All in one place: the best AI course you’ve ever watched!

You can now enjoy all 6.5 hours of Google’s legendary AI course designed to enlighten AI beginners, grow technology leaders, inform better citizens, and amuse AI experts!

This video is the feast version. If you prefer to learn in bite-sized nibbles, you can find the individual chapters as short videos on the http://bit.ly/mf-ml playlist. The episode guide is in the top right hand corner of every video.

Don't forget to hit subscribe+notify! If you found this video useful or enjoyable, the best way to say thank you is by sharing it.

Looking for hands-on ML/AI tutorials? Here are some of my favorite 10 minute walkthroughs:

AutoML - https://console.cloud.google.c....om/?walkthrough_id=a
Vertex AI - https://bit.ly/kozvertex
AI notebooks - https://bit.ly/kozvertexnotebooks
ML for tabular data - https://bit.ly/kozvertextables
Text classification - https://bit.ly/kozvertextext
Image classification - https://bit.ly/kozverteximage
Video classification - https://bit.ly/kozvertexvideo

MarI/O is a program made of neural networks and genetic algorithms that kicks butt at Super Mario World.
Source Code: http://pastebin.com/ZZmSNaHX
"NEAT" Paper: http://nn.cs.utexas.edu/downlo....ads/papers/stanley.e
Some relevant Wikipedia links:
https://en.wikipedia.org/wiki/Neuroevolution
https://en.wikipedia.org/wiki/....Evolutionary_algorit
https://en.wikipedia.org/wiki/....Artificial_neural_ne
BizHawk Emulator: http://tasvideos.org/BizHawk.html

SethBling Twitter: http://twitter.com/sethbling
SethBling Twitch: http://twitch.tv/sethbling
SethBling Facebook: http://facebook.com/sethbling
SethBling Website: http://sethbling.com
SethBling Shirts: http://sethbling.spreadshirt.com
Suggest Ideas: http://reddit.com/r/SethBlingSuggestions

Music at the end is Cipher by Kevin MacLeod

How would you explain what machine learning is? In this video I try to explain it in varying degrees of specificity and how to answer the question depending on the person asking the question. I also go into what subcategories of machine learning exists, which we will also go deeper into in future videos.

Timestamps:
0:00 - Introduction
0:40 - What is Machine Learning?
1:36 - More specific explanation by Arthur Samuel
2:21 - Most specific definition
3:35 - Subcategories of ML
5:27 - Supervised Learning (vaguely explained)
5:47 - Unsupervised Learning (vaguely explained)
6:10 - Recommender Systems (vaguely explained)
6:30 - Reinforcement Learning (vaguely explained)
7:00 - Sub-Sub Categories: Self-Supervised Learning
7:40 - Sub-Sub Categories: Semi-Supervised Learning
8:32 - Ending

I share some of the best books for learning neural network or deep learning. I've been learning machine learning/deep learning for the past three years now, these books have all been instrumental throughout.

Book links (in order):
Make Your Own Neural Network by Tariq Rashid (Author)
Neural Networks and Deep Learning by Michael Nielsen (http://neuralnetworksanddeeplearning.com/)
Deep Learning by Ian Goodfellow, Yoshua Bengio & Aaron Courville : https://amzn.to/30UMTGl

If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those.

If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful.

Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching.

You can find me on:
Blog - http://bhattbhavesh91.github.io
Twitter - https://twitter.com/_bhaveshbhatt
GitHub - https://github.com/bhattbhavesh91
Medium - https://medium.com/@bhattbhavesh91

#neuralnetwork #deeplearning #bestbook

*I use affiliate links on the products that I recommend. These give me a small portion of the sales price at no cost to you. I appreciate the proceeds and they help me to improve my channel!

FPGA-based hardware is a good fit for deep learning inferencing on embedded devices because they deliver low latency and power consumption. Early prototyping is essential to developing a deep learning network that can be efficiently deployed to an FPGA.

See how Deep Learning HDL Toolbox™ automates FPGA prototyping of deep learning networks directly from MATLAB®. With a few lines of MATLAB code, you can deploy to and run inferencing on a Xilinx® ZCU102 FPGA board. This direct connection allows you to run deep learning inferencing on the FPGA as part of your application in MATLAB, so you can converge more quickly on a network that meets your system requirements.
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Get a free product trial: https://goo.gl/ZHFb5u
Learn more about MATLAB: https://goo.gl/8QV7ZZ
Learn more about Simulink: https://goo.gl/nqnbLe
See what's new in MATLAB and Simulink: https://goo.gl/pgGtod

© 2020 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.

How the brain works, according to Geoff Hinton.
Presented at the NIPS'2010 workshops banquet for the Deep Learning and Unsupervised Feature Learning workshop.

Realized by Yoshua Bengio with the help of Olivier Delalleau, and the complicity of Andrew Ng, Yann LeCun, Marc'Aurelio Ranzato, and Honglak Lee.

Extended motivational speech to study deep learning mathematically.

I gave this talk at an NSF Town Hall where the goal was to discuss successes of deep learning especially in light of more traditional fields (other talks can be found here: https://www.nsf.gov/events/event_summ.jsp?cntn_id=304013&org=CISE).

Wide & Deep Learning (https://research.googleblog.co....m/2016/06/wide-deep- combines the power of memorization and generalization by jointly training wide linear models and deep neural networks. We've open-sourced the implementation with an easy-to-use API in TensorFlow. It's effective for generic large-scale regression and classification problems with sparse inputs, such as recommender systems, search, ranking problems and more. We hope you find it useful in your machine learning projects.

Check out our blog post with links to tutorials, code samples, and our research paper:
- Google Research Blog post: https://research.googleblog.co....m/2016/06/wide-deep-
- TensorFlow Linear Model Tutorial: https://www.tensorflow.org/tutorials/wide/
- TensorFlow Wide & Deep Learning Tutorial: https://www.tensorflow.org/tut....orials/wide_and_deep
- Research paper: http://arxiv.org/abs/1606.07792

Watch all Machine Learning videos: https://goo.gl/gnXPPw

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Tesla has currently pioneered a major shift in the auto industry towards Self-driving cars. Have you ever wondered how it achieves this? Let's discuss the basic essentials of Self-Driving capabilities in a Tesla Car:

1. Computer Vision:

A broad field in Artificial Intelligence focusing on enabling computers to see. Tesla cars primarily make use of 2 Computer Vision Applications that make use of Deep Learning; Image Localization and Image Classification. Image Localization allows Tesla cars to position where an object is while Image classification allows the car to identify the object. These two come hand in hand to allow Tesla cars to "see".

2. Decision Making:

Besides using Deep Learning to make more efficient models to enable Tesla cars to "see", it has to carry out these resource-intensive tasks in real time. So the chips in Tesla cars have to be powerful enough to handle all the computations in Self-Driving cars. Tesla recently unveiled their own chips in 2019, which they claim is 21x more powerful than Nvidia chips.

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