In this first video we go through the necessary notation in order to make the mathematical calculations for the forward as well as the backward propagation.

As I said in the video if you have never heard of neural networks before but still want to learn and wonder where to start to gain some understanding and intuition here is a great place:

https://www.youtube.com/watch?v=aircAruvnKk

3blue1brown has more videos of neural networks that I also recommend you to watch!

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Don't miss the making movie of the film : https://youtu.be/DzxhKY-9QfY

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Learn more about FUJIFILM X Series & GFX : https://fujifilm-x.com
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This video talks about what you need to know when sourcing parts to build your own deep learning machine similar Lambda Labs Workstation. What type of CPU do you need? or What GPU is good for your use case. How much RAM? This video breaks it all down.

Outline:
00:01:30 - GPU
00:03:59 - CPU
00:05:38 - RAM
00:06:21 - Motherboard
00:06:55 - Storage
00:07:44 - Power Supply
00:08:19 - Cooling
00:08:51- Case
00:09:15 - Parts Compatibility
00:10:01 - Training on Cloud vs Your Own Machine

Reference:
https://l7.curtisnorthcutt.com..../build-pro-deep-lear

parts list: https://pcpartpicker.com/user/....learnedvector/saved/

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Lambda Labs
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You want to learn what Data Science is? And you like brief explanations? Then this is the perfect video for you!

Video Timestamps:
0:00-0:23 Video intro
0:23-0:50 Why the data science notion is still confusing to people
0:50-1:09 Data science is an interdisciplinary field
1:09-1:35 Data science and AI
1:35-1:46 Data science, ML, Deep Learning
1:46-2:09 Channel promo
2:09-2:19 What you will see next
2:19-2:51 Types of data used
2:51-3:36 Activities performed by data scientists
3:36-3:54 Time allocation
3:54-4:06 Key skills needed by data scientists
4:06-4:24 Frequently used data science methods
4:24-5:21 How data scientists create value

We will try to provide a dynamic overview of the data science profession along with some key aspects such as:

✔️ Type of data used:
✔️ Activities performed on the job
✔️ Time allocation
✔️ Key skills
✔️ And frequently used data science methods

We’ll focus on how data scientists create value for companies and will explain how data science relates to AI, Machine Learning, and Deep Learning.

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At 365 Data Science we make #DataScience accessible to all by providing the highest quality online data science education. Our courses are designed to build up your skill set from beginner to job-ready, regardless of your current level, background education, and experience.

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Our mission is to close that gap. We provide high quality, affordable training to open new opportunities for students and professionals all over the world. We are committed to hiring top talent and helping everyone in the company reach their full potential. We care about our students, colleagues and the industry and are committed to their advancement.

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Learn more about Transformers → http://ibm.biz/ML-Transformers
Learn more about AI → http://ibm.biz/more-about-ai
Check out IBM Watson → http://ibm.biz/more-about-watson

Transformers? In this case, we're talking about a machine learning model, and in this video Martin Keen explains what transformers are, what they're good for, and maybe ... what they're not so good at for.

Download a free AI ebook → http://ibm.biz/ai-ebook-free
Read about the Journey to AI → http://ibm.biz/ai-journey-blog

Get started for free on IBM Cloud → http://ibm.biz/Bdf7QA
Subscribe to see more videos like this in the future → http://ibm.biz/subscribe-now

#AI #Software #ITModernization

Why didn't OpenAI release their "Unicorn" GPT2 large transformer? Rob Miles suggests why it might not just be a a PR stunt.

Unicorn AI: https://youtu.be/89A4jGvaaKk
Unicorn AI (More Examples): https://youtu.be/p-6F4rhRYLQ
Generative Adversarial Networks (GANs): https://www.youtube.com/watch?v=5oXyibEgJr0

More from Rob Miles: http://bit.ly/Rob_Miles_YouTube

Thanks to Nottingham Hackspace for providing the filming location: http://bit.ly/notthack

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This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: https://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com