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A friendly introduction to neural networks and deep learning.

This is a follow up to the Introduction to Machine Learning video.
https://www.youtube.com/watch?v=IpGxLWOIZy4

Note: In this tutorial I use natural logarithms. If you used logarithms base 10, you may get different answers that I got, although at the end it doesn't matter, since using a different base for the logarithm just scales all the logarithms by a constant.

00:00 What is machine learning?
2:22 Gradient descent
5:07 Neural network
10:11 logistic regression
12:28 Probability
14:57 Activation Function
19:56 Error function
22:34 Node(Neuron)
24:07 Non-linear regions
31:22 Deep neural network

In this video, I am building Deep Learning PC build from scratch. Now just a disclaimer, in order to learn deep learning you don't need to have such an expensive setup. Whatever laptop or personal computer you have should still work ok. If you have a requirements for running heavy jobs, you can utilize cloud for that. For me, NVIDIA gave 2500$ Titan RTX GPU for free and than I spent close to 1000$ for other parts to build this PC. My plan is to run some heavy deep learning jobs on this computer and I will be making videos on that in future.
Special thanks to my nephew (Harsh Patel) who helped me a lot during entire PC build process. Thanks Harsh! :)

Do you want to learn technology from me? Check https://codebasics.io/ for my affordable video courses.

PC Parts
========
Processor: AMD Ryzen 7 3700 8-Core
Motherboard: Asus ROG Strix B450-F Gaming Motherboard
GPU: NVIDIA Titan RTX
SSD: Sabrent Rocket Q 1TB SSD
Power Supply: Thermaltake 650 W power supply
32 GB RAM
Cooling Graphite Thermal Pad (To keep the CPU cool)
BenQ 27 inch monitor
Thermaltake V200 Tempered Glass Case (Cabin)
Magnetic LED strip (for inside lighting)
Wireless keyboard and mouse

Website: https://codebasics.io/
Facebook: https://www.facebook.com/codebasicshub
Twitter: https://twitter.com/codebasicshub

Lista:

Arxiv Insights
bycloud
Daniel Bourke
DeepMind
OpenAI
facebook ai
PyTorch
PyTorch Lightning
Roboflow
Two Minute Papers
What's AI
Yannic Kilcher
3Blue1Brown
CodeEmporium
DeepLearning.TV
deeplizard
henry ai
Jeremy Howard
Jabrils
Karol Majek

Neural Networks and Deep Learning
Artificial Intelligence - All in One
Data Science Dojo
Machine Learning Study Groups
ML in PL
MIT OpenCourseWare
the Artificial Intelligence Channel
Yang Xu
ML Explained - Aggregate Intellect - AI.SCIENCE
Jordan Harrod
DeepMind
Machine Learning Street Talk
wasspPL
Aladdin Persson
Abhishek Thakur

This is a clip from a conversation with Gary Marcus from Oct 2019. New full episodes every Mon & Thu and 1-2 new clips or a new non-podcast video on all other days. You can watch the full conversation here: https://www.youtube.com/watch?v=vNOTDn3D_RI
(more links below)

Podcast full episodes playlist:
https://www.youtube.com/playli....st?list=PLrAXtmErZgO

Podcasts clips playlist:
https://www.youtube.com/playli....st?list=PLrAXtmErZgO

Podcast website:
https://lexfridman.com/ai

Podcast on iTunes:
https://apple.co/2lwqZIr

Podcast on Spotify:
https://spoti.fi/2nEwCF8

Podcast RSS:
https://lexfridman.com/category/ai/feed/

Note: I select clips with insights from these much longer conversation with the hope of helping make these ideas more accessible and discoverable. Ultimately, this podcast is a small side hobby for me with the goal of sharing and discussing ideas. For now, I post a few clips every Tue & Fri. I did a poll and 92% of people either liked or loved the posting of daily clips, 2% were indifferent, and 6% hated it, some suggesting that I post them on a separate YouTube channel. I hear the 6% and partially agree, so am torn about the whole thing. I tried creating a separate clips channel but the YouTube algorithm makes it very difficult for that channel to grow unless the main channel is already very popular. So for a little while, I'll keep posting clips on the main channel. I ask for your patience and to see these clips as supporting the dissemination of knowledge contained in nuanced discussion. If you enjoy it, consider subscribing, sharing, and commenting.

Gary Marcus is a professor emeritus at NYU, founder of Robust.AI and Geometric Intelligence, the latter is a machine learning company acquired by Uber in 2016. He is the author of several books on natural and artificial intelligence, including his new book Rebooting AI: Building Machines We Can Trust. Gary has been a critical voice highlighting the limits of deep learning and discussing the challenges before the AI community that must be solved in order to achieve artificial general intelligence.

Subscribe to this YouTube channel or connect on:
- Twitter: https://twitter.com/lexfridman
- LinkedIn: https://www.linkedin.com/in/lexfridman
- Facebook: https://www.facebook.com/lexfridman
- Instagram: https://www.instagram.com/lexfridman
- Medium: https://medium.com/@lexfridman
- Support on Patreon: https://www.patreon.com/lexfridman

In this first video of this series in object detection we try to understand what object detection is and how it works. We also look at an overview of model architectures in object detection such as a sliding windows approach, regional based family of models (r-CNN) and lastly a quick overview of Yolo which we will go into more in depth (and code from scratch) in a future video!

GitHub Repository:
https://github.com/aladdinpers....son/Machine-Learning

✅ Equipment I use and recommend:
https://www.amazon.com/shop/aladdinpersson

❤️ Become a Channel Member:
https://www.youtube.com/channe....l/UCkzW5JSFwvKRjXABI

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▶️ You Can Connect with me on:
Twitter - https://twitter.com/aladdinpersson
LinkedIn - https://www.linkedin.com/in/al....addin-persson-a95384
GitHub - https://github.com/aladdinpersson

Object Detection Playlist:
https://youtube.com/playlist?l....ist=PLhhyoLH6Ijfw0Tp

PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf