A self-driving car in GTA 5.

For more information on this project, and how it all began from simple lane detection to deep learning, follow the full tutorial series here: https://pythonprogramming.net/....game-frames-open-cv-

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Welcome to the next tutorial covering deep learning with Python, Tensorflow, and Keras. We've been working on a cryptocurrency price movement prediction recurrent neural network, focusing mainly on the pre-processing that we've got to do. In this tutorial, we're going to be finishing up by building our model and training it.

Text tutorials and sample code: https://pythonprogramming.net/....crypto-rnn-model-dee

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Welcome to a tutorial on sockets with Python 3. We have a lot to cover, so let's just jump right in. The socket library is a part of the standard library, so you already have it.

Source code and text-based tutorial: https://pythonprogramming.net/....sockets-tutorial-pyt

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#sockets #python #tutorial

DreamFusion is Google research (https://arxiv.org/pdf/2209.14988.pdf) into generating 3D objects from text prompts, and is based off 2D diffusion models.

Stable DreamFusion github: https://github.com/ashawkey/st....able-dreamfusion?s=0

NeRF (Neural Radiance Fields): https://www.matthewtancik.com/nerf
Meta AI make-a-video: https://makeavideo.studio/

Neural Networks from Scratch book: https://nnfs.io
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Downloading and installing Python 3, along with an editor and writing our very first, though super basic, program!

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

#python #programming #tutorial

Welcome to a reinforcement learning tutorial. In this part, we're going to focus on Q-Learning.

Q-Learning is a model-free form of machine learning, in the sense that the AI "agent" does not need to know or have a model of the environment that it will be in. The same algorithm can be used across a variety of environments.

For a given environment, everything is broken down into "states" and "actions." The states are observations and samplings that we pull from the environment, and the actions are the choices the agent has made based on the observation. For the purposes of the rest of this tutorial, we'll use the context of our environment to exemplify how this works.

Text-based tutorial and sample code: https://pythonprogramming.net/....q-learning-reinforce

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#reinforcementlearning #machinelearning #python

Using Python to Play Cyberpunk 2077 through computer vision.

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

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Basic Raspberry Pi Robot Car Playlist: http://www.youtube.com/watch?v=LlFkybEQFFA&list=PLQVvvaa0QuDeJlgD1RX9_49tMLUxvIxF4&feature=share

Greetings and welcome to the second part of our robotics with python and the raspberry pi! In this video, the basic assembly is shown for how to hook up our pi to the l298n controller, battery pack, and motors.

After the initial hooking up, you are shown an extremely basic python script that will make the motor run momentarily and then stop. As we continue, we will build slightly more complex code, and get the motors to work with each other.

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An actually-open-AI model is available for you to download the weights to and tinker with, GPT-J. It's a 6 billion parameter large language model that can do long-form natural language generation, do programming, be a chatbot, do translations, and much more.

GPT-J Text examples: https://pythonprogramming.net/....GPT-J-6b-parm-transf
GPT-J Github: https://github.com/kingoflolz/mesh-transformer-jax
The Pile paper: https://arxiv.org/pdf/2101.00027.pdf
GPT-J Web demo: https://6b.eleuther.ai/
EleutherAI: https://www.eleuther.ai/
Multimodal Few-Shot Learning with
Frozen Language Models: https://arxiv.org/pdf/2106.13884.pdf

Neural Networks from Scratch book: https://nnfs.io
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Exploring a couple of the latest text to image generators that you can begin using right now, Stable Diffusion and MidJourney.

Stable Diffusion model download: https://huggingface.co/CompVis..../stable-diffusion-v1
MidJourney website: https://www.midjourney.com/home/
BLOOM AI model video: https://www.youtube.com/watch?v=3EjtHs_lXnk
DreamStudio: https://beta.dreamstudio.ai

Neural Networks from Scratch book: https://nnfs.io
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Working on a chatbot that you can speak to with your microphone and that responds via your speakers.

Part 2: https://youtu.be/lheXIYXanuU

Setting up and checking out jarvis: https://youtu.be/fQzjgaKSrkc
DialoGPT large: https://huggingface.co/microsoft/DialoGPT-large

Learn more about NVIDIA Jarvis: https://nvda.ws/2QC3NYv

See more of the DGX Station A100 here: https://youtu.be/0mAesfFt4us

Neural Networks from Scratch book: https://nnfs.io
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Welcome to another Python 3 basics tutorial. This tutorial is going to be covering the basics of classes in python. For the most part, I just want you to just understand how to read and understand a class' workings. You can think of classes as groupings of functions, usually. Classes quickly work their way into "intermediate" programming, so hopefully I can just help you understand how they work and how to follow code that uses them.

Sample code for this basics series: http://pythonprogramming.net/b....eginner-python-progr

Python 3 Programming tutorial Playlist: http://www.youtube.com/watch?v=oVp1vrfL_w4&feature=share&list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M

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GAN Theft Auto is a Generative Adversarial Network that recreates the Grand Theft Auto 5 environment. It is created using a GameGAN fork based on NVIDIA's GameGAN research.

With GAN Theft Auto, the neural network *is* the environment and you can play within it.

Github: https://github.com/sentdex/GANTheftAuto/

Unboxing and reviewing the DGX Station A100 80GB: https://www.youtube.com/watch?v=0mAesfFt4us

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

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An updated deep learning introduction using Python, TensorFlow, and Keras.

Text-tutorial and notes: https://pythonprogramming.net/....introduction-deep-le

TensorFlow Docs: https://www.tensorflow.org/api_docs/python/
Keras Docs: https://keras.io/layers/about-keras-layers/

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My home lab journey.

Part 2: https://youtu.be/BVWmstt0AWM

Server rack:
StarTech.com 42U 19" Open Frame Server Rack - 4 Post Adjustable Depth 22-40" https://amzn.to/3NSeQ8B

Rails:
1U 19 inch Server Rack Rails: https://amzn.to/3OhHHD9

Switch:
10G 8 Port TP-Link switch: https://amzn.to/3NTRRKn

NAS Build:
Case: Rosewill 4U 12-bay hot swap server rack casing: https://amzn.to/3xks2fm
Motherboard: GIGABYTE Z590 AORUS Master: https://amzn.to/3tzCHlj
CPU: Intel® Core™ i5-11600K: https://amzn.to/3Of9W5f
RAM: Corsair Vengeance LPX 32GB: https://amzn.to/3NUJM80
Power Supply: Corsair RM850x: https://amzn.to/3xLYqJh
4x 18TB: Seagate Exos X18 18TB Enterprise HDD: https://amzn.to/3MKwM3H
3x 120mm Noctua fans: https://amzn.to/3QlXi6j
2x 80mm Noctua fans: https://amzn.to/3xOFb1w

Super quiet Puget Workstation build: https://hubs.ly/H0-By8Q0

PCIe SATA Expansion: https://amzn.to/3Odm9Yi

TrueNAS Core 12.0 install tutorial video I used: https://www.youtube.com/watch?v=nVRWpV2xyds

Neural Networks from Scratch book: https://nnfs.io
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Contents:
00:00 - Server rack types
00:30 - StarTech 42U 4 Post Open Frame Server Rack
01:15 - Network Area Storage (NAS)
02:42 - Prebuilt NAS Options (QNAP vs Synology)
03:36 - Custom-built rack-mount NAS
07:52 - Server rack Power Distribution Unit (PDU) (Tripplite PDU1230)
09:48 - Server Rack/Homelab networking (TP-Link TL-SX1008) 8x 10G
11:35 - StarTech 1U 19" Server Rack Rails
12:21 - TrueNAS 13 w/ RAIDZ1
14:03 - Why HomeLab?

#homelab #server

My thoughts and some examples of Github Copilot.

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