Nicholas Renotte
Deep Reinforcement Learning for Atari Games Python Tutorial | AI Plays Space Invaders
updated
This was a labour of love but you guys gave me so much of your trust that I want to repay the favour. I wouldn't be here without you guys and I truly, deeply appreciate it. So....I give you NODDY v1.
It's a full end to end DETR object detection pipeline, in this video its fine tuned for detecting signs but it could definitely be repurposed for other object detection tasks. It covers:
⚡️ Detections using my pre-trained transformer
💾 Collecting custom data for you to train your own
🏋🏽♀️ Fine tuning, with or without pre-trained weights
📹 ANNNNNNND Real time detections...ay ay ayyyy
Okay I've been finishing the editing on this video for what feels like six hours now and I'm a little delirious so I'll leave it there. Enjoy guys, hope you like it, I'm going to McDonalds.
Love y'all. Happy coding!
Code: github.com/nicknochnack/SignDETR
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This pretty much covers all the fundamentals you need to get up and running with langgraph. Importing a LLM, setting up the state, constructing the graph itself and then calling it. Also noticed a little bug with the yfinance documentation but I covered it in the video, just make sure you use size=5 instead of count=5 and you should be good to go.
Anyway, love ya all. Happy coding!
Code: github.com/nicknochnack/LanggraphCrashCourse
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I want to keep this one going as a live project so i’ll be monitoring any issues you guys have a bit more closely. If you get stuck let me know and I’ll make as many updates as I can. Anyway, hope you enjoy it guys!
Code: github.com/nicknochnack/ViTCandlesticks
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Stuff I found useful:
Albumentations: albumentations.ai/docs
CutMix/Mixup: docs.pytorch.org/vision/main/auto_examples/transforms/plot_cutmix_mixup.html
Lucid Rains ViT for PyTorch: github.com/lucidrains/vit-pytorch/tree/main/vit_pytorch
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We just released a couse on Deeplearning.AI 🤖 🦾 🚀 around all the amazing stuff you can do with ACP! In it you'll learn:
1. The differences between MCP vs ACP vs A2A
2. How to build ACP compliant agents
3. How to run pre-setup ACP agents like gpt-researcher, aider and more
4. Getting it all to work together as an agent system
Thanks to Hawraa, Andrew and the team for the backing on this was an amazing project to get together. And a huge shoutout to my amazing co-lecturer Sandi, you smashed it.
This was months of planning, coding, editing and recording to get this out but I'm super proud of the final product. Go check it out, link in the comments!
P.s. I still have the photo in my office 😉
Anyway, had a ton of fun coding this up and finally getting back to some proper ML. I've been thinking about just swapping out the pdf with some specific libraries to get an ultimate Blender bot or Manim bot (also learned it for the video...we're boosting animation quality this year!!).
Would love to see what you end up fine tuning and deploying. If you need a hand just ping me I'm flying out tonight but will answer as much as i can. Love ya, thanks for watching!
Code: github.com/nicknochnack/EndToEndLoRA
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Documentation: https://agentcommunicationprotocol.dev/
GitHub Repo for the Library: github.com/i-am-bee/acp
So I think getting LLMs to work together is going to be critical to getting huge amounts of value from them. Right now most deployments that I've seen have been single use case, single agent type stuff.
But imagine...if we could get them to work together?
This is where Agent Communication Protocol comes in, it provides a vendor, framework and language agnostic way to deploy agent servers. The biggie is that this is going to make it easier for agents to talk to each other across the net and between organizations. I show a pretty simple example here but collaboration is the big play, I think there's interesting things to come.
Happy coding ✌️
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Disclaimer: This has been developed for academic purposes. Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing.
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Probably will get back to reinforcement learning for ML and some deep visualisation stuff I've been wanting to get my hands on, alas I digress. In this, you're going to learn;
1. How to hook up a bot to Kraken
2. How to setup a LLM to help make trading decisions (see performance for yourself don't at me with "you should've used {insert fav llm here} llm" 😅
3. How to refactor all the code you just wrote and use finbert.
All the code, logs and stuff is available via GitHub as always: github.com/nicknochnack/LLMAgentCrypto
Happy coding ✌️
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Disclaimer: This has been developed for academic purposes. Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing.
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Wanted to give MCP a crack so here ya go. Bit of slightly different style of vid but I'm experimenting in the new YouTube studio. Hope y'all enjoy it, if you get stuck, drop me a comment and I'll be checking it out. I've included pretty detailed instructions in the GitHub repo as well and allll the code is there.
Happy coding ✌️
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Love y'all
Nick
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Disclaimer: This has been developed for academic purposes. Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing. These are all my views and not those of anyone else, my clients or my employer, I mean who else could come up with these ideas tbf 😅.
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🤖 Get the Code:github.com/nicknochnack/Falcon180b
Check out Falcon 180b the all new model from TII UAE.
Falcon 180b was released on the 6th of September 2023 by the TII UAE. It's a 180 billion parameter decoder only model that was trained on 3.5 billion tokens with a context window of 2048 tokens. What makes it unique is that it performs exceptionally well with the authors claiming it ranks just behind GPT4 and on par with Google’s PaLM 2 Large, in spite of being half the size of that model.
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Learn how to use Llama 2 70B Chat for Retrieval Augmented Generation...for FINANCE! Albeit in a hella haphazard way, oh, we'll also build a streamlit app while we're at it.
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Also, you can try it out here: dataplatform.cloud.ibm.com/wx/home?context=wx
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P.s. Let me know how you go and drop a comment if you need a hand!
#machinelearning #python #datascience
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Links:
MusicLM Examples: google-research.github.io/seanet/musiclm/examples
Paper: arxiv.org/pdf/2301.11325.pdf
MusicCaps: kaggle.com/datasets/googleai/musiccaps
AudioLM Examples: google-research.github.io/seanet/audiolm/examples
Paper: arxiv.org/pdf/2209.03143.pdf
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Nick
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#machinelearning #python #datascience
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Nick
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Get the code: github.com/nicknochnack/LipNet
Links: tensorflow.org/api_docs/python/tf/data
Original Paper: arxiv.org/abs/1611.01599
Associated Code for Paper: github.com/rizkiarm/LipNet
ASR Tutorial: keras.io/examples/audio/ctc_asr/#model
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Github repo for the code: github.com/nicknochnack/ImagePatches
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Github repo for the code: github.com/nicknochnack/TensorflowDecisionForests
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Nick
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#machinelearning #python #datascience
Github repo for the code: github.com/nicknochnack/TensorflowDecisionForests
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Nick
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#machinelearning #python #datascience
Github repo for the code: github.com/nicknochnack/TensorflowDecisionForests
Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off:
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Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!
#machinelearning #python #datascience
Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off:
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💼 Find AWESOME ML Jobs: jobsfromnick.com
Oh, and don't forget to connect with me!
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Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!
#machinelearning #python #datascience
Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off:
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Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!
#machinelearning #python #datascience
Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off:
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💼 Find AWESOME ML Jobs: jobsfromnick.com
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Nick
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#machinelearning #python #datascience
Good to be back to Code That!
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Thanks again to Seeed Studio for the Jetson Nano, check them out here: seeedstudio.com/reComputer-J1020-v2-p-5498.html
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Happy coding!
Nick
Get notified of the free Python course on the home page at coursesfromnick.com
Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off:
coursesfromnick.com/bundles/fullstackml
Hopefully you enjoyed this video.
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Oh, and don't forget to connect with me!
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Nick
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You'll get a blast fast walkthrough of how to download and convert Pascal VOC annotations to YOLO format and how to apply them to a Kaggle Dataset using Yolov5 and Python.
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🤖 Get the CODE: github.com/nicknochnack/StopSign
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Nick
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#machinelearning #codingchallenge #react #javascript


