Using some basic computer vision techniques with #Python and #OpenCV to have a simple AI walk around in #cyberpunk2077.

Series playlist: https://www.youtube.com/playli....st?list=PLQVvvaa0QuD

Sample code: https://github.com/Sentdex/cyberpython2077

Keys file: https://github.com/Sentdex/pyg....ta5/blob/master/keys
Grabscreen file: https://github.com/Sentdex/pyg....ta5/blob/master/grab

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Imagine an AI where, all in the same model you could Translate languages, Write code, solve crossword puzzles, Be a chatbot and do a whole bunch of other crazy things.

In this video, we check out the BLOOM large language model. A free and totally open source 176B parameter LLM.

BLOOM model: https://huggingface.co/bigscience/bloom

Quick examples of running BLOOM locally and/or via API: https://github.com/Sentdex/BLOOM_Examples

Neural Networks from Scratch book: https://nnfs.io
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Contents:

0:00 - BLOOM model basics
3:05 - What's a Large Language Model (LLM)?
4:06 - What's Prompting?
6:40 - BLOOM Training Data & Model Behavior
9:09 - Tokens & Tokenization
12:03 - Using your $5M AI (How to prompt)
16:49 - Advanced Prompt examples
21:16 - What's Next?


#deeplearning #artificialintelligence

Learning how to read EEG data in Python for the purposes of creating a brain computer interface with hopes of doing things like controlling characters in a game and hopefully much more!

https://openbci.com/

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#BCI #EEG #Python

Tinkering with reinforcement learning via Stable Baselines 3 and Starcraft 2.

Code and model: https://github.com/Sentdex/SC2RL

Stable Baselines 3 tutorial: https://pythonprogramming.net/....introduction-reinfor

Neural Networks from Scratch book: https://nnfs.io
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#artificialintelligence #machinelearning #python

Welcome to a tutorial for building artificial intelligence agents within Blizzard's Starcraft 2 game. I visited this topic some years ago, but quite a bit has changed, so I figured I would revisit it again. For those of you who are new to Starcraft 2 (SC2) or it's been a while, the game at a basic level is actually fairly easy to understand. In order to be highly competitive, lots of factors can indeed come into play, but if you're looking for a fun environment to try your programming and maybe even deep learning/reinforcement learning skills, this is a pretty good choice.

Text-based tutorial: https://pythonprogramming.net/....intro-python-sc2-int

Asyncio basics: https://pythonprogramming.net/....asyncio-basics-inter

Neural Networks from Scratch book: https://nnfs.io
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Pondering the future of user interfaces with advancements in natural language processing and artificial intelligence

Tweet in reference: https://twitter.com/c_valenzue....lab/status/157444845

Neural Networks from Scratch book: https://nnfs.io
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Welcome to a tutorial series covering how to do reinforcement learning with the Stable Baselines 3 (SB3) package. The objective of the SB3 library is to be for reinforcement learning like what sklearn is for general machine learning.

Text-based tutorial and sample code: https://pythonprogramming.net/....introduction-reinfor

Neural Networks from Scratch book: https://nnfs.io
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OpenAI's ChatGPT AI (a neural network) functioning as a Linux system that I am able to navigate, read, write and execute scripts on.

Neural Networks from Scratch book: https://nnfs.io
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OpenAI's Whisper is a speech to text, or automatic speech recognition model. It is a "weakly supervised" encoder-decoder transformer trained on 680,000 hours of audio. Not only can it transcribe English, it can transcribe 96 other languages along with also being able to translate from those languages to English.

Neural Networks from Scratch book: https://nnfs.io
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Building neural networks from scratch in Python introduction.

Neural Networks from Scratch book: https://nnfs.io

Playlist for this series: https://www.youtube.com/playli....st?list=PLQVvvaa0QuD

Python 3 basics: https://pythonprogramming.net/....introduction-learn-p
Intermediate Python (w/ OOP): https://pythonprogramming.net/....introduction-interme

Mug link for fellow mug aficionados: https://amzn.to/2xcyfPC

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#nnfs #python #neuralnetworks

ChatGPT is the latest GPT style generative AI model from OpenAI, which, at it's most basic level acts as a chatbot, but this back and forth structure allows for many complex capabilities.

Chat with ChatGPT here: https://chat.openai.com/chat

Operating system example with the script writing to a file: https://twitter.com/Sentdex/status/1600609223548739585?t=3KJw_yb6hPjDNbQoI27ScA&s=19

Neural Networks from Scratch book: https://nnfs.io
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StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

Course Materials: https://github.com/maziarraiss....i/Applied-Deep-Learn

In this video I explain LDA and go through a tutorial paper on how it works using collapsed gibbs sampling. The next video will be a implementation video :)

This method is an old school method and it's not clear how useful it is today, but a lot of course teach this topic so it must have some historical value I feel. It's one of Andrew Ng's most cited papers!

Paper tutorial: https://coli-saar.github.io/cl....19/materials/darling

Timestamps:
0:00 - Introduction
1:18 - What is topic modelling?
2:41 - LDA
13:30 - Posterior Inference
15:09 - Gibbs Sampling
18:06 - Collapsed Gibbs
21:50 - Finding conditional probability
26:48 - Implementation
30:08 - Pseudo code
32:25 - Ending

In this video we do a paper walk through of the GCN (Graph Convolutional Network) paper that is the most cited and one of the impactful in graph neural networks. We understand how they derived it and how it works in detail.

Big thanks to MakinaRocks for sponsoring this video, and I encourage you to check out Link which is a jupter lab extension they have developed to make notebooks more intuitive and easier to use. A few of the features I’ve found useful are:
1️⃣ Caching result from running cells in notebooks to avoid re-running them
2️⃣ Visualizing cell dependencies
3️⃣ Integrated version control

Check it out⬇️
https://bit.ly/3F4COvv

Link Demo Video 👉 https://youtu.be/uM2uPG-1eQQ
Link Documentation 👉https://makinarocks.gitbook.io/link/

Timestamps:
0:00 - Introduction
1:00 - Sponsored Segment: Link
2:09 - Abstract and overview
4:13 - Introduction to the problem
8:53 - How GCNs work
13:47 - Theory of GCN derivation
21:25 - GCN Node classification example
24:33 - Conclusions & Results
25:13 - Ending thoughts

#link #gcn

A quick look at CBOW and how it looks like it in code. I explained how this model works in the previous example so check that out before you look at this one to get a better understanding.

Timestamps:
0:00 - Introduction
0:48 - Imports
0:53 - CBOW model
5:10 - Example of how to train it
8:52 - Ending