Deephi Tech is a FPGA deep learning platform provider for drones, robotics, surveillance cameras and data center applications. DeePhi platforms are based on Xilinx All Programmable FPGAs and SoCs, which provide the ideal combination of flexibility, high performance, low latency and low power consumption.

This video on Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We’ll also implement a neural network manually. Finally, we’ll code a neural network in Python using TensorFlow.

Below topics are explained in this Deep Learning with Python tutorial:
1. What is Deep Learning (01:56)
2. Biological versus Artificial Intelligence (02:45)
3. What is a Neural Network (04:09)
4. Activation function (08:49)
5. Cost function (14:08)
6. How do Neural Networks work (16:05)
7. How do Neural Networks learn (18:58)
8. Implementing the Neural Network (20:26)
9. Gradient descent (23:21)
10. Deep Learning platforms (24:48)
11. Introduction to TensoFlow (26:00)
12. Implementation in TensorFlow (28:56)

To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1

To access the slides, click here: https://www.slideshare.net/Simplilearn/deep-learning-with-python-deep-learning-and-neural-networks-deep-learning-tutorial-simplilearn/Simplilearn/deep-learning-with-python-deep-learning-and-neural-networks-deep-learning-tutorial-simplilearn

Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip

#DeepLearningWithPython #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.

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

Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Deep-Learning-with-Python-fcD6YeEYKNg&utm_medium=Tutorials&utm_source=youtube

For more information about Simplilearn’s courses, visit:
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– Website: https://www.simplilearn.com

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Prateek Joshi, Founder of Pluto AI, talks about how we can use deep learning to analyze time-series data generated by the ecosystem of internet-connected sensors, collectively referred to as Internet of Things (IoT).

Prateek Joshi is an Artificial Intelligence researcher, a published author of 4 books, and a TEDx speaker. He is the founder of Pluto AI, a Silicon Valley startup building an analytics platform for water management based on Deep Learning. His work in this field has led to patents, tech demos, and research papers at IEEE conferences. He actively blogs at www.prateekjoshi.com on topics such as Artificial Intelligence, Python programming, and abstract mathematics. The blog has received more than 1 million page views from 200+ countries.

Prateek has been an invited speaker at technology and entrepreneurship conferences. He has also been featured as a guest author in prominent tech magazines. He is an avid coder and has won many hackathons. He graduated from University of Southern California with a Masters degree specializing in Artificial Intelligence. He was elected to become a member of the Honors Society for academic excellence and an ambassador for the school of engineering. He has worked at companies such as Nvidia and Microsoft Research. You can learn more about him on his personal website at www.prateekj.com.

Stanford Professor Richard Socher gives a TEDX talk on the present and future of Deep Learning and AI. Implement Deep Learning: http://bit.ly/2BDTHcs

For the full episode Where AI is today and where it’s going. | Richard Socher | TEDxSanFrancisco click here: https://www.youtube.com/watch?v=8cmx7V4oIR8

For more videos like this:
ARTIFICIAL INTELLIGENCE at SKYMIND
https://www.youtube.com/watch?v=LRcbfqHGzj4

This Deep Learning tutorial is designed for beginners who want to learn Deep Learning from scratch. We will look at where Deep Learning is applied and what exactly this term means. We’ll see how Deep Learning, Machine Learning, and AI are different and why Deep Learning even came into the picture. We will then proceed to look at Neural Networks, which are the core of Deep Learning. Before we move into the working of Neural Networks, we’ll cover activation and cost functions. The video will also introduce you to the most popular Deep Learning platforms. We wrap it up with a demo in TensorFlow to predict if a person receives a salary above or below 50k. Now, let us get started and understand Deep Learning in detail.

Below topics are explained in this Deep Learning tutorial:
1. Applications of Deep Learning
2. What is Deep Learning
3. Why is Deep Learning important
4. What are Neural Networks
5. Activation function
6. Cost function
7. How do Neural Networks work
8. Deep Learning platforms
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow

To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1

Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip

#DeepLearningTutorial #DeepLearning #DeepLearningAndNeuralNetworks #WhatIsDeepLearning#DeepLearningCourse #Simplilearn

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. According to payscale.com, 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

Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Deep-Learning-Tutorial-Cq_P8kJgjvI&utm_medium=Tutorials&utm_source=youtube

For more information about Simplilearn’s courses, visit:
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– Website: https://www.simplilearn.com

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Get the iOS app: http://apple.co/1HIO5J0

( ** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka video on “Deep Learning Frameworks” (https://goo.gl/27nAwR) 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

Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV

Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz

————————————-

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#PyTorch #TensorFlow #DeepLearning #Python
————————————-

Got a question on the topic?
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.

– – – – – – – – – – – – – –

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.

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!

– – – – – – – – – – – – – –

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 sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free).

Organizers: Jason (Jinquan) Dai Location: Room 151 A-C & G Time: 0900-1200 (Half Day — Morning) Description: Recent breakthroughs in artificial intelligence applications have brought deep learning to the forefront of new generations of data analytics. In this tutorial, we will pre-sent the practice and design tradeoffs for building large-scale deep learning applications (such as computer vision and NLP) for production data and workflow on Big Data platforms. In particular, we will provide an overview of emerging deep learn-ing frameworks for Big Data (e.g., BigDL, TensorFlow-on-Spark, Deep Learning Pipelines for Spark, etc.), present the underlying distributed systems and algorithms, and discuss innovative data analytics + AI application pipelines (with a focus on computer vision models and use cases) for Big Data platforms and workflows. Schedule: 0900 Motivation 0910 Overview 0930 Analytics Zoo for Spark and BigDL 1000 Morning Break 1030 Distributed Training and Inference 1100 Advanced Applications 1130 Real-World Applications 1150 Q&A

AI and deep learning will be front and center at GTC 2017 across key industries like healthcare and financial services. Register now: http://nvda.ws/2nrJyK4

Rising AI startups will discuss key technologies and discoveries. Innovative researchers will speak to critical breakthroughs accelerated with GPU-based deep learning. The NVIDIA Deep Learning Institute will offer hands-on technical training on the latest open-source frameworks and GPU-accelerated deep learning platforms.

François Chollet is the creator of Keras, which is an open source deep learning library that is designed to enable fast, user-friendly experimentation with deep neural networks. It serves as an interface to several deep learning libraries, most popular of which is TensorFlow, and it was integrated into TensorFlow main codebase a while back. Aside from creating an exceptionally useful and popular library, François is also a world-class AI researcher and software engineer at Google, and is definitely an outspoken, if not controversial, personality in the AI world, especially in the realm of ideas around the future of artificial intelligence. This conversation is part of the Artificial Intelligence podcast.

INFO:
Podcast website: https://lexfridman.com/ai
Full episodes playlist: http://bit.ly/2EcbaKf
Clips playlist: http://bit.ly/2JYkbfZ

EPISODE LINKS:
François twitter: https://twitter.com/fchollet
François web: https://fchollet.com/

OUTLINE:
0:00 – Introduction
1:14 – Self-improving AGI
7:51 – What is intelligence?
15:23 – Science progress
26:57 – Fear of existential threats of AI
28:11 – Surprised by deep learning
30:38 – Keras and TensorFlow 2.0
42:28 – Software engineering on a large team
46:23 – Future of TensorFlow and Keras
47:53 – Current limits of deep learning
58:05 – Program synthesis
1:00:36 – Data and hand-crafting of architectures
1:08:37 – Concerns about short-term threats in AI
1:24:21 – Concerns about long-term existential threats from AI
1:29:11 – Feeling about creating AGI
1:33:49 – Does human-level intelligence need a body?
1:34:19 – Good test for intelligence
1:50:30 – AI winter

CONNECT:
– Subscribe to this YouTube channel
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The Vienna Deep Learning Meetup and the Centre for Informatics and Society of TU Wien jointly organized an evening of discussion on the topic of Ethics and Bias in AI. As promising as machine learning techniques are in terms of their potential to do good, the technologies raise a number of ethical questions and are prone to biases that can subvert their well-intentioned goals.

Machine learning systems, from simple spam filtering or recommender systems to Deep Learning and AI, have already arrived at many different parts of society. Which web search results, job offers, product ads and social media posts we see online, even what we pay for food, mobility or insurance – all these decisions are already being made or supported by algorithms, many of which rely on statistical and machine learning methods. As they permeate society more and more, we also discover the real world impact of these systems due to inherent biases they carry. For instance, criminal risk scoring to determine bail for defendants in US district courts has been found to be biased against black people, and analysis of word embeddings has been shown to reaffirm gender stereotypes due to biased training data. While a general consensus seems to exist that such biases are almost inevitable, solutions range from embracing the bias as a factual representation of an unfair society to mathematical approaches trying to determine and combat bias in machine learning training data and the resulting algorithms.

Besides producing biased results, many machine learning methods and applications raise complex ethical questions. Should governments use such methods to determine the trustworthiness of their citizens? Should the use of systems known to have biases be tolerated to benefit some while disadvantaging others? Is it ethical to develop AI technologies that might soon replace many jobs currently performed by humans? And how do we keep AI and automation technologies from widening society’s divides, such as the digital divide or income inequality?

This event provides a platform for multidisciplinary debate in the form of keynotes and a panel discussion with international experts from diverse fields:

Keynotes:

– Prof. Moshe Vardi: “Deep Learning and the Crisis of Trust in Computing”
– Prof. Sarah Spiekermann-Hoff: “The Big Data Illusion and its Impact on Flourishing with General AI”

Panelists: Ethics and Bias in AI

– Prof. Moshe Vardi, Karen Ostrum George Distinguished Service Professor in Computational Engineering, Rice University
– Prof. Peter Purgathofer, Centre for Informatics and Society / Institute for Visual Computing & Human-Centered Technology, TU Wien
– Prof. Sarah Spiekermann-Hoff, Institute for Management Information Systems, WU Vienna
– Prof. Mark Coeckelbergh, Professor of Philosophy of Media and Technology, Department of Philosophy, University of Vienna
– Dr. Christof Tschohl, Scientific Director at Research Institute AG & Co KG

Moderator: Markus Mooslechner, Terra Mater Factual Studios

The evening will be complemented by networking & discussions over snacks and drinks.

More details: http://www.aiethics.cisvienna.com

This gives overview of the features and the deep learning frameworks made available on AMD platforms. The speaker also presents some ideas about performance parameters and ease of use of AMD software too.

Presenter

Dr. Prakash Raghavendra
PMTS (Software), AMD India Pvt Ltd
Bangalore

“Apache Spark is a powerful, scalable real-time data analytics engine that is fast becoming the de facto hub for data science and big data. However, in parallel, GPU clusters are fast becoming the default way to quickly develop and train deep learning models. As data science teams and data savvy companies mature, they will need to invest in both platforms if they intend to leverage both big data and artificial intelligence for competitive advantage.

This session will cover:
– How to leverage Spark and TensorFlow for hyperparameter tuning and for deploying trained models
– DeepLearning4J, CaffeOnSpark, IBM’s SystemML and Intel’s BigDL
– Sidecar GPU cluster architecture and Spark-GPU data reading patterns
– The pros, cons and performance characteristics of various approaches

You’ll leave the session better informed about the available architectures for Spark and deep learning, and Spark with and without GPUs for deep learning. You’ll also learn about the pros and cons of deep learning software frameworks for various use cases, and discover a practical, applied methodology and technical examples for tackling big data deep learning.

Session hashtag: #SFds14″

On Tuesday, September 25th, Jeff Dean, Head of Google AI and Google Brain, visited heidelberg.ai (http://heidelberg.ai) at the German Cancer Research Center in Heidelberg:

For the past seven years, the Google Brain team has conducted research on difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to many Google products. Our group has open-sourced the TensorFlow system, a widely popular system designed to easily express machine learning ideas, and to quickly train, evaluate and deploy machine learning systems. We have also collaborated closely with Google’s platforms team to design and deploy new computational hardware called Tensor Processing Units, specialized for accelerating machine learning computations. In this talk, I’ll highlight some of our research accomplishments, and will relate them to the National Academy of Engineering’s Grand Engineering Challenges for the 21st Century, including the use of machine learning for healthcare, robotics, and engineering the tools of scientific discovery. I’ll also cover how machine learning is transforming many aspects of our computing hardware and software systems.

This talk describes joint work with many people at Google.

Human-monitored security feeds are prohibitively expensive; why not get a Deep Science AI to watch your cameras and detect threats as they happen?

Subscribe to TechCrunch today: http://bit.ly/18J0X2e

TechCrunch is excited to announce the 19 startups pitching in the Startup Battlefield for TechCrunch Disrupt NYC 2017. Over the next three days on the most prestigious tech stage in the world, the Battlefield teams will compete for $50,000 and the coveted Disrupt Cup.

Watch more from Startup Battlefield here: https://www.youtube.com/playlist?list…

TechCrunch Disrupt is the world’s leading authority in debuting revolutionary startups, introducing game-changing technologies and discussing what’s top of mind for the tech industry’s key innovators.

In Lecture 14 we move from supervised learning to reinforcement learning (RL), in which an agent must learn to interact with an environment in order to maximize its reward. We formalize reinforcement learning using the language of Markov Decision Processes (MDPs), policies, value functions, and Q-Value functions. We discuss different algorithms for reinforcement learning including Q-Learning, policy gradients, and Actor-Critic. We show how deep reinforcement learning has been used to play Atari games and to achieve super-human Go performance in AlphaGo.

Keywords: Reinforcement learning, RL, Markov decision process, MDP, Q-Learning, policy gradients, REINFORCE, actor-critic, Atari games, AlphaGo

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture14.pdf

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Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

CEVA introduces a new DSP-based offering bringing deep learning and Artificial Intelligence (AI) capabilities to low-power embedded systems.

A comprehensive, scalable, integrated hardware and software silicon IP platform that is centered around a new imaging and vision DSP – the CEVA-XM6.

It allows developers to efficiently harness the power of neural networks and machine vision for smartphones, autonomous vehicles, surveillance, robots, drones and other camera-enabled smart devices.

For more information, visit http://www.ceva-dsp.com
or Email: info@ceva-dsp.com

When consumers experience AI/ML benefit from various sources in our daily life, enterprises are facing challenges when applying similar AI/ML techniques to transform business. In this session, we will share how Workday (Enterprise SaaS company on HCM and FIN) has identified specific business problem for ML to solve, collected enough data to prototype, and deployed the solution as part of Workday Application product available to all Workday customers in less than 18months. We will also share lessons learned from legal, privacy, and security aspect with Human-in-the-loop approach which is a critical part of the enterprise ML product development journey.

Can an AI learn to play the perfect game of Snake?
Huge thanks to Brilliant.org for supporting this channel, check them out: https://www.brilliant.org/CodeBullet

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Art created by @Dachi.art https://www.instagram.com/dachi.art

Intelligent real time applications are a game changer in any industry. This session explains how companies from different industries build intelligent real time applications. The first part of this session explains how to build analytic models with R, Python or Scala leveraging open source machine learning / deep learning frameworks like TensorFlow, DeepLearning4J or H2O.ai. The second part discusses the deployment of these built analytic models to your own applications or microservices by leveraging the Apache Kafka cluster and Kafka’s Streams API instead of setting up a new, complex stream processing cluster. The session focuses on live demos and teaches lessons learned for executing analytic models in a highly scalable, mission-critical and performant way.

Key takeaways for the audience:
– Insights are hidden in Historical Data on Big Data Platforms such as Hadoop
– Machine Learning and Deep Learning find these Insights by building Analytics Models
– Streaming Analytics uses these Models (without Redeveloping) to act in Real Time
– See different open source frameworks for Machine Learning and Stream Processing like TensorFlow, DeepLearning4J or H2O.ai
– Understand how to leverage Kafka Streams to use analytic models in your own streaming microservices
– Learn best practices for building and deploying analytic models in real time leveraging the open source Apache Kafka Streams platform

You can find the Java code examples and analytic models for H2O and TensorFlow in my Github project: https://github.com/kaiwaehner/kafka-streams-machine-learning-examples

CONNECT
Subscribe: http://youtube.com/c/confluent?sub_confirmation=1
Site: http://confluent.io
GitHub: https://github.com/confluentinc
Facebook: https://facebook.com/confluentinc
Twitter: https://twitter.com/confluentinc
Linkedin: https://www.linkedin.com/company/confluent

ABOUT CONFLUENT
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 http://confluent.io

Jeffrey is the CTO and part of the team of founders of Stratified Medical. He is a serial technologist, start-up founder, fund-raiser and deep R&D strategist in Big Data, Natural Language Processing, state-of-the-art Deep Learning and deployment of AI platforms at internet scale for Tier1 Silicon Valley companies. He has a doctorate in Machine Learning and Computer Vision and another 7 years of Post-Doctoral research experience in brain-inspired pattern recognition at Imperial College. He has successfully spun-out a start-up out of Imperial with multi-million VC investment and revenue from a big UK retailer within 10 months. He is now working in big data and advanced machine learning to leverage the totality of human knowledge, teaching machines to understand and reason, with the goal of making a real difference in the world. Author of over 45 articles in scientific journals and conferences, 3 granted patents in US and EU and 4 pending patents.

Professor Stuart Russel (UC Berkeley) is a pioneer in Artificial Intelligence. We talked about the future developments of AI, and their implications on our lives. Is the movie Terminator just science fiction? Not really. The technology is already here. Stuart Russel tells us more in this 10-minute interview.

Regard.

Emission produite par tomg conseils (http://tomg-conseils.com/) pour Regards Connectés.

Avec le soutien de Petit Web et Frenchweb.

All rights reserved, tomg conseils 2018

Don’t forget to credit http://regards-connectes.fr/

Home page: https://www.3blue1brown.com/
Brought to you by you: http://3b1b.co/nn2-thanks
And by Amplify Partners.

For any early stage ML startup founders, Amplify Partners would love to hear from you via 3blue1brown@amplifypartners.com

To learn more, I highly recommend the book by Michael Nielsen
http://neuralnetworksanddeeplearning.com/
The book walks through the code behind the example in these videos, which you can find here:
https://github.com/mnielsen/neural-networks-and-deep-learning

MNIST database:
http://yann.lecun.com/exdb/mnist/

Also check out Chris Olah’s blog:
http://colah.github.io/
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:
https://distill.pub/

For more videos, Welch Labs also has some great series on machine learning:
https://youtu.be/i8D90DkCLhI
https://youtu.be/bxe2T-V8XRs

“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:
https://arxiv.org/abs/1611.03530
https://arxiv.org/abs/1706.05394
https://arxiv.org/abs/1412.0233

Music by Vincent Rubinetti:
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

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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: http://3b1b.co/recommended

Various social media stuffs:
Website: https://www.3blue1brown.com
Twitter: https://twitter.com/3Blue1Brown
Patreon: https://patreon.com/3blue1brown
Facebook: https://www.facebook.com/3blue1brown
Reddit: https://www.reddit.com/r/3Blue1Brown

Godfather of artificial intelligence Geoffrey Hinton gives an overview of the foundations of deep learning. In this talk, Hinton breaks down the advances of neural networks, as applied to speech and object recognition, image segmentation and reading or generating natural written language.

For more info visit:
Website: http://elevatetechfest.com
Twitter: @elevatetechfest
Facebook: @elevatetechfest
Instagram: @elevatetechfest

In this video from the 2016 Stanford HPC Conference, Julie Bernauer from Nvidia presents: HPC, Deep Learning and GPUs.

Note that the audio drops out briefly towards the end of this video.

“From image recognition in social media to self-driving cars and medical image processing, deep learning is everywhere in our daily lives. Learn about recent advancements in deep learning that have been made possible by improvements in algorithms, numerical methods, and the availability of large amounts of data for training, as well as accelerated computing solutions based on GPUs. With GPUs, great performance can be reached across a wide range of platforms, from model development on a workstation to training on HPC and data-center systems to embedded platforms, enabling new horizons for computing and AI applications.”

Julie Bernauer is Senior Solutions Architect for Machine Learning and Deep Learning at NVIDIA Corporation. She joined NVIDIA in 2015 after fifteen years in academia as an expert in machine learning for computational structural biology. She obtained her PhD from Université Paris-Sud in Structural Genomics studying Voronoi models for modelling protein complexes. After a post-doc at Stanford University with Pr. Michael Levitt, Nobel Prize in Chemistry 2013, she joined Inria, the French National Institute for Computer Science. While Senior Research Scientist at Inria, Adjunct Associate Professor of Computer Science at École Polytechnique and Visiting Research Scientist at SLAC, her work focused on computational methods for structural bioinformatics, specifically scoring functions for macromolecule docking using machine learning, and statistical potentials for molecular simulations.

Learn more: http://nvidia.com

Sign up for our insideHPC Newsletter: http://insideHPC.com/newsletter

Silicon Valley Deep Learning Group is honored to host Peter Norvig. Peter talks about Deep Learning and Understandability versus Software Engineering and Verification.

Peter Norvig is a Director of Research at Google Inc. Previously he was head of Google’s core search algorithms group, and of NASA Ames’s Computational Sciences Division, making him NASA’s senior computer scientist. He received the NASA Exceptional Achievement Award in 2001. He has taught at the University of Southern California and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006. He was co-teacher of an Artifical Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. His publications include the books Artificial Intelligence: A Modern Approach (the leading textbook in the field), Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX. He is also the author of the Gettysburg Powerpoint Presentation and the world’s longest palindromic sentence. He is a fellow of the AAAI, ACM, California Academy of Science and American Academy of Arts & Sciences.

See Peter’s web site for more info:

http://norvig.com/bio.html

Deep learning has made great progress in a variety of language tasks. However, there are still many practical and theoretical problems and limitations. In this talk I will introduce solutions to some of these: How to predict previously unseen words at test time. How to have a single input and output encoding for words. How to grow a single model for many tasks. How to use a single end-to-end trainable architecture for question answering.

M.A. Tokatlioglu’s and my undergraduate thesis project at Sakarya University.

Source Code : https://github.com/onurgunes/DeepFacialEmotion

Contacts:
M.A. Tokatlioglu https://tr.linkedin.com/in/makiftokatlioglu
O. Gunes https://tr.linkedin.com/in/onurgunes

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.

Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data.

Please watch: “Free Online Digital Marketing Course |How Digital Marketing works in 2019|Career| Courses|Lecture-01”
https://www.youtube.com/watch?v=QiZVxQcVoAM –~–
Machine Learning and Deep Learning Is the future!!
Guys in this vodeo you can know about How can Machine learning and deep learning implemented in real life and how to we can improve technology using machine learning and depp learning.

If anybody have some questions regarding my videos please tell me at

E-mail– iamsameer1997@gmail.com

Facebook: https://www.facebook.com/mdsamir.shaikh.948

Website: iamsameer1997@ggmail.com

Machine Learning and Deep learning–https://www.youtube.com/watch?v=A_JB7rA3rAE

Amcat Syllabus Exam Pattern–https://www.youtube.com/watch?v=nGDDPplGapY

Database working Architecture–https://www.youtube.com/watch?v=_6TSCsCv8go-

How to Split Videos–https://www.youtube.com/watch?v=d6UcRTE1yOk

Best Motivational Video–https://www.youtube.com/watch?v=C5-YuQ6onOQ

DBMS Vs FIle Management System–
https://www.youtube.com/watch?v=r8mpHVmnhSY

Free Blogging without coding–https://www.youtube.com/watch?v=DBv_SalBWjo

YouTube Policy Update–https://www.youtube.com/watch?v=G_gXIkAMj94

OOP concept of java easily–
https://www.youtube.com/watch?v=WqZWzjU5Qm8

school boy can make lots of apps–
https://www.youtube.com/watch?v=v9UAJcxY2W4

Drawbacks/Disadvantages using Filemanagement system–https://www.youtube.com/watch?v=DnqAxD1neJU

Advantages of Database management System–https://www.youtube.com/watch?v=5aCocxijJO0

Introduction of java–https://www.youtube.com/watch?v=8cNNJpw6_CA

Introduction, Application and History of C programming–
https://www.youtube.com/watch?v=KUoNnog2rVs

Full video editting tutorial–
https://www.youtube.com/watch?v=xl_zonzC-eE

Features/Characteristics of Java–https://www.youtube.com/watch?v=eLKg8utHAT0

how to create a free youtube channel–https://www.youtube.com/watch?v=mh3D1qTCu1I

Complete project Line Following Car Using Arduino –https://www.youtube.com/watch?v=aM093_y02rw

Heart touching speech for students–https://www.youtube.com/watch?v=KHYn_ngjmaQ

Sandeep Maheshwari Summary speech–
https://www.youtube.com/watch?v=l0ILUPPKCGw

Build a basic calculator using C –https://www.youtube.com/watch?v=F6meAJoUFLw

How to implement if statement in c —
https://www.youtube.com/watch?v=iRb4VdqVFJc

how to set path in java–
https://www.youtube.com/watch?v=zGNMnhe2Cwk

OOP vs POP included TDA and BUA —
https://www.youtube.com/watch?v=7TPmVTQanic

Learn basic concept of database–
https://www.youtube.com/watch?v=mJr67YdoJn4

Jvm,jre, jdk and full compilation concept in java–https://www.youtube.com/watch?v=R7oP8bmYjX8

Increment and decrement operators in c–
https://www.youtube.com/watch?v=_ypin9Sl_Iw

Largest between three numbers in C–https://www.youtube.com/watch?v=UgDMS5fefGM

how to promote youtube videos on facebook–
https://www.youtube.com/watch?v=h3YP1mIEcoY

YouTube Funny Tips and Tricks–
https://www.youtube.com/watch?v=qUU9_B2cbiw

Please Like ,Comment & Subscribe!

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.

Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data.

This presentation took place at the Deep Learning Summit in London on 24-25 Sept 2015: https://www.re-work.co/events/deep-learning-london-2015 #reworkDL

The Challenges of Human Labour Automatization with Deep Learning in the Transport Industry

The key idea behind deep learning is to automate human labour, reduce the costs and reduce the time and increase precision in which a task can be done. In the transport industry things like cargo number recognition and counting of objects were first to be automated, and are now improved to a very high precision. However, there are many other tasks in the industry that can be automated, but computers are currently lacking the precision to guarantee the appliance with the industry security standards. This presentation will discuss how we have overcome some of the challenges and give an insight of the upcoming applications and their effects on industry.

Juris Pūce an adventurous entrepreneur, always looking for new challenges and business to build. Interested in all things technologically innovative and somewhat unknown, hence most of his companies are IT related. With over 15 years of experience in technology related business management, Juris Pūce currently divides his work between being a visionary for various start-ups as well as being the CTO of KleinTech, a company that specialises in complex machine vision and deep learning technology solutions for transport and security industries.

When Geoffrey Hinton, a researcher at Google and professor emeritus at the University of Toronto, began his work in deep learning in the 1970s, he was told he would spend his life toiling away in obscurity. Deep learning is a form of artificial intelligence that mimics the human brain. Now, four decades later, his research is revolutionizing AI. He joins The Agenda to discuss his work and what kept him going.

Artificial Intelligence is by far the most important Technology of our era. AI is about to transform society, and I think it’s very important for people to understand the basics of it. In this video, I explain you the differences between Artificial Intelligence, Machine Learning and Deep Learning.

The Article related to this video: http://selimchehimi.com/what-are-the-differences-between-artificial-intelligence-machine-learning-and-deep-learning/

Music: Our Samplus – Spell on You


My name is Selim Chehimi and I’m an engineering student. I’m passionate about AI and I really want to be involved in this industry. I’m posting Articles about it on my blog selimchehimi.com. Thanks for watching, reading and subscribing!

My Blog: http://selimchehimi.com/

FOLLOW MY INSTAGRAM- http://instagram.com/selimchehimi

FACEBOOK- https://www.facebook.com/Selim-Chehimi-1681013242200195/

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( TensorFlow Training – https://www.edureka.co/ai-deep-learning-with-tensorflow )
This video on Artificial intelligence gives you an introduction to artificial intelligence with futuristic applications of AI. It also tells you how to implement artificial intelligence using deep neural networks.

The video covers the following topics:
1. What is Artificial Intelligence & its applications
2. Subsets of AI – Machine Learning & Deep Learning
3. What is Deep Learning?
4. Use Case – Recognizing handwritten digits from MNIST dataset
5. Applications of Deep Learning

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

Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE

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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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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.

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

Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Video recap conference about Artificial Intelligence for accountants in business in the Netherlands: Deep Finance

What does deep learning and artificial intelligence mean for accountants in business? How much of the ‘human element’ will still be incorporated in the figures? Are we headed for a future of ‘self-driving financials’? The accountants in business member group of the NBA (The Royal Netherlands Institute of Chartered Accountants) share the findings of the annual conference.
More than 500 financials in the Netherlands attended the Deep finance congress: the next step in financial intelligence. The video recap (with English subtitles) takes the form of a mini documentary, revealing the most important insights gained. Various speakers feature in the video such as Marcel Smits, CFO of agri-business Cargill, and Gerard van Olphen, Chairman of the Executive Board of APG Group. APG manages the pensions of 4.5 million clients, amounting to 370 billion euros of invested assets. They are both accountants in business and illustrate how big data and self-learning systems are used in their organisation.

Critical thinking
Is data self-evident? Christien Brinkgreve, professor of social and behavioural sciences, published the book ‘Weten vraagt meer dan meten’ (There’s more to knowledge than numbers). This work reveals the dangers of thinking we can just mindlessly allow ourselves to be at the mercy of data. Her fundamental argument is that we should not blindly trust data but keep thinking critically and continue to pay attention to what we see and hear.

What does the future hold for accountants and controllers?
Eric Postma, professor of artificial intelligence, agrees with the point of view described above and stresses data is just a representation of reality. He does not believe the dangers sketched by Elon Musk and Stephen Hawking are in any way justified. Can intelligent technology supersede humans in accountancy and controlling jobs? Eric advises adopting the ‘tsunami strategy’ to remain relevant in your field. This video also contains a fragment on bionics, highlighting why this field of expertise is so promising for the future. ‘Bionic Woman’, Ylva Poelman, highlights the many advantages of using nature as inspiration for technological innovation.

The Royal Netherlands Institute of Chartered Accountants (Koninklijke NBA)
The ‘accountants in business’ member group of the NBA has over 9,200 members and has been organising the annual congress, de Dag van De Financial, since 1999. The main objective is to address and examine those themes that really matter and, in this way, go beyond the issues of daily activities. This encourages members to deepen their professional knowledge and to stay abreast of the most recent developments in their field.