Peter Albert Railton is an American philosopher who is Gregory S. Kavka Distinguished University Professor and John Stephenson Perrin Professor of Philosophy at the University of Michigan, Ann Arbor, where he has taught since 1979.

Here we go over a Python Project using OpenCV and simple Machine Learning
Google Colab Link : https://colab.research.google.com/drive/1DOvXJZRkjfKfF9oUpCZXSU3hPG3g1h-l?usp=sharing
Support me on Patreon
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DISCLOSURE: Some of the links on this page are affiliate links, meaning, at no additional cost to you, I may earn a commission if you click through and make a purchase. Affiliate commissions help fund videos like this one.

Hands on Machine Learning with Scikit-Learn, Keras and TensorFlow. Still the best book on machine learning?

Buy the book here (in the US) https://amzn.to/3hxB6US (affiliate link)
or from my Amazon store https://www.amazon.co.uk/shop/pythonprogrammer

3 Data Science Learning Platforms I would recommend

1. Data Quest – https://bit.ly/3hcLjbY (my favourite)
2 Data Camp – https://bit.ly/39erDk8
3 365 Data Science – https://bit.ly/3hbUHdd

2 Recommended Python Courses

1. Exploratory Data Analysis with Python and Pandas – https://bit.ly/2QXMpxJ
2. Complete Python Programmer Bootcamp – http://bit.ly/2OwUA09

Data Science Interview Preparation
StrataScratch https://bit.ly/30ul0nX

(These contain affiliate links, which means I receive a percentage of any sales made. There is no additional cost for anybody clicking on them)

👌 SUBSCRIBE 👌
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Recorded on Mar 24 2016 at GCP NEXT 2016 in San Francisco.

Visual effects rendering is a computationally intensive process where one second of screen-time can require thousands of cores and terabytes of frame data. Learn how Academy Award-winning and recognized studios take advantage of cloud economics and Google’s on-demand computing to realize their creative visions and expand this digital medium for storytelling.

Speakers: Julia Ferraioli, Google & David Zuckerman, Wix

Hands On Machine Learning with Scikit Learn and Tensorflow published by O’Reilly and written by Aurelien Geron could just be the best practical book on machine learning. In this review I explain why.

►Subscribe to my YouTube Channel http://bit.ly/2LCdOy1

3 Data Science Learning Platforms I would recommend

1. Data Quest – https://bit.ly/3hcLjbY (my favourite)
2 Data Camp – https://bit.ly/39erDk8
3 365 Data Science – https://bit.ly/3hbUHdd

2 Recommended Python Courses

1. Exploratory Data Analysis with Python and Pandas – https://bit.ly/2QXMpxJ
2. Complete Python Programmer Bootcamp – http://bit.ly/2OwUA09

Data Science Interview Preparation
StrataScratch https://bit.ly/30ul0nX

(These contain affiliate links, which means I receive a percentage of any sales made. There is no additional cost for anybody clicking on them)

You can buy the book at my Amazon store https://www.amazon.com/shop/pythonprogrammer

Music: www.bensound.com

In his talk, Kristian Hammond discusses the evolution of communication by artificial intelligence. Hammond mentions potential future uses, as well as practical applications of machine-generated language that are already affecting, and improving, our day-to-day lives. In addition to being Chief Scientist, Kris is a professor of Computer Science at Northwestern University. Prior to Northwestern, Kris founded the University of Chicago’s Artificial Intelligence Laboratory. His research has always been focused on artificial
intelligence, machine-generated content and context-driven information systems.

Kris previously sat on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). Kris received his PhD from Yale. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

The questions: What is machine learning in data science & what is machine learning used for are a bit ironic since the ML algorithms of youtube are what determined to show you this video in the first place. On this video you will learn the science behind ML or machine learning and a.i or artificial intelligence and what they are used for. Deep learning is another concept that we will focus on another video. Machine learning and ai on the other hand are going to shape the future of our lives since they are being used in the 21st century global economy. Data scientists will have more oppurtunities to focus their attention on creating ML artificial intelligence systems. We’ve already seen “Sophia” an A.I Robot that responds intelligently on peopl’se questions. This will become the norm as machine learning develops in the coming years. Watch the video to learn more about ML and data science.

#ML #MachineLearning #DataScience

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Sources: “Sophia the Robot on CNBC International TV” https://www.youtube.com/watch?v=1noWqYaDpjQ&t=0s
Future of ML
https://yourstory.com/2020/01/technologies-future-ai-ml-startups-machine-intelligence
https://learn.g2.com/future-of-machine-learning
https://www.kdnuggets.com/2020/02/future-machine-learning-less-engineering.html

Music Credits:
CO.AG Music https://www.youtube.com/channel/UCcavSftXHgxLBWwLDm_bNvA
SoundTrack 1: “Background Music 4” https://www.youtube.com/watch?v=KjEh-sSp6EE&t=0s

Title: Break Into AI: A Q&A with Andrew Ng on Building a Career in Machine Learning
Speaker: Andrew Ng
Date: 12/4/2018

Abstract
Andrew Ng will share tips and tricks on how to break into AI. He will discuss some of the most valuable skills for today’s machine learning engineers, how to gain the experience to successfully switch careers, and how to build a habit of lifelong learning. He will also take questions from aspiring engineers and business professionals who want to work on AI-powered products.

SPEAKER
Andrew Ng, General Partner, AI Fund; CEO, Landing AI;
Adjunct Professor, Stanford University
Dr. Andrew Ng, a globally recognized leader in AI, is a General Partner at AI Fund and CEO of Landing AI. As the former Chief Scientist at Baidu and the founding lead of Google Brain, he led the AI transformation of two of the world’s leading technology companies. A longtime advocate of accessible education, Dr. Ng is the Co-founder of Coursera, an online learning platform, and founder of deeplearning.ai, an AI education platform. Dr. Ng is also an Adjunct Professor at Stanford University’s Computer Science Department.

MODERATOR
Juan Miguel de Joya, UN ITU; ACM Practitioners Board
Juan Miguel de Joya is the in-house consultant for Artificial Intelligence and Emerging Technologies at the United Nations International Telecommunications Union. Prior to this role, he served as a contractor at Facebook/Oculus and Google, worked at Pixar Animation Studios and Walt Disney Animation Studios, and was an undergraduate researcher in graphics at the Visual Computing Lab at the University of California, Berkeley. In his spare time, he is part of the ACM Practitioners Board, the ACM Professional Development Committee, and the ACM SIGGRAPH Strategy Group. His current interests include artificial intelligence, computer vision, mixed reality, computational physics, the web, and the human impact of computing in society at large.

The highly selective and rigorous 11-month online PG Diploma in Machine Learning and AI, co-developed by IIIT Bangalore and UpGrad aims to prepare professionals for advanced Data Science and Machine Leaning roles.

Get most in-demand certification with the upGrad Post Graduate Diploma in Machine Learning and Artificial Intelligence, in association with IIIT Bangalore. Develop skills such as Machine learning, Deep learning, Graphical models etc. Enroll today!
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#MachineLearning #ArtificialIntelligence #PGDiplomaCourses

Open the doors for your career through upGrad. Experience a new way of learning online with dedicated mentorship, industry exposure and expert career assistance. #LifeKoKaroLift

upGrad is an online higher education platform providing rigorous industry-relevant programs designed and delivered in collaboration with world-class faculty and industry. Merging the latest technology, pedagogy, and services, upGrad is creating an immersive learning experience – anytime and anywhere. upGrad began in 2015 with the conviction that in an ever-changing industry, professionals need to continuously upskill themselves in order to stay relevant.

upGrad has created some of India’s largest online programs to help thousands of professionals achieve their career goals in the areas of data, technology, and management.

Stay on top of your industry by interacting with us on our social channels:
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Detecting and starting treatment of autism spectrum disorder (ASD) at an age of 18 to 24 months can increase a child’s IQ by up to 17 points—in some cases moving them into the “average” child IQ range of 90-110 (or above it)—and improving the child’s quality of life significantly. Researchers at Duke University are using Machine Learning on AWS to create a faster, less expensive, more reliable, and more accessible system to screen children early for ASD.

To learn more about research projects like this that are enabled by AWS, see the AWS Machine Learning Research Awards website – https://amzn.to/2RR78PM

For More information Please visit
https://www.appliedaicourse.com
#ArtificialIntelligence,#MachineLearning,#DeepLearning,#DataScience,#NLP,#AI,#ML

The Current State of Adversarial Machine Learning – Heather Lawrence
Proving Ground
BSidesLV 2018 – Tuscany Hotel – Aug 08, 2018

As Machine Learning (ML) systems are increasingly becoming part of user-facing applications, their reliability and robustness are key to building and maintaining trust with users and customers, especially for high-stake domains. While advances in learning are continuously improving model performance on expectation, there is an emergent need for identifying, understanding, and mitigating cases where models may fail in unexpected ways. This session is going to discuss ML reliability and robustness from both a theoretical and empirical perspective. In particular, the session will aim at summarizing important ongoing work that focuses on reliability guarantees but also on how such guarantees translate (or not) to real-world applications. Further, the talks and the panel will aim at discussing (1) properties of ML algorithms that make them more preferable than others from a reliability and robustness lens such as interpretability, consistency, transportability etc. and (2) tooling support that is needed for ML developers to check and build for reliable and robust ML. The discussion will be grounded on real-world applications of ML in vision and language tasks, healthcare, and decision making.

Session Lead: Besmira Nushi, Microsoft

Speaker: Thomas Dietterich, Oregon State University
Talk Title: Anomaly Detection in Machine Learning and Computer Vision

Speaker: Ece Kamar, Microsoft
Talk Title: AI in the Open World: Discovering Blind Spots of AI

Speaker: Suchi Saria, Johns Hopkins University
Talk Title: Implementing Safe & Reliable ML: 3 key areas of development

Q&A panel with all 3 speakers

See more on-demand sessions from Microsoft Research’s Frontiers in Machine Learning 2020 virtual event: https://www.microsoft.com/en-us/research/event/frontiers-in-machine-learning-2020/

H. James Wilson, Managing Director of Accenture Research, presents his lates book “Reimagining Work in the Age of AI” at Tatarklubben November 6th, 2018. Paul R. Daugherty is the co-author of the book.

Try out Teachable Machine → https://goo.gle/3clys2J
Check out this blog → http://goo.gle/3ovgpvH

Creating an ML model seems like a complex and time-consuming task to complete, but it doesn’t have to be this way. In this episode of Making with Machine Learning, we show you how to easily create and train an ML model using Teachable Machines – Google Cloud’s no-code platform that trains ML models to recognize images, sounds, or poses. Watch to to learn how you can easily create an ML model with this tool!

Chapters:
0:00 – Intro
0:25 – Train models to recognize letters
0:55 – Collecting training data
1:33 – Training the model
1:48 – Test model
2:13 – Conclusion

Watch more episodes of Making with Machine Learning → https://goo.gle/2YysJRY
Subscribe to get all the episodes as they come out → https://goo.gle/GCP

#MakingwithMachineLearning #MakingwithML

Product: AI Platform Training; fullname: Dale Markowitz;

Geordie Rose: Machine Learning is Progressing Faster Than You Think

Dr. Geordie Rose is a founder and Chief Technology Officer at D-Wave Computers. I met Geordie at the IdeaCity conference in Toronto where he made an impassioned presentation about D-Wave and quantum computing. Needless to say, as soon as Dr. Rose stoped speaking I rushed to ask him for an interview. As it turns out Geordie is already a fan of Singularity 1 on 1 and isntantly said that he would be happy to do it.

As a father of three kids and the CTO of a trail-blazing quantum computing company, Dr. Rose is a very busy person. Yet somehow he was generous beyond measure in giving me two full hours for an interview with the apparent desire to address as many of mine and the audience’s questions as possible.

During our conversation with Geordie Rose we cover a variety of interesting topics such as: how wrestling competitively created an opportunity for him to discover Quantum Mechanics; why he decided to become an entrepreneur building computers at the edge of science and technology; what the name D-wave stands for; what is a quantum computer; why fabrication tech is the greatest limiting factor towards commoditizing quantum computing; hardware specs and interesting details around Vesuvius – D-Wave’s latest model, and the kinds of problems it can compute; Rose’s Law as the quantum computer version of Moore’s Law; how D-wave resolves the de-coherence/interference problem; the traditional von Neumann architecture behind classical computer design and why D-Wave had to move beyond it; Vesuvius’ computational power as compared to similarly priced classical super-computers and the inherent difficulties in accurate bench-marking; Eric Ladizinski’s qubit and the velodrome metaphor used to describe it; the skepticism among numerous scientists as to whether D-Wave really makes quantum computers or not; whether Geordie feels occasionally like Charles Babbage trying to build his difference engine; his prediction that quantum computers will help us create AI by 2029; whether the brain is more like a classical or quantum computer; how you can apply for programming time on the two D-wave quantum computers; his take on the technological singularity…

Recorded at the GAIA conference on April 10th 2018 in collaboration with Ericsson.

Artificial Intelligence and Machine Learning have made enormous progress during the last decade, and AI techniques are already widely used in industry and society. Expectations on these technologies are high, often so high that the technology we have today cannot come close to fulfil them. We will discuss what modern AI is, what is possible and what problems remain to be solved. We will present what RISE AI does within the area, how to create more interpretable machine learning models, approaches to bridge the gap between learning and reasoning, and what we believe is a way forward towards more general, large-scale AI-systems.

Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Learn more at: https://stanford.io/3bhmLce

Andrew Ng
Adjunct Professor of Computer Science
https://www.andrewng.org/

To follow along with the course schedule and syllabus, visit:
http://cs229.stanford.edu/syllabus-autumn2018.html

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit:
http://learn.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

How is a total beginner supposed to get started learning machine learning? I’m going to describe a 3 month curriculum to help you go from beginner to well-versed in machine learning. Its an accelerated learning plan, something i’d create for myself if I were to get started today, but I’m going to open source it for you guys. This curriculum will cover all the math concepts, the machine learning theory, and the deep learning theory to get you up to speed with the field as fast as possible. If anyone asks how to best get started with machine learning, direct them to this video!

Curriculum from this video:
https://github.com/llSourcell/Learn_Machine_Learning_in_3_Months

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

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology
instagram: https://www.instagram.com/sirajraval

Month 1

Week 1 Linear Algebra
https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
Week 2 Calculus
https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
Week 3
https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2
Week 4 Algorithms
https://www.coursera.org/courses?languages=en&query=Algorithm%20design%20and%20analysis

Month 2

Week 1
learn python for data science
https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU
Math of Intelligence
https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D
Intro to Tensorflow
https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV

Week 2
Intro to ML (Udacity)
https://eu.udacity.com/course/intro-to-machine-learning–ud120

Week 3-4
ML Project Ideas
https://github.com/NirantK/awesome-project-ideas

Month 3 (Deep Learning)

Week 1
Intro to Deep Learning
https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3

Week 2
Deep Learning by Fast.AI
http://course.fast.ai/

Week 3-4
Re-implement DL projects from my github
https://github.com/llSourcell?tab=repositories

ML people to follow on Twitter:
https://www.quora.com/Who-should-I-follow-on-Twitter-to-get-useful-and-reliable-machine-learning-information

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
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Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

As machine learning systems enter the open world, their accountability becomes a high priority problem. Accountability requires deep understanding of system behavior and its failures. Characterization of failures and shortcomings is particularly complex for systems composed of multiple machine learned components. In this talk, I will discuss our work on troubleshooting and in-depth failure analysis for such systems. First, I will present a methodology for applying counterfactual analysis with humans in the loop for the purpose of understanding which component fixes are the most effective ones for a given system architecture. Second, I will describe the functionalities and tools that we have built for detailed system performance analysis and prediction. Both works will be illustrated via a real-world case study example of a machine learning pipeline for image captioning.

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Please comments, likes, share, subscribe to our channel and ring the bell to see my latest videos update.

👉Know what is corona, what is machine learning,how machine learning technology can be used to fight corona, corona virus predictions and lot more.
#fightingcovid19withmachinelearning #covid19 #covid19outbreakprediction #machinelearning #covid19technology

🔥🔥Intellipaat Machine Learning course: https://www.youtube.com/watch?v=4gqZLajDWh8

#fightingcovid19withmachinelearning #covid19 #covid19outbreakprediction #machinelearning #covid19technology

📌 Do subscribe to Intellipaat channel & get regular updates on videos: https://goo.gl/hhsGWb

🔗 Watch Machine Learning video tutorials here: https://intellipaat.com/blog/tutorial/machine-learning-tutorial/

📕 Read complete Machine Learning tutorial here: https://intellipaat.com/blog/tutorial/machine-learning-tutorial/

⭐ Get Machine Learning cheat sheet here: https://intellipaat.com/blog/tutorial/machine-learning-tutorial/mllib-cheat-sheet/

Interested to learn machine learning still more? Please check similar machine learning blog here: https://intellipaat.com/blog/tutorial/machine-learning-tutorial/

Are you looking for something more? Enroll in our machine learning full course and become a certified professional (https://intellipaat.com/machine-learning-certification-training-course/). It is a 32 hrs instructor led machine learning training provided by Intellipaat which is completely aligned with industry standards and certification bodies.

If you’ve enjoyed this machine learning video, Like us and Subscribe to our channel for more similar machine learning videos and free tutorials.
Got any questions about machine learning? Ask us in the comment section below.
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——————————
Why should you watch this machine learning video?

Machine learning is one of the fastest growing arms of the domain of artificial intelligence. It has far reaching consequences and in the next couple of years we will be seeing every industry deploying the principles of artificial intelligence, machine learning and deep learning technologies at scale.

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.
——————————
For more Information:
Please write us to sales@intellipaat.com, or call us at: +91- 7847955955

Website: https://intellipaat.com/machine-learning-certification-training-course/

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Neal Jean, Michael Xie, Stefano Ermon, Matt Davis, Marshall Burke, David Lobell
“Combining satellite imagery and machine learning to predict poverty”
Stanford University Depts of Computer Science and Earth Systems Science
CompSust-2016
4th International Conference on
Computational Sustainability
July 7, 2016
3:30 PM, Machine and Statistical Learning for Conservation, Poverty, Energy, and Climate
http://www.compsust.net/compsust-2016/program.php

In this tutorial, you will learn how to create a Machine Learning Linear Regression Model using Python. You will be analyzing a house price predication dataset for finding out the price of a house on different parameters. You will do Exploratory Data Analysis, split the training and testing data, Model Evaluation and Predictions.

Blog and Dataset – https://studygyaan.com/data-science-ml/linear-regression-machine-learning-project-for-house-price-prediction

Coronavirus (Covid-19) has become the most buzzed topic these days. Its outbreak has taken the world by storm. In this video, we’ll see what Coronavirus is, how did it emerge, and what are its symptoms. Then, we will see what has been its impact so far and analyze the outbreak of Coronavirus across various regions, visualize them using charts and graphs, and predict the number of upcoming confirmed cases using the Linear Regression model in Python. Finally, we’ll look at the various safety measures that you can take to save yourself from getting attacked by Coronavirus.

Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1

To access the slides, click here: https://www.slideshare.net/Simplilearn/coronavirus-outbreak-prediction-using-machine-learning-covid19-outbreak-prediction-simplilearn/Simplilearn/coronavirus-outbreak-prediction-using-machine-learning-covid19-outbreak-prediction-simplilearn

Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning, and follow the path towards your dream career- https://www.simplilearn.com/machine-learning-career-guide-pdf?utm_campaign=Coronavirus-outbreak-prediction-sHWKN5dakPw&utm_medium=Tutorials&utm_source=youtube

You can also go through the Slides here: https://goo.gl/m5Txob

Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy

#MachineLearning #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse

We’ve partnered with Purdue University and collaborated with IBM to offer you the unique Post Graduate Program in AI and Machine Learning. Learn more about it here – https://www.simplilearn.com/ai-and-machine-learning-post-graduate-certificate-program-purdue?utm_campaign=Machine-Learning-Tutorial-DWsJc1xnOZo&utm_medium=Tutorials&utm_source=youtube

About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.

Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

What skills will you learn from this Machine Learning course?

By the end of this Machine Learning course, you will be able to:

1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.

We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning

Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Coronavirus-outbreak-prediction-sHWKN5dakPw&utm_medium=Tutorials&utm_source=youtube

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We invest heavily in machine learning to continually improve our member experience and optimize the Netflix service end-to-end. As researchers, we innovate using machine learning in many areas where we research, design, implement, evaluate, and productionalize models and algorithms through both offline experiments and online A/B testing.

Cloud Tensor Processing Units (TPUs ) enable machine learning engineers and researchers to accelerate TensorFlow workloads with Google-designed supercomputers on Google Cloud Platform. This talk will include the latest Cloud TPU performance numbers and survey the many different ways you can use a Cloud TPU today – for image classification, object detection, machine translation, language modeling, sentiment analysis, speech recognition, and more. You’ll also get a sneak peak at the road ahead.

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Is it possible to use machine learning without needing to code? The answer is yes! Uber’s AI lab recently open sourced python library called Ludwig that they’ve been using internally for 2 years. The tagline is that it allows anyone to use deep learning without coding. It will require some configuration and unix commands to setup, but I’ll show you how in this video. I’ll also talk about other code-free tools like Azure ML Studio, DataRobot, and DeepCognition. Enjoy!

Code for this video:
https://github.com/llSourcell/ludwig

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More learning resources:
https://uber.github.io/ludwig/
https://azure.microsoft.com/en-us/services/machine-learning-studio/
https://www.datarobot.com/
https://deepcognition.ai/

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Guillaume Laforge in conversation with Google’s Senior Fellow Jeff Dean for Devoxx BE 2016.

Artificial Intelligence in Hindi | Free Course by Google | Machine Learning | Career, Scope

Google Free Course – https://ai.google/education/

EDUCATIONAL VIDEOS [हिंदी में ]
►Countries where Education is free for Indians | दुनिया के ऐसे देश जहां Education फ्री है✔https://www.youtube.com/watch?v=qDNG6H5qRA0&t=2s
► Right Attitude Motivation For Students | Power of Positive thinking in hindi ✔ https://www.youtube.com/watch?v=cmVvZb6b5VE
► What is Investment Banking in HINDI || Investment Banker ? ✔ https://www.youtube.com/watch?v=tMYVaPFG4Kw
► Fastest and Easiest Way to Learn English || ENGLISH Sikhne ka Aasan tarika ✔ https://www.youtube.com/watch?v=GF5OHAZcW0k
► How to Get Education LOAN in INDIA 2017 ✔ https://www.youtube.com/watch?v=DoluUHBZ1zw.
► MBA INDIA VS MBA ABROAD – WHICH IS BEST ? ✔ https://www.youtube.com/watch?v=ufgd8pkvtjE
► Highest Paying Jobs in India 2017✔ https://www.youtube.com/watch?v=zAa0w1eufLs

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Peter Norvig, Research Director at Google, in conversation with Toby Walsh, UNSW Scientia Professor of Artificial Intelligence.

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In this episode: Learning LETTERS with TuTiTu’!
Teaching Toddlers the ABC in a Fun and memorable way!

TuTiTu Preschool is a series for children making their first steps into numbers, letters, shapes, colors, and more. Each episode will teach your toddler one of the basic building blocks of knowledge using vivid colors, fun and engaging animation and exciting music!

Facebook – https://www.facebook.com/TuTiTuTV
Pinterest – http://www.pinterest.com/tutitutv
Twitter – https://twitter.com/TuTiTuTV

Production & Animation: Twist Animation Ltd.
Production Designers: Tal Gamliel, Yossi Dahan
TuTiTu’S Theme Song: Lyrics: Sarit Ido Schechter, Music: Sarit Ido Schechter and Tal Gamliel,
Musical Arrangement: Uri Kariv
Vocals: Yael Shoshana Cohen
Sound engineering: Gil Landau

Category: Preschool

Meet Lisa Steiner, a marine biologist who has spent her life and career trying to better understand sperm whales—and as a self-described “technophobe”—as she embraces machine learning models created by CapGemini. In the middle of the Atlantic Ocean, technology is helping Lisa do what she loves: understand more about these massive marine mammals so that they stand a better chance at survival.

The Big Idea is a short documentary series that seeks out people who look at the world differently—as they experiment with novel approaches to reinventing industries and lives, not the least of which are their own. There’s a whole world out there that’s ready to be reshaped. All it takes is one big idea.

Learn more:
Building a winning solution – using AI to help identify and track whale population – https://bit.ly/3tSdW2j
Using AI to protect whales – http://bit.ly/3tuZih4

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Don’t forget to turn on the subtitles

Rebecca Winthrop, co-director of the Center for Universal Education at The Brookings Institution, and Emilia Ahvenjärvi, Science and Education Attaché at the Embassy of Finland in Buenos Aires talk about risks and opportunities in education.

enlightED is the great world conference on education, innovation and technology, which dedicated its third edition to reflecting, with the help of international experts, on the challenges that the Covid-19 has posed to learning and work, and to proposing solutions. Organised by Fundación Telefónica, IE University, Fundación Santillana and South Summit.

Find out more at: enlighted.education

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