopting it.
Many developers have concern about how and where to learn AI.

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In this video we will see
– How to learn Artificial intelligence for free
– Three things most important: petience , commmitment and time
– Prerequisite to learn Artificial intelligence
– Programming language
– Maths
– Time or duration to learn
– Efforts
– Where to learn from
– Some of the suggestions: Edx, Coursera, Udacity
– Courses on Artificial intelligence

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Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data mining.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:

The heavily hyped, self-driving Google car? The essence of machine learning.
Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
Fraud detection? One of the more obvious, important uses in our world today.


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This is my opinion about a chat of technology.

( ** AI & Deep Learning with Tensorflow Training: ** )
This Edureka video on “Deep Learning Frameworks” ( provides you an insight into the top 8 Deep Learning frameworks you should consider learning

00:38 Chainer
01:40 CNTK
03:12 Caffe
04:46 MXNet
05:55 Deeplearning4j
07:42 Keras
09:21 PyTorch
10:45 TensorFlow
12:10 Conclusion

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#PyTorch #TensorFlow #DeepLearning #Python

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Please share it in the comment section below and our experts will answer it for you.

About the Course
Edureka’s Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

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Who should go for this course?

The following professionals can go for this course:

1. Developers aspiring to be a ‘Data Scientist’

2. Analytics Managers who are leading a team of analysts

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

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Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
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 have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

For more information, please write back to us at or call us at IND: 9606058406 / US: 18338555775 (toll-free).

Traditional education systems are failing us. Download your guide to bridging the Digital Skills Gap here:

The future of education won’t involve learning from outdated textbooks or from highly-specialised professors. Future skills will need to be learnt and developed in a different way – one which adapts to the rate of change and combines technology and education.

Most people fail to adapt to the rate of change, and breakthroughs in automation, behavioural psychology, artificial intelligence, design, tooling, data, neuroscience and technology are starting to leave people behind.

There’s no doubt about it – advancing technology will change the nature of many people’s jobs, and the future of education looks uncertain at the moment, with predictions that 50 % of the 4,000 colleges and universities in the U.S will be bankrupt in 10 to 15 years.

But it’s not all doom and gloom – this potential threat can be turned into an opportunity to combine technology and education, and foster future skills. Embrace the benefits of the future of technology by learning new skills and upskilling – now.

Only so much can be learnt from self-learning, online courses, undergraduate degrees or master’s degrees. And on-the-job learning can only take place in the right learning environment, where companies allow employees and team members space and resources to learn and develop their skills. The future of education depends on innovative organisations adopting multi-disciplinary based approaches that create the opportunity for future skills to develop. The future of jobs depends on it.


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Carl Henrik Ek is a Senior Lecturer in Computer Science at the University of Bristol, UK. In his entertaining and informative manner, he peels back the hype from deep learning and machine learning and looks at what is really going on under the hood.

For more information and the slides of this talk, take a look at our resource page:

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And by Amplify Partners.

For any early stage ML startup founders, Amplify Partners would love to hear from you via

To learn more, I highly recommend the book by Michael Nielsen
The book walks through the code behind the example in these videos, which you can find here:

MNIST database:

Also check out Chris Olah’s blog:
His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great.

And if you like that, you’ll *love* the publications at distill:

For more videos, Welch Labs also has some great series on machine learning:

“But I’ve already voraciously consumed Nielsen’s, Olah’s and Welch’s works”, I hear you say. Well well, look at you then. That being the case, I might recommend that you continue on with the book “Deep Learning” by Goodfellow, Bengio, and Courville.

Thanks to Lisha Li (@lishali88) for her contributions at the end, and for letting me pick her brain so much about the material. Here are the articles she referenced at the end:

Music by Vincent Rubinetti:


3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you’re into that).

If you are new to this channel and want to see more, a good place to start is this playlist:

Various social media stuffs:

Ten years ago, researchers thought that getting a computer to tell the difference between a cat and a dog would be almost impossible. Today, computer vision systems do it with greater than 99 percent accuracy. How? Joseph Redmon works on the YOLO (You Only Look Once) system, an open-source method of object detection that can identify objects in images and video — from zebras to stop signs — with lightning-quick speed. In a remarkable live demo, Redmon shows off this important step forward for applications like self-driving cars, robotics and even cancer detection.

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Matt Zeiler sits down with GLG (Gerson Lehrman Group) to discuss advances in computer vision and how AI enables your computer to learn what’s inside an image.

Matthew Zeiler is the Founder and CEO of Clarifai Inc, a company that uses machine learning to enable search engines to better source content from visually similar images. He studied under several pioneers in the field of deep learning at the University of Toronto and received a PhD in Machine Learning and Image Recognition from New York University. Through his work with Clarifai, Zeiler won the 2013 ImageNet Classification Challenge.

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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.

Trainer: Navin Reddy

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In this talk, Molly speaks about design and artificial intelligence. More specifically, what can AI learn from design, and what can design reveal about what goes on within AI.

Molly Wright Steenson is a designer, writer, and speaker, and an associate professor at Carnegie Mellon’s School of Design. Her work focuses on the intersection of design, architecture, and artificial intelligence. She is the author of the forthcoming book Architectural Intelligence: How Designers and Architects Created the Digital Landscape (MIT Press, Fall 2017), which tells the radical history of AI’s impact on design and architecture and how it poured the foundation for contemporary digital design. A web pioneer since 1994, she’s worked at groundbreaking design studios, consultancies, and Fortune 500 companies. Dr. Steenson holds a PhD in architecture from Princeton University and a master’s in architectural history from Yale.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at

This is a talk I gave at the SF Machine Learning Meetup on 4/11/17, hosted by AWS.

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.

Artificial Intelligence and Biotechnology are redefining what it means to be human, and succeed in life and work. Education curricula must be deeply redesigned for versatility and adaptability, implying all of the four dimensions of Knowledge, Skills, Character and Meta-Learning, and the interplay between them. Our future requires holistic humans who are like “Swiss Army knives”: multi-faceted capabilities in many domains, broadly and deeply, with the ability to continuously reflect and adapt by having “learned how to learn”. Charles Fadel is a global education thought leader and author, futurist and inventor; founder Center for Curriculum Redesign; visiting scholar Harvard GSE; chair education committee BIAC/OECD; co-author “Four-Dimensional Education” (now in 15 languages) and best-selling “21st Century Skills”; founder Fondation Helvetica Educatio. He was formerly: Global Education Lead at Cisco Systems, worked with education entities in thirty countries; visiting scholar at MIT; angel investor. Holder of BSEE, MBA, 7 patents. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at