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 ***

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#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.

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1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work
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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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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.


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#AI vs #ML vs #DL
This Lecture Explains The Differences Among Artificial Intelligence,Machine Learning & Deep Learning (AI vs ML vs DL) .
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Moving beyond supervised deep learning

Watch Yoshua Bengio, Professor of Computer Science and Operations Research at Université de Montréal on stage at World Summit AI Americas 2019.

Einstein offers a variety of Deep Learning APIs that allow developers of all skill levels to leverage the power of image recognition and natural language processing to build AI-enabled apps fast. With Einstein, you can iterate quickly to build the most accurate models without the complexity of management and maintenance. Join this session and learn how to use Einstein APIs to make your applications smarter with deep learning-powered image recognition and natural language processing.

An introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.

Blog post:

0:00 – Introduction
0:53 – Deep learning in one slide
4:55 – History of ideas and tools
9:43 – Simple example in TensorFlow
11:36 – TensorFlow in one slide
13:32 – Deep learning is representation learning
16:02 – Why deep learning (and why not)
22:00 – Challenges for supervised learning
38:27 – Key low-level concepts
46:15 – Higher-level methods
1:06:00 – Toward artificial general intelligence

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Lecture on most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA benchmark results, but rather a set of highlights of machine learning and AI innovations and progress in academia, industry, and society in general. This lecture is part of the MIT Deep Learning Lecture Series.


0:00 – Introduction
0:33 – AI in the context of human history
5:47 – Deep learning celebrations, growth, and limitations
6:35 – Deep learning early key figures
9:29 – Limitations of deep learning
11:01 – Hopes for 2020: deep learning community and research
12:50 – Deep learning frameworks: TensorFlow and PyTorch
15:11 – Deep RL frameworks
16:13 – Hopes for 2020: deep learning and deep RL frameworks
17:53 – Natural language processing
19:42 – Megatron, XLNet, ALBERT
21:21 – Write with transformer examples
24:28 – GPT-2 release strategies report
26:25 – Multi-domain dialogue
27:13 – Commonsense reasoning
28:26 – Alexa prize and open-domain conversation
33:44 – Hopes for 2020: natural language processing
35:11 – Deep RL and self-play
35:30 – OpenAI Five and Dota 2
37:04 – DeepMind Quake III Arena
39:07 – DeepMind AlphaStar
41:09 – Pluribus: six-player no-limit Texas hold’em poker
43:13 – OpenAI Rubik’s Cube
44:49 – Hopes for 2020: Deep RL and self-play
45:52 – Science of deep learning
46:01 – Lottery ticket hypothesis
47:29 – Disentangled representations
48:34 – Deep double descent
49:30 – Hopes for 2020: science of deep learning
50:56 – Autonomous vehicles and AI-assisted driving
51:50 – Waymo
52:42 – Tesla Autopilot
57:03 – Open question for Level 2 and Level 4 approaches
59:55 – Hopes for 2020: autonomous vehicles and AI-assisted driving
1:01:43 – Government, politics, policy
1:03:03 – Recommendation systems and policy
1:05:36 – Hopes for 2020: Politics, policy and recommendation systems
1:06:50 – Courses, Tutorials, Books
1:10:05 – General hopes for 2020
1:11:19 – Recipe for progress in AI
1:14:15 – Q&A: what made you interested in AI
1:15:21 – Q&A: Will machines ever be able to think and feel?
1:18:20 – Q&A: Is RL a good candidate for achieving AGI?
1:21:31 – Q&A: Are autonomous vehicles responsive to sound?
1:22:43 – Q&A: What does the future with AGI look like?
1:25:50 – Q&A: Will AGI systems become our masters?

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World-known pioneer Yoshua Bengio (MILA) discusses the challenges ahead for deep learning toward artificial intelligence.

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Andrew Ng is one of the most impactful educators, researchers, innovators, and leaders in artificial intelligence and technology space in general. He co-founded Coursera and Google Brain, launched,, and the AI fund, and was the Chief Scientist at Baidu. As a Stanford professor, and with Coursera and, he has helped educate and inspire millions of students including me.

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0:00 – Introduction
2:23 – First few steps in AI
5:05 – Early days of online education
16:07 – Teaching on a whiteboard
17:46 – Pieter Abbeel and early research at Stanford
23:17 – Early days of deep learning
32:55 – Quick preview:,, and AI fund
33:23 – how to get started in deep learning
45:55 – Unsupervised learning
49:40 – (continued)
56:12 – Career in deep learning
58:56 – Should you get a PhD?
1:03:28 – AI fund – building startups
1:11:14 – – growing AI efforts in established companies
1:20:44 – Artificial general intelligence

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Juris Puce from KleinTech Services will be giving presentation on actual uses of machine vision in combination with deep learning as for inspection of passing trains and wagons on railroad.
Juris will provide information on what kind of tasks KleinTech Services and 4SmartStreets teams are solving as well as information on limitations of vision and deep learning and some ways to possibly overcome them by using common sense.
(Language — Latvian)
Tags: Deep learning, Machine Vision, Artificial Intelligence
Juris is a CEO of KleinTech Services, Partner in 4SmartStreets and a technology enthusiast with main focuses on finding ways to use technology in optimisation of different areas. The main expertise Juris has is in areas of DL/Machine vision and information security. Juris has more than 15 years of experience in technology and holds large amounts of different certifications, but more importantly has a lot of practical experience managing teams in related fields.

Deep nets are the current state of the art in pattern recognition, but they build upon a decades-old technology of neural networks talked about in the past module. Deep learning is a machine learning method based on neural networks. What distinguishes deep learning from the more general approach of neural networks is its use of multiple layers within the network to represent different levels of abstraction. Deep learning algorithms use a cascading structure with multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. In this way, they learn multiple levels of representation that correlate to different levels of abstraction.
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This Deep Learning tutorial covers all the essential Deep Learning frameworks that are necessary to build AI models. In this video, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languages used to build the frameworks, the different companies that use these frameworks, the characteristics of these Deep Learning frameworks, and type of models that were built using these frameworks. Now, let us get started with understanding the different popular Deep Learning frameworks being used in industries.

Below are the different Deep Learning frameworks we’ll be discussing in this video:
1. TensorFlow (01:28)
2. Keras (02:54)
3. PyTorch (05:02)
4. Theano (06:30)
5. Deep Learning 4 Java (07:55)
6. Caffe (09:51)
7. Chainer (11:29)
8. Microsoft CNTK (13:48)

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#DeepLearningFrameworks #DeepLearningTutorial #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse

Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.

Why Deep Learning?

It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.

And according to, the median salary for engineers with deep learning skills tops $120,000 per year.

You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:

1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence

There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:

1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning

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Dato GraphLab is a good software platform for Deep Learning projects that require graph analytics and other important algorithms. It provides two deep nets, sophisticated data munging, an intuitive UI, and built-in enhancements for handling big data.

Deep Learning TV on

Dato GraphLab currently offers a vanilla MLP and a convolutional net. An important feature of the platform is the Graph Analytics toolset, which can be run alongside the deep learning models. Other provided tools include text analytics, a recommender, classification, regression, and clustering. You can also point GraphLab at multiple data sources in order to train data loads.

The platform has an intuitive UI along with an extension called the GraphLab Canvas. This extension offers highly sophisticated visualizations of your models.

Even though GraphLab needs to be deployed and maintained on your own hardware, the platform comes with many performance enhancements that speed up training on big data sets.
GraphLab offers three different types of built-in storage – tabular, columnar, and graph. In addition, the platform provides built-in GPU support which is extremely beneficial for training. You can also set up each type of model as a service that can be accessed programmatically through an API.

Under what circumstances would you use a graph in your deep learning projects? Please comment and share your thoughts.

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Professor Christopher Manning, Stanford University & Margaret Mitchell, Google AI

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

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May 1, 2017 – In less than a decade, the field of “artificial intelligence” or “AI” has been jolted by the extraordinary and unexpected success of a set of techniques now called “Deep Learning”. These methods (with some other related rapidly advancing technologies) already exceed average human performance in some kinds of image understanding; spoken word recognition and language translation; and indeed some tasks, like the game of Go, previously thought to require generalized human intelligence. AI may soon replace humans in driving cars, coding new software, robotic caregiving, and making healthcare decisions. The societal implications are enormous.

Suchi Saria, Assistant Professor of Computer Science, Johns Hopkins University presents “The Impact of Artificial Intelligence on Healthcare”

A Stationers’ Company Digital Media Group event at Stationers’ Hall on ‘Artificial Intelligence – Our Future: The Impact on the Content and Communication Industries’

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Watch this free webinar to get started developing applications with advanced AI and computer vision using NVIDIA’s deep learning tools, including TensorRT and DIGITS.

By watching this webinar, you’ll learn:
1. How to use NVIDIA’s deep learning tools such as TensorRT and DIGITS
2. About various types of neural network-based primitives available as a building blocks, deployable onboard intelligent robots and drones using NVIDIA’s Jetson Embedded Platform.
3. Realtime deep-learning solutions for image recognition, object localization, and segmentation
4. Training workflows for customizing network models with new training datasets and emerging approaches to automation like deep reinforcement learning and simulation is a software platform that offers a host of machine learning algorithms, as well as one deep net model. It also provides sophisticated data munging, an intuitive UI, and several built-in enhancements for handling data. However, the tools must be run on your own hardware.

Deep Learning TV on
Twitter: was founded by SriSatish Ambati, Cliff Click, and Arno Candel. In addition to its only deep net – a vanilla MLP – the platform offers a variety of models like GLM, Distributed Random Forest, Naive Bayes, a K-Means clustering model, and a few others. can be linked to multiple data sources in order to train data loads.

The UI is highly intuitive, but you can also work with the tools through other apps like Tableau or Excel. These interfaces allow you to set up a deep net by configuring its hyper-parameters. needs to be deployed and maintained on your own hardware, which may be a limiting factor. However, the platform comes with many performance enhancements like in-memory map-reduce, columnar compression, and distributed parallel processing. Depending on your hardware’s capabilities, training on big data sets could be completed in a reasonable amount of time. As an added note, it’s unclear whether or not GPU support is a built-in feature at this point in time.

Do you have any experience with the platform? Please comment and share your thoughts.

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Kuhelee Chandel
Geospatial Media and Communications

Director General

Ed Parsons
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President and CEO
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Managing Director
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Dr Peter Woodgate
Chief Executive Officer
CRC for Spatial Information

#gis #IoT #DeepLearning

Hey Guys,
Hope you enjoying my AI tutorials using Keras and Tensorflow.

This is the video for facial emotion recognition using CNN.
Transfer learning is the best way to perform such a complicated task.
For this task, we will classify the emotions from the frame coming directly through your webcam or any external live camera.

This is a realtime emotion detection easy tutorial using python and Keras.

You can use this video as realtime emotion detection using python.

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Facial Landmark Detection OpenCV Too Easy Tutorial

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Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired towards an Organisation, measuring impact of Promotions in Social Media, and (Aspect based) Sentiment Analysis of Reviews. These techniques can not only help gauge the customer’s feedback, but also can help in providing users a better experience.

Traditional solutions focused on heavy domain-specific Feature Engineering, and thats exactly where Deep Learning sounds promising!

We will depict our foray into Deep Learning with these classes of Applications in mind. Specifically, we will describe how we tamed Deep Convolutional Neural Network, most commonly applied to Computer Vision, to help classify (short) texts, attaining near-state-of-the-art results on several SemEval tasks consistently, and a few tasks of importance to Flipkart.

In this talk, we plan to cover the following:

Basics of Deep Learning as applied to NLP: Word Embeddings and its compositions a la Recursive Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.

New Experimental results on an array of SemEval / Flipkart’s internal tasks: e.g. Tweet Classification and Sentiment Analysis. (As an example we achieved 95% accuracy in binary sentiment classification task on our datasets – up from 85% by statistical models)

Share some of the learnings we have had while deploying these in Flipkart!

Here is a mindmap explaining the flow of content and key takeawys for the audience:

We have decided to open source the code for this talk as a toolkit. Feel free to use it to train your own classifiers, and contribute!

An AI learns to park a car in a parking lot in a 3D physics simulation. The simulation was implemented using Unity’s ML-Agents framework ( The AI consists of a deep Neural Network with 3 hidden layers of 128 neurons each. It is trained with the Proximal Policy Optimization (PPO) algorithm, which is a Reinforcement Learning approach.

Basically, the input of the Neural Network are the readings of eight depth sensors, the car’s current speed and position, as well as its relative position to the target. The outputs of the Neural Network are interpreted as engine force, braking force and turning force. These outputs can be seen at the top right corner of the zoomed out camera shots.

The AI starts off with random behaviour, i.e. the Neural Network is initialized with random weights. It then gradually learns to solve the task by reacting to environment feedback accordingly. The environment tells the AI whether it is doing good or bad with positive or negative reward signals.
In this project, the AI is rewarded with small positive signals for getting closer to the parking spot, which is outlined in red, and gets a larger reward when it actually reaches the parking spot and stops there. The final reward for reaching the parking spot is dependent on how parallel the car stops in relation to the actual parking position. If the car stops in a 90° angle to the actual parking direction for instance, the AI will only be rewarded a very small amount, relative to the amount it would get for stopping completely parallel to the actual direction.
The AI is penalized with a negative reward signal, when it either drives further away from the parking spot or if it crashes into any obstacles.

The training process shown in this video took about 23 hours on a computer with an i5 (7th or 8th gen) and a GTX 1070 with 100x simulation speed.

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Two AI fight for the same Parking Spot:

Neural Networks Explained in a Minute:

Cars learn to maneuver Parcour with Genetic Algorithm:

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Timelapse Music: “The Elevator Bossa Nova”
Comedic Background: “Jazz Comedy”
Outro: “All That”

#ArtificialIntelligence #MachineLearning #ReinforcementLearning #AI #NeuralNetworks

🔥Intellipaat Artificial Intelligence Master’s course:
In this video you will learn about the difference between ai vs machine learning vs deep learning also known as ai vs ml vs dl. Most of the people have this doubt about the differences between machine learning vs artificial intelligence, ai vs dl, deep learning vs machine learning, ai vs machine learning so we have come up with this video tutorial for you to learn and become expert in these technologies. I bet you won’t get a comprehensive detailed video between deep learning vs machine learning vs artificial intelligence on YouTube.
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📝Following topics are covered in this video:
AI vs ML vs DL – 0:54
Machine Learning & It’s Types – 4:29
Types of Supervised Learning – 5:19
Classification Algorithm – Decision Tree – 6:54
Use Cases of Supervised Learning – 8:06
Unsupervised Learning – 9:55
Unsupervised Algorithm – K-means Clustering – 10:32
Use Cases of Unsupervised Learning – 11:25
Reinforcement Learning – 12:10
Use Cases of Reinforcement Learning – 13:06
Limitations of Machine Learning – 15:19
Automatic Feature Extraction with Deep Learning – 16:04
Deep Learning with Artificial Neural Networks – 16:35
Perceptron – How does it works? – 17:38
Why do we need weights? – 18:31
Perceptron Training Algorithm – 19:09
Deep Learning Application – 19:54
Quiz 1 – 20:44
Quiz 2 – 21:03

In our Artificial Intelligence Master’s course, you will learn about Ai, Machine Learning, Deep learning, tensorflow and this will help you become a successful AI architect in future. You can get more details about our course at –

All Intellipaat trainings are provided by Industry experts and is completely aligned with industry standards and certification bodies.

If you’ve enjoyed this artificial intelligence vs machine learning vs deep learning video, Like us and Subscribe to our channel for more informative tutorials.

Got any questions about artificial intelligence, machine learning and deep learning? Ask us in the comment section below.
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Why Artificial Intelligence is important?

Artificial Intelligence is taking over each and every industry domain. Machine Learning and especially Deep Learning are the most important aspects of Artificial Intelligence that are being deployed everywhere from search engines to online movie recommendations. Taking the Intellipaat deep learning training & Artificial Intelligence Course can help professionals to build a solid career in a rising technology domain and get the best jobs in top organizations.

Why machine learning is important?

Machine learning might just be one of the most important fields of science that we are just moving towards. It differs from other science in the sense that this is one of the one domains where the input and output are not directly correlated and neither do we provide the input for every task that the machine will perform. It is more about mimicking how humans think and solving real world problems like humans without actually the intervention of humans. It focuses on developing computer programs that can be taught to grown and change when exposed to data.

Why should you opt for an Artificial Intelligence career?

If you want to fast-track your career then you should strongly consider Artificial Intelligence. The reason for this is that it is one of the fastest growing technology. There is a huge demand for professionals in Artificial Intelligence. The salaries for A.I. Professionals is fantastic.There is a huge growth opportunity in this domain as well. Hence this Intellipaat Artificial Intelligence tutorial is your stepping stone to a successful career!

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Deep Learning Vs Machine Learning | AI Vs Machine Learning Vs Deep Learning
Hello and welcome to Acadgild’s tutorial on data science.
In this video, we explain the difference between three key concepts artificial intelligence vs machine learning vs deep learning – to understand how they relate to the field of data science.

First up, artificial intelligence or AI! What is it?
Artificial intelligence is simply any code, technique or algorithm that enables machines to mimic, develop and demonstrate human cognition or behavior.

We are in, what many refer to as, the era of “weak AI”. The technology is still in its infancy and is expected to make machines capable of doing anything and everything humans do, in the era of “strong AI”.
To transition from weak AI to strong AI, machines need to learn the ways of humans. The techniques and processes, which help machines in this endeavor are broadly categorized under machine learning.
Machines learn in predominantly two ways. Their learning is either supervised or unsupervised.
In supervised learning, machines learn to predict outcomes with help from data scientists.
In unsupervised learning, machines learn to predict outcomes on the go by recognizing patterns in input data.

When machines can draw meaningful inferences from large volumes of data sets, they demonstrate the ability to learn deeply.
Deep learning requires artificial neural networks (ANNs), which are like the biological neural networks in humans. These networks contain nodes in different layers that are connected and communicate with each other to make sense of voluminous input data.

Deep learning is a subset of machine learning, which in turn, is a subset of artificial intelligence.
The three technologies help scientists and analysts interpret tons of data and are hence crucial for the field of data science.
To learn more about these technologies, subscribe to Acadgild’s blog and Youtube channel. To become an expert, join one of our courses.
Thank you for watching and happy learning!

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ممكن هو اول فيديو عن التعلم بعمق DEEP LEARNING او التعلم المعمق بالدارجة الجزائرية و العربية مع الانجليزية “المهم التعلم”. انتظرونا غدا الثلاثاء في فيديو جديد عن التعلم المعمق , تعلم الالة و الذكاء الاصطناعي ارضاءا لرغباتكم عبر قناة اليوتوب مباشر سيليكون فالي.

برنامج و التطبيقات المجانية التعلم المعمق لشركة انتل
التعليم باشراف و التعليم بغير اشراف
مباشر سيليكون فالي
الذكاء الاصطناعي
التعلم بعمق DEEP LEARNING
لماذا نستعمل التعلم بعمق | ? WHY DEEP LEARNING
استعمال الذكاء الاصطناعي
الببيانات الضخمة BIG DATA
الذكاء الاصطناعي | تعليم الالة | التعلم بعمق
الفرق بين التدريب و الاستدلال | Training and Inference
منصات التعلم بعمق | DEEP LEARNING PLATFORMS

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Part 2 –
Part 3 –

In the first of a four-part series, Yoshua Bengio opens his hour-long Keynote from October 2019 discussing the current state of Deep Learning and how Human-level AI capabilities have been worked toward in 2019.

Yoshua’s opening remarks proclaimed that there are principles giving rise to intelligence, both machine or animal, which can be described using the laws of physics. That is, that our intelligence is not gained through a big bag of tricks, but rather the use of mechanisms used to specifically acquire knowledge. Similar to the laws of physics, we should consider understanding the physical world, mostly by having figured out the laws of physics, not just by describing its consequences.

Join pioneers like Yoshua at RE•WORK events in 2020! See the full event listing here –

Can’t wait for the next section of Professor Bengio’s talk? You can see a high-level overview here –

NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet aims to provide many of the tools, functionality and implementations that are essential for medical image analysis but missing from standard general purpose toolkits. Due to its modular structure, NiftyNet makes it easier to share networks and pre-trained models, adapt existing networks to new imaging data, and quickly build solutions to your own image analysis problems. This talk will explore the whys, the whats and the hows of this open source framework.

I have a BSc in Biomedical Engineering (2006) and an MSc in Medical Electronics and Signal Processing for Biomedical Engineering (2008) from the Universidade do Minho, Portugal, followed by a PhD (2008-2012) and PostDoc (2012-2015) in medical image analysis, machine learning and biomarker development between CMIC and the Dementia Research Centre at UCL. In June 2015 I have been appointed Lecturer in Quantitative Neuroradiology at the Translational Imaging Group, part of CMIC, in collaboration with the National Hospital for Neurology and Neurosurgery, working on developing, translating and integrating artificial intelligence-based quantitative imaging biomarkers into the clinical environment.

Intelligent real time applications are a game changer in any industry. Deep Learning is one of the hottest buzzwords in this area. New technologies like GPUs combined with elastic cloud infrastructure enable the sophisticated usage of artificial neural networks to add business value in real world scenarios. Tech giants use it e.g. for image recognition and speech translation. This session discusses how any company can leverage deep learning in real time applications.

The session demos how to deploy Deep Learning models built with TensorFlow, DeepLearning4J or H2O into real time applications to do predictions on new events. The Apache Kafka open source ecosystem can be used to train, apply and monitor deep learning models in a highly scalable and performant way. The examples focus on Apache Kafka and Apache Kafka’s Streams API.

Links and further reading:
– Github:
– Confluent Blog Post:
– Slides:
– Video (more detailed about Kafka + Machine Learning):


Confluent, founded by the creators of Apache™ Kafka™, enables organizations to harness business value of live data. The Confluent Platform manages the barrage of stream data and makes it available throughout an organization. It provides various industries, from retail, logistics and manufacturing, to financial services and online social networking, a scalable, unified, real-time data pipeline that enables applications ranging from large volume data integration to big data analysis with Hadoop to real-time stream processing. To learn more, please visit

Magnimind Academy TV Presents – Deep Learning 101: Artificial Intelligence Based On The Brain – Tyler Suard, April 7th, 2020

In the last few years, AI has made some huge strides, all thanks to Deep Learning.

“Deep Learning” is a phrase that gets thrown around a lot, and is often synonymous with AI. What does it mean, and what is so “Deep” about it? In this class, we will explore this new and exciting type of artificial intelligence, so revolutionary that it has led to breakthroughs like computer vision, self-driving cars, and autonomous drones.

About Magnimind Academy
Magnimind helps people to experience a career change or improve developer skills with its enthusiastic team and 10+ years experienced lecturers. We organize regular courses, weekly boot camps and daily meet-ups to transfer the knowledge.

We select promising applicants from our wide range of admissions and try to help them as much as possible not only in education process but also after finishing the program to find the best jobs with that knowledge.

As Magnimind team, we believe the power of integrity with the information age. In that way, we are trying to become a source code for people to excel in their endeavors.

Magnimind’s Mission:

We create opportunities for people to comply with the technology and help them to improve that technology for the good of the World. Our main motivation is to serve for the good and educate people in the direction of leading innovation. We provide chances for people to adjust what is new or create new things with the knowledge. In that way, we are helping our participants to build their future, rewire their mindset and provide a chance to change the way they live. Our enthusiastic team believe the power of change and the innovation.

Magnimind’s Vision:

We are serving with the purpose of creating a different World that is wiser, and open to the innovation. We believe the World will be a better place with the help of science and belief of human beings that they can achieve everything with the intention of serving for the good.

Follow Magnimind Academy

The field of natural language processing has undergone many big changes during the past years. In this introductory talk we will briefly discuss what the biggest challenges in natural language processing are, and then dive into an overview of the most important deep learning milestones in NLP. We will namely cover word embeddings, recurrent neural networks for language modeling and machine translation, and the recent boom of Transformer-based models.


Jiří Materna: He is a machine learning expert with machine learning experience in industry since 2007. After finishing his Ph.D., he was working as the head of research at and now offers machine learning solutions and consulting as a freelancer. He is the founder and lecturer at Machine Learning College and the organizer of an international conference Machine Learning Prague.

Location / Místo: Prague, Czech Republic


There’s a discussion going on about the topic we are covering today: what’s the difference between AI and machine learning and deep learning. (Get our free list of the worlds best AI newsletters right here 👉

Very frequently, press coverage and even practitioners of analytics use the terms Artificial Intelligence and Machine Learning interchangeably. Disregarding the difference between AI and machine learning and deep learning.

However, these three concepts do not represent the same. In this video, we are going to break this down for you, giving you examples of use cases making the difference between ai and machine learning and deep learning more clear.

Any device that perceives its environment and takes actions to maximize its chances of success, can be said to have some kind of artificial intelligence, more frequently referred to as A.I.

More specifically, when a machine has “cognitive” capabilities, such as problem solving and learning by example it is usually associated with A.I.

Artificial Intelligence has three different levels:

Narrow AI: when a computer can perform one task much better than a human; this is where we stand nowadays.

2. General AI: when a machine can successfully perform any given intellectual task that a human being can too

3. Strong AI: when machines can beat humans in many of tasks.

Machine Learning is a subset of AI.
This is what most applications of AI in business rely on currently. Want to know more about how businesses are applying AI? Watch this video, in which we cover a list of them:

And finally, as a subset of machine learning, there’s Deep Learning. It is called “deep” because it makes use of deep artificial neural networks.

Also discussed in this video:

Difference between ai and machine learning
Difference between ai and machine learning and deep learning
Artificial intelligence
Machine learning
Deep learning
Difference AI ML
Difference AI machine learning
Difference ai machine learning deep learning


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