Scale By the Bay 2019 is held on November 13-15 in sunny Oakland, California, on the shores of Lake Merritt: Join us!

In this talk, I will describe deep learning algorithms that learn representations for language that are useful for solving a variety of complex language problems. I will focus on 3 tasks: Fine-Grained sentiment analysis; Question answering to win trivia competitions (like Whatson’s Jeopardy system but with one neural network); Multimodal sentence-image embeddings (with a fun demo!) to find images that visualize sentences. I will also show some demos of how deepNLP can be made easy to use with’s software.

Richard Socher is the CTO and founder of MetaMind, a startup that seeks to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng. He is interested in developing new AI models that perform well across multiple different tasks in natural language processing and computer vision. He was awarded the 2011 Yahoo! Key Scientific Challenges Award, the Distinguished Application Paper Award at ICML 2011, a Microsoft Research PhD Fellowship in 2012 and a 2013 ‘Magic Grant’ from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award.

** AI & Deep Learning with Tensorflow Training: **
This Edureka video on “Keras vs TensorFlow vs PyTorch” will provide you with a crisp comparison among the top three deep learning frameworks. It provides a detailed and comprehensive knowledge about Keras, TensorFlow and PyTorch and which one to use for what purposes. Following topics will be covered in this video:
1:06 – Introduction to keras, Tensorflow, Pytorch
2:13 – Parameters of Comparison
2:18 – Level of API
3:06 – Speed
3:28 – Architecture
4:03 – Ease of Code
4:27 – Debugging
4:59 – Community Support
5:19 – Datasets
5:37 – Popularity
6:14 – Suitable use cases

Subscribe to our channel to get video updates. Hit the subscribe button above

PG in Artificial Intelligence and Machine Learning with NIT Warangal :

Post Graduate Certification in Data Science with IIT Guwahati –
(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)


Check our complete Deep Learning With TensorFlow playlist here:
#keras #tensorflow #pytorch #deeplearning #machinelearning #frameworks
– – – – – – – – – – – – – –

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!

– – – – – – – – – – – – – –

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.

– – – – – – – – – – – – – –

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.

– – – – – – – – – – – – – –

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.

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).

Some ASEAN countries may be on the road to economic recovery, but many economists warned that it won’t be smooth. CABA ASEAN Summit 2020 brings a panel of experts to address how technology like AI, deep tech and blockchain to act as a tech enabler for the businesses and governments.

The panel led by Anndy Lian has covered the following topics:

– How technology like AI, deep tech and blockchain will affect lives?
– How AI can help in good data and 5G
– How to tackle teething problems such as security for AI?
– How can blockchain technology improve on security aspects of things?
– Do you really trust AI?
– What should investors and people who want to get into the technology industry look at? What is the future?

Moderated by:
– Anndy Lian, Advisory Board Member of Hyundai DAC

Panel members:
– Dr Andrew Wu, Founder & Chief Executive Officer, Meshbio Pte Ltd
– Sheeram Iyer, Chief Executive Officer & Founder, Prisma Global
– Stephen Ho, Group Chief Operating Officer, Skylab Group

Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer – Stanford University

Andrew Ng
Adjunct Professor, Computer Science

Kian Katanforoosh
Lecturer, Computer Science

To follow along with the course schedule and syllabus, visit:

Machine learning is everywhere in today’s NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations.

One of the main challenges for AI remains unsupervised learning, at which humans are much better than machines, and which we link to another challenge: bringing deep learning to higher-level cognition. We review earlier work on the notion of learning disentangled representations and deep generative models and propose research directions towards learning of high-level abstractions. This follows the ambitious objective of disentangling the underlying causal factors explaining the observed data. We argue that in order to efficiently capture these, a learning agent can acquire information by acting in the world, moving our research from traditional deep generative models of given datasets to that of autonomous learning or unsupervised reinforcement learning. We propose two priors which could be used by an agent acting in its environment in order to help discover such high-level disentangled representations of abstract concepts. The first one is based on the discovery of independently controllable factors, i.e., in jointly learning policies and representations, such that each of these policies can independently control one aspect of the world (a factor of interest) computed by the representation while keeping the other uncontrolled aspects mostly untouched. This idea naturally brings fore the notions of objects (which are controllable), agents (which control objects) and self. The second prior is called the consciousness prior and is based on the hypothesis that our conscious thoughts are low-dimensional objects with a strong predictive or explanatory power (or are very useful for planning). A conscious thought thus selects a few abstract factors (using the attention mechanism which brings these variables to consciousness) and combines them to make a useful statement or prediction. In addition, the concepts brought to consciousness often correspond to words or short phrases and the thought itself can be transformed (in a lossy way) into a brief linguistic expression, like a sentence. Natural language could thus be used as an additional hint about the abstract representations and disentangled factors which humans have discovered to explain their world. Some conscious thoughts also correspond to the kind of small nugget of knowledge (like a fact or a rule) which have been the main building blocks of classical symbolic AI. This, therefore, raises the interesting possibility of addressing some of the objectives of classical symbolic AI focused on higher-level cognition using the deep learning machinery augmented by the architectural elements necessary to implement conscious thinking about disentangled causal factors.

See more at

How does a group of animals — or cells, for that matter — work together when no one’s in charge? Tiny swarming robots–called Kilobots–work together to tackle tasks in the lab, but what can they teach us about the natural world?

↓ More info, videos, and sources below ↓

DEEP LOOK: a new ultra-HD (4K) short video series created by KQED San Francisco and presented by PBS Digital Studios. See the unseen at the very edge of our visible world. Get a new perspective on our place in the universe and meet extraordinary new friends. Explore big scientific mysteries by going incredibly small.


KQED Science:

About Kilobots

How do you simultaneously control a thousand robots in a swarm? The question may seem like science fiction, but it’s one that has challenged real robotics engineers for decades.

In 2010, the Kilobot entered the scene. Now, engineers are programming these tiny independent robots to cooperate on group tasks. This research could one day lead to robots that can assemble themselves into machines, or provide insights into how swarming behaviors emerge in nature.

In the future, this kind of research might lead to collaborative robots that could self-assemble into a composite structure. This larger robot could work in dangerous or contaminated areas, like cleaning up oil spills or conducting search-and-rescue activities.

What is Emergent Behavior?

The universe tends towards chaos, but sometimes patterns emerge, like a flock of birds in flight. Like termites building skyscrapers out of mud, or fish schooling to avoid predators.

It’s called emergent behavior. Complex behaviors that arise from interactions between simple things. And you don’t just see it in nature.

What’s so interesting about kilobots is that individually, they’re pretty dumb.

They’re designed to be simple. A single kilobot can do maybe… three things: Respond to light. Measure a distance, sense the presence of other kilobots.

But these are swarm robots. They work together.

How do Kilobots work?

Kilobots were designed by Michael Rubenstein, a research scientist in the Self Organizing Systems Research Group at Harvard. Each robot consists of about $15 worth of parts: a microprocessor that is about as smart as a calculator, sensors for visible and infrared light, and two tiny cell-phone vibration units that allow it to move across a table. They are powered by a rechargeable lithium-ion battery, like those found in small electronics or watches.

The kilobots are programed all at once, as a group, using infrared light. Each kilobot gets the same set of instructions as the next. With just a few lines of programming, the kilobots, together, can act out complex natural processes.

The same kinds of simple instructions that kilobots use to self-assemble into shapes can make them mimic natural swarming behaviors, too. For example, kilobots can sync their flashing lights like a swarm of fireflies, differentiate similar to cells in an embryo and follow a scent trail like foraging ants.

Read the article for this video on KQED Science:

More great DEEP LOOK episodes:

Where Are the Ants Carrying All Those Leaves?

What Happens When You Put a Hummingbird in a Wind Tunnel?

Pygmy Seahorses: Masters of Camouflage

Related videos from the PBS Digital Studios Network!

Is Ultron Inevitable? | It’s Okay to Be Smart

A History Of Robots | The Good Stuff

When Will We Worry About the Well-Being of Robots? | Idea Channel

Funding for Deep Look is provided in part by PBS Digital Studios and the John S. and James L. Knight Foundation. Deep Look is a project of KQED Science, which is supported by HopeLab, The David B. Gold Foundation; S. D. Bechtel, Jr. Foundation; The Dirk and Charlene Kabcenell Foundation; The Vadasz Family Foundation; Smart Family Foundation and the members of KQED.

We’re going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I’ll explain why we use recurrent nets for time series data, and why LSTMs boost our network’s memory power.

Coding challenge for this video:

Vishal’s winning code:

Jie’s runner up code:

More Learning Resources:

Please subscribe! And like. And comment. That’s what keeps me going.

Join other Wizards in our Slack channel:

And please support me on Patreon:

music in the intro is chambermaid swing by parov stelar
Follow me:
Facebook: Instagram: Instagram:
Signup for my newsletter for exciting updates in the field of AI:
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

Deep learning is a revolutionary technique for discovering patterns from data. We’ll see how this technology works and what it offers us for computer graphics. Attendees learn how to use these tools to power their own creative and practical investigations and applications.

Help fund future projects:
An equally valuable form of support is to simply share some of the videos.
Special thanks to these supporters:

This one is a bit more symbol-heavy, and that’s actually the point. The goal here is to represent in somewhat more formal terms the intuition for how backpropagation works in part 3 of the series, hopefully providing some connection between that video and other texts/code that you come across later.

For more on backpropagation:

Music by Vincent Rubinetti:

Video timeline
0:00 – Introduction
0:38 – The Chain Rule in networks
3:56 – Computing relevant derivatives
4:45 – What do the derivatives mean?
5:39 – Sensitivity to weights/biases
6:42 – Layers with additional neurons
9:13 – Recap

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:

What’s actually happening to a neural network as it learns?
Next chapter:
Help fund future projects:
An equally valuable form of support is to simply share some of the videos.
Special thanks to these supporters:

And by CrowdFlower:
Home page:

The following video is sort of an appendix to this one. The main goal with the follow-on video is to show the connection between the visual walkthrough here, and the representation of these “nudges” in terms of partial derivatives that you will find when reading about backpropagation in other resources, like Michael Nielsen’s book or Chis Olah’s blog.

Video timeline:
0:00 – Introduction
0:23 – Recap
3:07 – Intuitive walkthrough example
9:33 – Stochastic gradient descent
12:28 – Final words

A word embedding is a learned representation for text where words that have the same meaning have a similar representation. It is this approach to representing words and documents that may be considered one of the key breakthroughs of deep learning on challenging natural language processing problems.

Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more

Please do subscribe my other channel too

If you want to Give donation to support my channel, below is the Gpay id
GPay: krishnaik06@okicici

Connect with me here:




Yoshua Bengio, considered one of the ‘Godfathers of Artificial Intelligence’ discusses Recurrent independent mechanisms, sample complexity, end-to-end adaptation, multivariate categorical MLP conditionals and more.

When summarising his talk, Professor Bengio gave three key points to keep in mind when ‘looking forward’

– We must build a world model which meta-learns causal effects in abstract space of causal variables. This requires a necessity to quickly adapt to change and generalize out-of-distribution by sparsely recombining modules

– The necessity to acquire knowledge and encourage exploratory behaviour

– The need to bridge the gap between the aforementioned system 1 and system 2 ways of thinking, with old neural networks and consciousness reasoning taken into account

Yoshua Bengio is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. Bengio received his Bachelor of Science, Master of Engineering and PhD from McGill University.

Recorded: September 8, 2017

The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that’s useful for people who want to watch specific talks several times (like I do). Please check out the official website ( and full live streams below.

Having read, watched, and presented deep learning material over the past few years, I have to say that this is one of the best collection of introductory deep learning talks I’ve yet encountered. Here are links to the individual talks and the full live streams for the two days:

1. Foundations of Deep Learning (Hugo Larochelle, Twitter) –
2. Deep Learning for Computer Vision (Andrej Karpathy, OpenAI) –
3. Deep Learning for Natural Language Processing (Richard Socher, Salesforce) –
4. TensorFlow Tutorial (Sherry Moore, Google Brain) –
5. Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU) –
6. Nuts and Bolts of Applying Deep Learning (Andrew Ng) –
7. Deep Reinforcement Learning (John Schulman, OpenAI) –
8. Theano Tutorial (Pascal Lamblin, MILA) –
9. Deep Learning for Speech Recognition (Adam Coates, Baidu) –
10. Torch Tutorial (Alex Wiltschko, Twitter) –
11. Sequence to Sequence Deep Learning (Quoc Le, Google) –
12. Foundations and Challenges of Deep Learning (Yoshua Bengio) –

Full Day Live Streams:
Day 1:
Day 2:

Go to for more information on the event, speaker bios, slides, etc. Huge thanks to the organizers (Shubho Sengupta et al) for making this event happen.

Jeff Dean discusses the future of artificial intelligence and deep learning. This talk highlights Google research projects in healthcare, robotics, and in developing hardware to bring deep learning capability to smaller devices such as smart phones to enable solutions in remote and under-resourced locations. This talk was part of the AI in Real Life series presented by the Institute for Computational and Mathematical Engineering at Stanford University in Autumn 2018.

Jeff Dean, Head of Google Brain talks about using deep learning to solve challenging problems at the AI/ML Workshop on research and practice in India.

Find all speaker decks for the workshop at:

Subscribe to the Google Developers India channel:

For more updates, follow us at:

In his new book, Deep Medicine, Eric Topol – cardiologist, geneticist, digital medicine researcher – claims that artificial intelligence can put the humanity back into medicine. By freeing physicians from rote tasks, such as taking notes and performing medical scans, AI creates space for the real healing that occurs between a doctor who listens and a patient who needs to be heard. The counterintuitive recognition that technology can create space for compassion in the clinical setting could mean fewer burned-out doctors, more empowered patients, cost savings, and an entirely new way to approach medicine.

Featuring: David Brooks, Eric Topol

This conversation was recorded during Aspen Ideas: Health in Aspen, Colorado. Presented by the Aspen Institute, the three-day event opens the Aspen Ideas Festival and features more than 200 speakers engaging with urgent health care challenges and exploring cutting-edge innovations in medicine and science.

Learn more at

Moderated by Katherine Gorman, Executive Producer, Talking Machines

Dr. Andrew Ng is Chief Scientist at Baidu. He leads Baidu Research, which comprises three interrelated labs: the Silicon Valley AI Lab, the Institute of Deep Learning and the Big Data Lab. The organization brings together global research talent to work on fundamental technologies in areas such as image recognition and image-based search, speech recognition, natural language processing and semantic intelligence. In addition to his role at Baidu, Dr. Ng a faculty member in Stanford University’s Computer Science department, and Chairman of Coursera, an online education platform that he co-founded. Dr. Ng is the author or co-author of over 100 published papers in machine learning, robotics and related fields. He holds degrees from Carnegie Mellon University, MIT and the University of California, Berkeley.

Katherine Gorman is the Executive Producer of the Talking Machines podcast and the Creative Director of Tote Bag Productions. After a decade as a daily news producer for public radio, she pursued her passion for science reporting and launched the podcast with host Harvard Professor Ryan Adams. Through clear explanations of fundamental concepts and in depth interviews with those at the forefront of research, Talking Machines introduces the reality of machine learning to a wide audience. Early in its first season, the show has already become one of the most popular tech news podcasts on the global iTunes charts.

For More information Please visit

This is a combined slide/speaker video of Yoshua Bengio’s talk at NeurIPS 2019. Slide-synced non-YouTube version is here:

This is a clip on the Lex Clips channel that I mostly use to post video clips from the Artificial Intelligence podcast, but occasionally I post clips from other lectures by me or others. Hope you find these interesting, thought-provoking, and inspiring. If you do, please subscribe, click bell icon, and share.

Lex Clips channel:

Lex Fridman channel:

Artificial Intelligence podcast website:

Apple Podcasts:



Connect with on social media:
– Twitter:
– LinkedIn:
– Facebook:
– Instagram:
– Medium:

Zachary Lipton (Carnegie Mellon University)
Emerging Challenges in Deep Learning

Recent advances in machine learning techniques such as deep learning (DL) has rejuvenated data-driven analysis in aerospace and integrated building systems. DL algorithms have been successful due to the presence of large volumes of data and its ability to learn the features during the learning process.
The performance improvement is significant from the features learnt from DL techniques as compared to the hand crafted features. This talk demonstrates using deep belief networks (DBN), deep auto encoders (DAE), deep reinforcement learning (DRL) and generative adversarial networks (GAN) in five different aerospace and building systems applications: (i) estimation of fuel flow rate in jet engines, (ii) fault detection in elevator cab doors using smart phone, (iii) prediction of chiller power consumption in heating, ventilation, and air conditioning (HVAC) systems, (iv) material and structural characterization of aerospace parts, and (v) end-to-end control of high-precision additive manufacturing process.

Do you like this material?
See a lot of videos related to this topic for FREE at our AI Learning Accelerator –

#DeepLearning #Aerospace #ODSC

3:30 Deep Learning: Machine Learning via Large-scale Brain Simulations

51:03 Q&A

This presentation took place at the RE•WORK Deep Learning Summit in San Francisco on 28-29 January 2016:

Multimodal Question Answering for Language and Vision

Deep Learning has made tremendous breakthroughs possible in visual understanding and speech recognition. Ostensibly, this is not the case in natural language processing (NLP) and higher level reasoning. However, it only appears that way because there are so many different tasks in NLP and no single one of them, by itself, captures the complexity of language. I will talk about dynamic memory networks for question answering. This model architecture and task combination can solve a wide variety of visual and NLP problems, including those that require reasoning.

Richard Socher is the CEO and founder of MetaMind, a startup that seeks to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng and won the best Stanford CS PhD thesis award. He is interested in developing new AI models that perform well across multiple different tasks in natural language processing and computer vision.

He was awarded the Distinguished Application Paper Award at the International Conference on Machine Learning (ICML) 2011, the 2011 Yahoo! Key Scientific Challenges Award, a Microsoft Research PhD Fellowship in 2012 and a 2013 “Magic Grant” from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award.

MIT Introduction to Deep Learning 6.S191: Lecture 1
Foundations of Deep Learning
Lecturer: Alexander Amini
January 2020

For all lectures, slides, and lab materials:

Lecture Outline
0:00 – Introduction
4:14 – Course information
8:10 – Why deep learning?
11:01 – The perceptron
13:07 – Activation functions
15:32 – Perceptron example
18:54 – From perceptrons to neural networks
25:23 – Applying neural networks
28:16 – Loss functions
31:14 – Training and gradient descent
35:13 – Backpropagation
39:25 – Setting the learning rate
43:43 – Batched gradient descent
46:46 – Regularization: dropout and early stopping
51:58 – Summary

Subscribe to @stay up to date with new deep learning lectures at MIT, or follow us on @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

A small 2D simulation in which cars learn to maneuver through a course by themselves, using a neural network and evolutionary algorithms.

Also check out my other project “AI Learns to Park”:

Two AI fight for the same Parking Spot:

Interested in how Neural Networks work? Have a look at my one-minute-explanation:

This simulation was implemented in Unity. You can find detailed information about how this simulation works, as well as a link to the entire source code on my website:

Don’t miss any future videos, by subscribing to my channel.
Follow me on Twitter:

#MachineLearning #Evolution #GeneticAlgorithm

Ever wondered how to take a photo from a galaxy which is millions of light-years away? Lean back and enjoy the beauty of our universe…

#TXGroup #SDSC #ETHZ #EPFL #TXConference #TXConference2020

TX | Conference is the yearly digital exchange conference focused on Marketing, Product and Technology within TX Group AG.
This year the TX | Conference has transcended physical reality into the Metaverse!

TX Group is a network of media and platforms offering information, orientation, entertainment and services to over 80 percent of the Swiss population every day.

Anima Anandkumar (NVIDIA): Large-scale Machine Learning: Deep, Distributed and Multi-Dimensional

This trailer is for the Deep learning Specialization. If you want to break into Artificial Intelligence (AI), this specialization will help you do so. Enroll today at to get access to the specialization!

About this specialization:
Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.

You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.

AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.

We will help you master Deep Learning, understand how to apply it, and build a career in AI.

Visit to learn more!

Keep in touch with Coursera!

Recognizing or Detecting Emotions from Faces has never been an easy task. For making this easy, I’m presenting this video. With Deep Learning and Computer Vision, I’ve tried to achieve this task, however other algorithms may also be used. This video tells you how to implement such a cool project as well as tries to convey the fact that Deep Learning has been proven very helpful so far.

I’ve used Tensorflow and OpenCV in this video. For more information, watch the video till the end and follow the code for this video on GitHub:

*** EDIT: Uploaded the trained model file and labels ***

Thanks guys for watching.
Please Like, Subscribe and Keep Supporting Me!


If you find my videos helpful, I’d like your support on Patreon 🙂

Love u Guys. Keep Going…

#MachineLearning #FacialExpressionDetection #Python

Presented at Cognitive Computational Neuroscience (CCN) 2017 ( held September 6-8, 2017.

**AI and Deep Learning with TensorFlow: **
This video on Deep Learning Projects will provide you with a list of the top open-source deep learning projects you must try in 2019.

Lung Cancer Detection:
Google Tulip:
Image Enlarging:
IBM Watson:
Check out our playlist for more videos:

Subscribe to our channel to get video updates. Hit the subscribe button above.

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!

About the course:

Edureka’s Deep Learning in TensorFlow with Python Certification Training is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies.



Deep Learning in TensorFlow with Python Training is designed by industry experts to make you a Certified Deep Learning Engineer. The Deep Learning in TensorFlow course offers:
In-depth knowledge of Deep Neural Networks
Comprehensive knowledge of various Neural Network architectures such as Convolutional Neural Network, Recurrent Neural Network, Autoencoders
Implementation of Collaborative Filtering with RBM
The exposure to real-life industry-based projects which will be executed using TensorFlow library
Rigorous involvement of an SME throughout the AI & Deep Learning Training to learn industry standards and best practices

Why should one go for this course?

Deep Learning is one of the most accelerating and promising fields, among all the technologies available in the IT market today. To become an expert in this technology, you need structured training with the latest skills as per current industry requirements and best practices.

Besides strong theoretical understanding, you will be working on various real-life data projects using different neural network architectures as a part of the solution strategy.

Additionally, you will receive guidance from a Deep Learning expert who is currently working in the industry on real-life projects.


Skills that you will be learning:

Deep Learning and TensorFlow Concepts
Working with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)
Proficiency in Long short-term memory (LSTM)
Implementing Keras, TFlearn, Autoencoders
Implementing Restricted Boltzmann Machine (RBM)
Knowledge of Neural Networks & Natural Language Processing (NLP)
Using Python with TensorFlow Libraries
Perform Text Analytics
Perform Text Processing

Who should go for this course?

The TensorFlow with Python Training is for all the professionals who are passionate about Deep Learning and want to go ahead and make their career as a Deep Learning Engineer. It is best suited for individuals who are:

Developers aspiring to be a ‘Data Scientist’
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Deep Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
Analysts wanting to understand Data Science methodologies

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


*** Machine Learning Podcast – ***

Please write back to us at or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information

DARPA SUPERHIT 2021 Play Now!Close


(StoneBridge Mix)

Play Now!