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Note: Recent Research has disputed the effectiveness of learning styles:
The idea of this video is to take Gardner's Multiple Intelligences and use them as student learning styles, although Gardner specifically says learning styles are NOT multiple intelligences
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Student learning styles and multiple intelligences are described and explored in this video. Seven different learning styles are described in detail and explained fully for teachers and educators in all grade levels and disciplines. This video gives credit to howard gardner and his work on multiple intelligences. The video also explains why multiple intelligences are so important to classroom teachers. The first learning style mentioned is the interpersonal learner also known as the social learner. That is followed by the opposite type of learner, which is the intrapersonal learner, sometimes called the solitary learner. Other learning styles included are kinesthetic (physical), verbal or linguistic, auditory or aural, logical or mathematical, and visual. Definition and theory surrounding multiples intelligences are provided throughout along with with suggestions that educators can make to improve their instruction for these students. Other videos in teachings in education playlists are designed for classroom teachers to learn as much as they can, grow as a teacher, and advance in their career of education.
Learning Japanese tips to help you on your language learning quest.
► Get speaking with a Japanese person now! (your iTalki voucher): http://goo.gl/XDL1BO
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Transfer learning is a statistical technique that's been getting more attention lately that enables you to reuse a model for a different task than what it was trained for. In this episode, I’m going to show you how to use transfer learning to predict instances of gold deposits using publicly available satellite imagery. We'll discuss the 4 different mathematical ways to frame the transfer learning problem, then look into how a U-Net architecture works. The U-Net is really popular for image segmentation and classification tasks on sites like Kaggle, so knowing how it works will be a useful skill. Enjoy!
Code for this video:
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The belief in learning styles is so widespread, it is considered to be common sense. Few people ever challenge this belief, which has been deeply ingrained in our educational system. Teachers are routinely told that in order to be effective educators, they must identify & cater to individual students' learning styles; it is estimated that around 90% of students believe that they have a specific learning style but research suggests that learning styles don't actually exist! This presentation focuses on debunking this myth via research findings, explaining how/why the belief in learning styles is problematic, and examining the reasons why the belief persists despite the lack of evidence.
Dr. Tesia Marshik is an Assistant Professor of Psychology at the University of Wisconsin-La Crosse. Her research interests in educational psychology include student motivation, self-regulation, and teacher-student relationships.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
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Best Toy Learning Video for Baby - Teach Colors with Cookie Monster! In this preschool educational video for babies and toddlers, let's learn colors, counting, numbers, and more! This fun educational toy video features great Sesame Street toys and characters like Cookie Monster and Elmo. Family friendly educational toy fun for toddlers!
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Jamazing Patrons of September: JN Valente, Sachin Shenoy, Avijit Singh, Madhan, Bollywood By Karak, Jaydioactive, Sachin Shenoy, Bhavya Singh, Harmandeep Singh, Riju Mahna, Daniel Prakash and Aqeel Shah!
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Bihar is battling floods yet again this monsoon season, as water level in the Ganga and other rivers surged after heavy rainfall continued to lash the region.
![Machine Learning VS Deep Learning: [Whats The Difference]](https://i.ytimg.com/vi/LhVY0-S7cAM/maxresdefault.jpg)
How does Sephora's Makeup Artificial Intelligence App works?
How does it detect where your eyes, lips, nose etc is?
How does Clothing detection apps work and know if its a Dress, Pants, Jacket etc.?
I am sure, these questions come to your mind when you see these apps and AI in action.
This tutorial explains what is the first step required for these apps to work and how machine learning & deep learning is applied to make these detections and predictions.
You will learn:
- What is Machine Learning?
- What is Deep Learning?
- Why is it everywhere all of sudden?
- How should you pick one over the other?
- and of course, How do these apps detect what I am wearing?
Machine learning is defined as algorithms that give the ability to computers to learn from data and make predictions.
Deep learning is a subfield of machine learning concerned with algorithms inspired by structure and function of the brain called artificial neural networks.
Machine learning and deep learning are disrupting various industries and you will see huge changes in next few years.
**************************
Other Videos on AI:
**************************
What is Artificial Intelligence [simple definition]
https://www.youtube.com/watch?v=9sRfihRSvt0
Google Duplex: Google AI Makes Appointment at Hair Salon
https://www.youtube.com/watch?v=PPym4ivamx0
How to Start a Clothing Line for $67?
https://www.youtube.com/watch?v=3JKL3xIc0Oc
Trish Shaffer is the Coordinator for Multi-Tiered Systems of Support (MTSS) for the Washoe County School District (WCSD). In this talk, she discusses the practice and value of social and emotional learning.
In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Как НОКАУТИРОВАТЬ в Тайском Боксе Фишки Муай Тай Тактика боя, техника, обучение, тактика в спарринге
Как нокаутировать в тайском боксе, покажет нам 3 фишки в Муай Тай тренер Вичарн. Тактика боя и техника - это важные навыки. В этом видео обучение тактики в спарринге.
Это школа муай тай. А мы знаем что школа тайского бокса - жесткая и беспощадная. Как правильно спаринговаться, как нокаутировать в тайском боксе мы посмотрим 3 варианта в этом видео. Ведь когда у тебя есть наработки, ты не задаешься вопросом как нокаутировать противника, как спарринговать в мма или муай тай. Ты просто работаешь, выбрасываешь на инстинктах, ты спаррингуешь на 6 чувстве. Как спарринговаться правильно, что бы лишнего не пропускать. В этом видео 3 фишки для любителей бокса, мма и тайского бокса, где есть удары руками на встречу, удары ногами и т.д.
Всем приятного просмотра и продуктивных тренировок. А что бы они были еще продуктивней, смотрите ниже:
Как бьют Лоу Кик в Таиланде: https://youtu.be/HK1YpHru-sA
Мидл кик с Тайцами: https://youtu.be/Xk3Bdg-catc
Когда бить ЛОУ КИК? Глазомер: https://youtu.be/R6BdlrZft4E
Тренировка ног. Развивающие упражнения: https://youtu.be/69_TTW55QD0
Тренировка ног - скорость ног: https://youtu.be/yjhXAFgn0Gg
Растяжка ног для ударов ХАЙ КИК: https://youtu.be/7nFuS2NIlbM
Как бить сдвоенные удары ногами: https://youtu.be/UiUVmvRgK9U
Саенчай кик https://youtu.be/QaPwLTYn6Mc
Как бить фронт кик: https://youtu.be/RbDmBbxGUq4
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Como as maquinas com inteligencia artificial aprendem? Por que alguns robôs e programas são capazes de prever o futuro?
O Machine Learning ou Aprendizado de Máquina treinam algoritmos os expondo a vários exemplos até que ele reconheça padrões. A partir daí, o céu é o limite.
Assista ao vídeo e entenda melhor essa história de máquinas nerds :)
Inscreva-se no canal e ative as notificações https://goo.gl/RulwZj
Créditos
Direção geral e apresentação: Marcelo Tas
Realização: Supernormal Comunicações
Direção: Sergio Zeigler
Conteúdo: Ludmila Tavares
Produção: Juliana Foresti
Roteiro: Marco Aurélio Gois dos Santos
Edição: Danilo Cesar Francisco
Assistente de Design: Aline Cavalcante
Trilhas: Estúdio PlugIn
First lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL. 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.
INFO:
Website: https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-deep-learning
Slides: http://bit.ly/2HtcoHV
Playlist: http://bit.ly/deep-learning-playlist
OUTLINE:
0:00 - Introduction
2:14 - Types of learning
6:35 - Reinforcement learning in humans
8:22 - What can be learned from data?
12:15 - Reinforcement learning framework
14:06 - Challenge for RL in real-world applications
15:40 - Component of an RL agent
17:42 - Example: robot in a room
23:05 - AI safety and unintended consequences
26:21 - Examples of RL systems
29:52 - Takeaways for real-world impact
31:25 - 3 types of RL: model-based, value-based, policy-based
35:28 - Q-learning
38:40 - Deep Q-Networks (DQN)
48:00 - Policy Gradient (PG)
50:36 - Advantage Actor-Critic (A2C & A3C)
52:52 - Deep Deterministic Policy Gradient (DDPG)
54:12 - Policy Optimization (TRPO and PPO)
56:03 - AlphaZero
1:00:50 - Deep RL in real-world applications
1:03:09 - Closing the RL simulation gap
1:04:44 - Next step in Deep RL
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Bias and Variance are two fundamental concepts for Machine Learning, and their intuition is just a little different from what you might have learned in your statistics class. Here I go through two examples that make these concepts super easy to understand.
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This talk will highlight some of Google Brain’s research and computer systems with an eye toward how it can be used to solve challenging problems, 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. He will also cover how machine learning is transforming many aspects of our computing hardware and software systems.
Watch more #io19 here: Inspiration at Google I/O 2019 Playlist → https://goo.gle/2LkBwCF
TensorFlow at Google I/O 2019 Playlist → http://bit.ly/2GW7ZJM
Google I/O 2019 All Sessions Playlist → https://goo.gle/io19allsessions
Learn more on the I/O Website → https://google.com/io
Subscribe to the TensorFlow Channel → https://bit.ly/TensorFlow1
Get started at → https://www.tensorflow.org/
Speaker: Jeff Dean
T0E51E
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Google has recently open-sourced its framework for machine learning and neural networks called Tensorflow. With this new tool, deep machine learning transitions from an area of research into mainstream software engineering. In this session, we will teach you how to choose the right neural network for your problem and how to make it behave. Familiarity with differential equations is no longer required. Instead, a couple of lines ofTensorflow Python, and a bag of "tricks of the trade" will do the job. No previous Python knowledge required.
This university session will cover the basics of deep learning, without any assumptions about the level of the participants. Machine learning beginners are welcome. We will cover: - fully connected neural networks - convolutional neural networks - regularisation techniques: dropout, learning rate decay, batch normalisation - recurrent neural networks - natural language analysis, word embeddings - transfer learning - image analysis - image generation - and many examples.
Martin Görner is passionate about science, technology, coding, algorithms and everything in between. He graduated from Mines Paris Tech, enjoyed his first engineering years in the computer architecture group of ST Microlectronics and then spent the next 11 years shaping the nascent eBook market, starting with the Mobipocket startup, which later became the software part of the Amazon Kindle and its mobile variants. He joined Google Developer Relations in 2011 and now focuses on parallel processing and machine learning.
[ULT-2698]
What is the best programming language to learn for machine learning? There are a lot options, and a lot of opinions on this. I'm going to describe the top 3, using code, animations, and data to validate my point. We'll learn about tensorflow.js, several python libraries, and why C++ compiles code so fast. All of that in just 8 minutes, enjoy!
Code for this video:
https://github.com/llSourcell/....Best-Programming-Lan
Please Subscribe! And like. And comment. That's what keeps me going.
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This video is apart of my Machine Learning Journey course:
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More Learning Resources:
https://www.youtube.com/watch?v=T5pRlIbr6gg&vl=en
https://learnpythonthehardway.org/
https://github.com/tensorflow/tfjs-examples
https://blog.bitsrc.io/11-java....script-machine-learn
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http://www.cplusplus.com/doc/tutorial/
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Listen English Everyday | How to understand native English speakers | Short Story
Please LIKE, COMMENT, SUBSCRIBE and SHARE as this really helps me out :)
Learn how to build and train machine learning models faster, and easily deploy to the cloud or the edge with Azure Machine Learning service. Using the latest open source technologies such as TensorFlow, PyTorch and Jupyter Notebooks, Chris Lauren shows you how to experiment locally and then quickly scale up or out with large GPU-enabled clusters in the cloud. Even if you have limited experience with data science, you can learn how to increase your productivity with automated machine learning and hyper-parameter tuning. Get started with Azure Machine Learning today for free using the link below.
Get started
http://aka.ms/AMLfree
Toy Learning Video for Kids with Paw Patrol Ultimate Rescue Vehicles! In this preschool learning video for kids, let's learn colors with new Paw Patrol Ultimate rescue toy vehicles. The Paw Patrol pups have new vehicles that need to be painted the correct color. Then join Skye, Marshall, Chase, and the gang to help rescue Peppa Pig!
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Play these other learning videos for kids!
Giant 1 Hour Long Paw Patrol Learning Videos: https://youtu.be/1tX2cyvMzX4
Educational Toy Car Videos for Kids: https://youtu.be/XN8k8ZkJwiQ
PJ Masks Toy Car Race: https://youtu.be/k0Twsb22Ld4
Genevieve Teaches Numbers with Cookie Monster: https://youtu.be/_X78XfQkLk0
Learn Words with Pororo the Little Penguin Toy House: https://youtu.be/G-zIQqBE-Jg
Laguna Blue and Pinkie Pie are Hungry: https://youtu.be/oCbegRDxgak
Learn Colors w/ Paw Patrol Underwater Rescue Mission: https://youtu.be/RhrKJvAKsGQ
Peppa Pig gets a NEW House! https://youtu.be/OoMONAVtcDw
15 млади учени, 15 лектори, 30 часа преживяване, събрано в 3 минути.
https://karollknowledge.bg/%D0....%BD%D0%B8%D0%B5-%D1%
Dr. Chiaravalloti discusses the learning process and techniques that have been shown to improve learning and memory in healthy persons. She walks through the evidence demonstrating these techniques to additionally be helpful to persons with Multiple Sclerosis and Traumatic Brain Injury. Techniques discussed include a structured treatment protocol, the modified Story Memory Technique (mSMT) and its critical ingredients, context and imagery.
Nancy D. Chiaravalloti, PhD is Director of Neuropsychology and Neuroscience and of TBI Research at Kessler Foundation, Professor in the Department of Physical Medicine and Rehabilitation at Rutgers University -NJ Medical School, and a licensed psychologist in NJ and NY. She is Project Director of the Northern NJ Traumatic Brain Injury Model System, one of 16 federally-designated model systems of research and care for persons with traumatic brain injury in the US. Dr. Chiaravalloti has obtained over 13 million dollars in grant funding since starting her professional career in 2001, including grants from the NIH, the National Institute on Disability, Independent Living and Rehabilitation Research (NIDILRR), the National Multiple Sclerosis Society, the New Jersey Commission on Traumatic Brain Injury Research, the New Jersey Commission on Spinal Cord Injury Research, the Carl Nielson Foundation and the National Stroke Association.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
