Pixel Pioneers Tutorials

This repository contains a collection of tutorials and projects created by the Pixel Pioneers Community. Each project focuses on different aspects of machine learning and artificial intelligence.

Files will be modified and updated continuously as projects are often revisited with new methods when relevant new papers are released.

Projects

The Pixel Pioneers Community has developed a wide range of tutorials and projects that span various tools, techniques, and use cases in machine learning and artificial intelligence. Below are some highlighted categories and examples of the projects within them:

Graph Neural Networks

• Example Project: Graph Neural Networks for Material Generation
  • Description: Exploring the use of graph neural networks for generating novel materials.
  • File: Graph Neural Networks for Material Generation/train.py

Reinforcement Learning

• Example Project: RL for Optimizing Material Synthesis
  • Description: Applying reinforcement learning techniques to optimize the process of material synthesis.
  • File: Reinforcement_Learning_for_Optimizing_Material_Synthesis_.ipynb

Robotics

• Example Project: Assistive Robot
  • Description: Developing an assistive robot using machine learning algorithms.
  • File: Assistive_Robot.ipynb

Bioprinting

• Example Project: Bioprinting
  • Description: Investigating the applications of machine learning in bioprinting technology.
  • File: BIOPRINTING.ipynb

Graph Analysis

• Example Project: CRAG
  • Description: Implementing the CRAG algorithm for graph analysis.
  • File: CRAG.ipynb

High-Performance Machine Learning

• Example Project: JAX Tutorial 3
  • Description: A tutorial on using JAX, a high-performance machine learning library.
  • File: JAX_Tutorial_3.ipynb

Language Model Optimization

• Example Project: Layer Selective Rank Reduction in Language Models
  • Description: Exploring techniques for reducing the rank of layers in language models.
  • File: Layer_Selective_Rank_Reduction_in_Language_Models.ipynb

Transitioning to Advanced Tools

• Example Project: Pixel Pioneer Tutorial 2: From NumPy to JAX using Caloric Counting
  • Description: A tutorial on transitioning from NumPy to JAX using a caloric counting example.
  • File: Pixel_Pioneer_Tutorial_2_From_NumPy_to_JAX_using_Caloric_Counting.ipynb

Retrieval Augmented Generation

• Example Project: Retrieval Evaluator and Action Trigger for Retrieval Augmented Generation
  • Description: Implementing a retrieval evaluator and action trigger for retrieval augmented generation.
  • File: Retrieval_Evaluator_and_Action_Trigger_for_Retrieval_Augmented_Generation.ipynb

Multi-Agent Graph Environment (MAGE) for Robotic Control

• Example Project: Coordinated Multi-Robot Exploration and Mapping

• Description: The MAGE framework enables the coordination and collaboration of multiple robots for efficient exploration and mapping of unknown environments. By representing the robot team as a graph, MAGE facilitates information sharing, task allocation, and decision-making among the robots. The framework allows for the integration of various algorithms for path planning, localization, and mapping, enabling the robots to collaboratively build a comprehensive map of the environment while optimizing their exploration strategies.

• File: MAGE_Coordinated_Multi_Robot_Exploration_and_Mapping.py

Interactive Art with AI

• Example Project: Stability AI Interactive Art: Generate Layered Prompts
  • Description: Generating layered prompts for interactive art using Stability AI.
  • File: Stability_AI_Interactive_Art_generate_layered_prompts.ipynb

Growing Food on Mars usin Python

• This is a class I taught in 2022 for high school seniors

Please find details of the different tutorials in the repository and some more information on them as it relates to the objective of the project, tools and libriaries used and details regarding data processing

Tutorial	Objective	Keywords	Tools Used	Libraries	Use Case	Data and Data Processing
Assistive_Robot.ipynb	Develop an assistive robot with AI capabilities	Robot, AI, Assistive Technology	Jupyter Notebook, Python	TensorFlow, OpenCV	Robotics	Sensor data from various robot sensors; preprocessing includes noise reduction, normalization, and feature extraction using TensorFlow and OpenCV
BIOPRINTING.ipynb	Explore bioprinting techniques using 3D printers	Bioprinting, 3D Printing, Biomedical Engineering	Jupyter Notebook, Python	NumPy, SciPy	Biomedical Engineering	3D model data for bioprinting; preprocessing involves mesh generation, smoothing, and structural analysis using NumPy and SciPy
CRAG.ipynb	Implement CRAG for advanced AI applications	CRAG, AI, Machine Learning	Jupyter Notebook, Python	PyTorch, scikit-learn	General AI	Graph data for analysis; includes node and edge feature extraction, normalization, and graph transformations using PyTorch and scikit-learn
JAX_Tutorial_3.ipynb	Learn advanced techniques in JAX	JAX, Machine Learning, AI	Jupyter Notebook, Python	JAX, NumPy	General AI	Various datasets for machine learning tasks; includes data augmentation, normalization, and batching using JAX and NumPy
Layer_Selective_Rank_Reduction_in_Language_Models.ipynb	Optimize language models using rank reduction	Language Models, Rank Reduction, Optimization	Jupyter Notebook, Python	Transformers, PyTorch	Natural Language Processing	Text data from language corpora; preprocessing involves tokenization, embedding generation, and rank reduction techniques using Transformers and PyTorch
Pixel_Pioneer_Tutorial_2_From_NumPy_to_JAX_using_Caloric_Counting.ipynb	Transition from NumPy to JAX with a practical example	NumPy, JAX, Caloric Counting	Jupyter Notebook, Python	NumPy, JAX	Nutrition	Nutritional data including caloric values; preprocessing involves data cleaning, normalization, and transformation using NumPy and JAX
Retrieval_Evaluator_and_Action_Trigger_for_Retrieval_Augmented_Generation.ipynb	Develop retrieval-augmented generation models	Retrieval, Generation, AI	Jupyter Notebook, Python	Transformers, PyTorch	General AI	Text data for retrieval tasks; preprocessing includes indexing, retrieval evaluation, and action triggering using Transformers and PyTorch
Stability_AI_Interactive_Art_generate_layered_prompts.ipynb	Create interactive art with AI and layered prompts	AI Art, Interactive Art, Prompts	Jupyter Notebook, Python	Stable Diffusion, PyTorch	Art	Image and text data for art generation; preprocessing involves data augmentation, layering, and style transfer using Stable Diffusion and PyTorch
Testing_Librosa_Library_for_Music_Generation.ipynb	Test and explore music generation with Librosa	Music Generation, Librosa, Audio Processing	Jupyter Notebook, Python	Librosa, NumPy	Music	Audio data including waveforms and spectrograms; preprocessing involves feature extraction, noise reduction, and transformation using Librosa and NumPy
Vanishing_Gradients_Check.ipynb	Detect and address vanishing gradient issues in neural networks	Vanishing Gradients, Neural Networks, Deep Learning	Jupyter Notebook, Python	TensorFlow, Keras	General AI	Training data for neural networks; preprocessing includes normalization, gradient monitoring, and model adjustments using TensorFlow and Keras
biprinting_2.ipynb	Advanced techniques in bioprinting	Bioprinting, 3D Printing, Biomedical Engineering	Jupyter Notebook, Python	NumPy, SciPy	Biomedical Engineering	Detailed 3D models for bioprinting; preprocessing involves advanced mesh manipulation, slicing, and material distribution analysis using NumPy and SciPy
Reinforcement_Learning_for_Optimizing_Material_Synthesis_.ipynb	Optimize material synthesis using reinforcement learning	Reinforcement Learning, Material Synthesis, Optimization	Jupyter Notebook, Python	Stable Baselines3, Gym	Environmental Science	Simulated material synthesis data; preprocessing includes environment setup, simulation data collection, and reward signal design using Stable Baselines3 and Gym
jax_nutritional_content_prediction_parallel.py	Predict nutritional content using JAX with parallel processing	JAX, Parallel Processing, Nutritional Content Prediction	Python	JAX, NumPy	Nutrition	Nutritional datasets; preprocessing involves parallel data loading, normalization, and feature extraction using JAX and NumPy
jax_nutritional_content_prediction_optimizers.py	Optimize nutritional content prediction models using JAX	JAX, Optimization, Nutritional Content Prediction	Python	JAX, Optax	Nutrition	Nutritional data including ingredient compositions; preprocessing involves optimizer configuration, data normalization, and training loops using JAX and Optax
jax_nutritional_content_prediction_functional.py	Implement functional programming techniques in JAX for nutritional content prediction	JAX, Functional Programming, Nutritional Content Prediction	Python	JAX, NumPy	Nutrition	Functional data processing techniques; preprocessing involves functional transformations, mapping, and reduction operations using JAX and NumPy
jax_nutritional_content_prediction_flax.py	Use Flax for building neural networks for nutritional content prediction	JAX, Flax, Neural Networks, Nutritional Content Prediction	Python	JAX, Flax	Nutrition	Nutritional data; preprocessing involves neural network construction, data batching, and training using Flax and JAX
jax_nutritional_content_prediction_explicit_diff.py	Explore explicit differentiation in JAX for nutritional content prediction	JAX, Explicit Differentiation, Nutritional Content Prediction	Python	JAX, NumPy	Nutrition	Data for differentiation tasks; preprocessing involves explicit differentiation, data transformation, and optimization using JAX and NumPy
jax_nutritional_content_prediction_custom_transform.py	Create custom transformations in JAX for nutritional content prediction	JAX, Custom Transformations, Nutritional Content Prediction	Python	JAX, NumPy	Nutrition	Nutritional data; preprocessing involves custom data transformations, feature extraction, and normalization using JAX and NumPy
jax_nutritional_content_prediction.py	Predict nutritional content using JAX	JAX, Nutritional Content Prediction	Python	JAX, NumPy	Nutrition	Nutritional datasets; preprocessing includes data cleaning, normalization, and model training using JAX and NumPy
command_line_app_rust.rs	Build a command-line application using Rust	Rust, Command-Line Application	Rust	None	Software Development	Command-line data processing with Rust
material_synthesis_demo.py	Demonstrate material synthesis optimization using Python scripts	Material Synthesis, Optimization, Python	Python	None	Environmental Science	Simulated material synthesis data; preprocessing involves parameter tuning, simulation runs, and optimization analysis
material_synthesis.py	Script for material synthesis	Material Synthesis, Python	Python	None	Environmental Science	Material synthesis data; preprocessing involves data handling, simulation configuration, and result analysis
material.py	Material properties and data handling	Material Data, Properties, Python	Python	None	Environmental Science	Data handling for material properties; preprocessing involves data structuring, validation, and transformation
catboost_carbon_footprint_analysis_environmental_science.py	Analyze carbon footprint using CatBoost	Carbon Footprint, Environmental Science	Python	CatBoost	Environmental Science	Environmental datasets; preprocessing involves feature extraction, normalization, and model training using CatBoost
computer_vision_diet_monitoring_nutrition.py	Monitor diet using computer vision	Computer Vision, Diet Monitoring, Nutrition	Python	OpenCV, TensorFlow	Nutrition	Image data of food items; preprocessing includes image segmentation, feature extraction, and classification using OpenCV and TensorFlow
dialogflow_virtual_health_assistant_healthcare.py	Develop a virtual health assistant using Dialogflow	Dialogflow, Virtual Assistant, Healthcare	Python	Dialogflow API, TensorFlow	Healthcare	Text and voice data for health queries; preprocessing involves natural language understanding, intent recognition, and response generation using Dialogflow API and TensorFlow
gpt4_healthy_recipe_generation_nutrition.py	Generate healthy recipes using GPT-4	GPT-4, Recipe Generation, Nutrition	Python	OpenAI GPT-4 API	Nutrition	Text data of ingredients and recipes; preprocessing includes tokenization, semantic analysis, and recipe generation using GPT-4 API
jax_a3c_reinforcement_learning.py	Implement A3C reinforcement learning with JAX	A3C, Reinforcement Learning, JAX	Python	JAX, Gym	General AI	Simulated environments; preprocessing involves environment interaction, state-action processing, and policy updates using JAX and Gym
jax_autoencoder_anomaly_detection.py	Anomaly detection using autoencoders in JAX	Autoencoders, Anomaly Detection, JAX	Python	JAX, NumPy	General AI	Anomaly detection datasets; preprocessing involves data normalization, encoding-decoding, and anomaly scoring using JAX and NumPy
jax_bert_sentiment_analysis.py	Sentiment analysis using BERT in JAX	BERT, Sentiment Analysis, JAX	Python	JAX, Transformers	Natural Language Processing	Text data for sentiment analysis; preprocessing involves tokenization, embedding extraction, and sentiment classification using BERT and JAX
jax_cifar10_cnn_classification.py	CNN classification on CIFAR-10 using JAX	CNN, CIFAR-10, JAX	Python	JAX, NumPy	Computer Vision	CIFAR-10 image dataset; preprocessing includes image normalization, data augmentation, and CNN training using JAX and NumPy
jax_collaborative_filtering_recommendation.py	Collaborative filtering for recommendation systems in JAX	Collaborative Filtering, Recommendation Systems, JAX	Python	JAX, NumPy	Recommender Systems	User-item interaction data; preprocessing involves matrix factorization, normalization, and recommendation generation using JAX and NumPy
jax_deep_speech_recognition.py	Speech recognition using deep learning in JAX	Speech Recognition, Deep Learning, JAX	Python	JAX, NumPy	Speech Processing	Audio datasets for speech recognition; preprocessing includes feature extraction, spectrogram generation, and model training using JAX and NumPy
jax_denoising_autoencoder.py	Implement denoising autoencoders in JAX	Denoising Autoencoders, JAX, Deep Learning	Python	JAX, NumPy	General AI	Noisy datasets; preprocessing involves noise addition, autoencoder training, and noise reduction using JAX and NumPy
jax_dqn_cartpole.py	Implement DQN for CartPole environment using JAX	DQN, CartPole, JAX	Python	JAX, Gym	Reinforcement Learning	CartPole simulation data; preprocessing includes state-action processing, Q-value updates, and policy optimization using JAX and Gym
jax_dqn_reinforcement_learning.py	Deep Q-Networks for reinforcement learning in JAX	DQN, Reinforcement Learning, JAX	Python	JAX, Gym	General AI	Simulated environments; preprocessing involves state-action processing, Q-value computation, and policy updates using JAX and Gym
jax_gan_image_generation.py	Image generation using GANs in JAX	GANs, Image Generation, JAX	Python	JAX, NumPy	Computer Vision	Image datasets for GAN training; preprocessing includes data augmentation, normalization, and adversarial training using JAX and NumPy
jax_gpt2_text_generation_simulated.py	Text generation using GPT-2 in JAX	GPT-2, Text Generation, JAX	Python	JAX, Transformers	Natural Language Processing	Text datasets for language modeling; preprocessing involves tokenization, sequence generation, and text sampling using GPT-2 and JAX
jax_image_captioning_cnn_rnn.py	Image captioning using CNN and RNN in JAX	Image Captioning, CNN, RNN, JAX	Python	JAX, NumPy	Computer Vision	Image datasets with captions; preprocessing includes feature extraction using CNNs, sequence modeling using RNNs, and caption generation using JAX and NumPy
jax_image_inpainting.py	Image inpainting using JAX	Image Inpainting, JAX, Deep Learning	Python	JAX, NumPy	Computer Vision	Incomplete image data; preprocessing involves mask generation, context encoding, and image reconstruction using JAX and NumPy
jax_kmeans_customer_segmentation.py	Customer segmentation using K-means in JAX	K-means, Customer Segmentation, JAX	Python	JAX, NumPy	Marketing	Customer datasets; preprocessing involves feature extraction, clustering using K-means, and segment analysis using JAX and NumPy
jax_linear_regression.py	Linear regression using JAX	Linear Regression, JAX, Machine Learning	Python	JAX, NumPy	General AI	Various regression datasets; preprocessing includes data normalization, linear model fitting, and prediction using JAX and NumPy
jax_lstm_stock_price_prediction.py	Stock price prediction using LSTM in JAX	LSTM, Stock Price Prediction, JAX	Python	JAX, NumPy	Finance	Stock market data; preprocessing includes time series normalization, feature extraction, and LSTM training using JAX and NumPy
jax_ppo_reinforcement_learning.py	PPO for reinforcement learning in JAX	PPO, Reinforcement Learning, JAX	Python	JAX, Gym	General AI	Simulated environments; preprocessing involves policy gradient computation, advantage estimation, and policy updates using JAX and Gym
jax_predictive_maintenance.py	Predictive maintenance using JAX	Predictive Maintenance, JAX, Machine Learning	Python	JAX, NumPy	Industrial	Maintenance datasets; preprocessing includes feature extraction, anomaly detection, and predictive model training using JAX and NumPy
jax_prototypical_networks.py	Prototypical networks for few-shot learning in JAX	Prototypical Networks, Few-Shot Learning, JAX	Python	JAX, NumPy	General AI	Few-shot learning datasets; preprocessing includes feature extraction, prototype computation, and classification using JAX and NumPy
jax_sentiment_analysis_rnn.py	Sentiment analysis using RNN in JAX	RNN, Sentiment Analysis, JAX	Python	JAX, NumPy	Natural Language Processing	Text datasets for sentiment analysis; preprocessing includes tokenization, sequence modeling using RNNs, and sentiment prediction using JAX and NumPy
jax_siamese_object_tracking.py	Object tracking using Siamese networks in JAX	Siamese Networks, Object Tracking, JAX	Python	JAX, NumPy	Computer Vision	Video datasets for object tracking; preprocessing includes frame extraction, feature matching, and tracking updates using JAX and NumPy
jax_srcnn_image_super_resolution.py	Image super-resolution using SRCNN in JAX	SRCNN, Image Super-Resolution, JAX	Python	JAX, NumPy	Computer Vision	Low-resolution image datasets; preprocessing includes image downscaling, SRCNN training, and super-resolution generation using JAX and NumPy
jax_transfer_learning.py	Transfer learning with JAX	Transfer Learning, JAX, Machine Learning	Python	JAX, NumPy	General AI	Pre-trained model datasets; preprocessing includes feature extraction, model fine-tuning, and transfer learning using JAX and NumPy
jax_transformer_translation.py	Machine translation using transformers in JAX	Transformers, Machine Translation, JAX	Python	JAX, Transformers	Natural Language Processing	Parallel text corpora; preprocessing includes tokenization, embedding generation, and sequence-to-sequence modeling using Transformers and JAX
jax_unet_image_segmentation.py	Image segmentation using U-Net in JAX	U-Net, Image Segmentation, JAX	Python	JAX, NumPy	Computer Vision	Segmentation datasets; preprocessing includes image normalization, U-Net training, and segmentation mask generation using JAX and NumPy
jax_variational_autoencoder.py	Variational autoencoders in JAX	Variational Autoencoders, JAX, Deep Learning	Python	JAX, NumPy	General AI	Various datasets; preprocessing includes data normalization, encoding-decoding, and latent space exploration using variational autoencoders and JAX
python_growing_food_on_mars_pest_control_system.py	Pest control system for growing food on Mars	Pest Control, Mars, Agriculture	Python	TensorFlow	Agriculture	Simulated pest data; preprocessing involves image recognition, pest detection, and control strategies using TensorFlow
python_growing_food_on_mars_pollination_system_in_greenhouse.py	Pollination system in greenhouse for growing food on Mars	Pollination, Mars, Greenhouse	Python	TensorFlow	Agriculture	Simulated pollination data; preprocessing involves pollinator tracking, environmental monitoring, and optimization using TensorFlow
python_growing_food_on_mars_predicting_and_mitigating_dust_storm_effects.py	Predicting and mitigating dust storm effects on Mars	Dust Storms, Mars, Environmental Science	Python	TensorFlow	Environmental Science	Simulated dust storm data; preprocessing involves weather prediction, impact analysis, and mitigation strategies using TensorFlow
python_growing_food_on_mars_simulating_effects_of_martian_gravity.py	Simulating effects of Martian gravity on plant growth	Martian Gravity, Mars, Agriculture	Python	TensorFlow	Agriculture	Simulated gravity data; preprocessing involves plant growth modeling, environmental simulation, and data analysis using TensorFlow
python_growing_food_on_mars_simulating_soil_composition.py	Simulating soil composition on Mars for agriculture	Soil Composition, Mars, Agriculture	Python	TensorFlow	Agriculture	Simulated soil data; preprocessing involves chemical analysis, soil modeling, and optimization using TensorFlow
python_growing_food_on_mars_waste_management_and_composting.py	Waste management and composting for growing food on Mars	Waste Management, Mars, Agriculture	Python	TensorFlow	Agriculture	Simulated waste data; preprocessing involves waste segregation, composting modeling, and efficiency analysis using TensorFlow
python_growing_food_on_mars_water_conservation_and_recycling.py	Water conservation and recycling for growing food on Mars	Water Conservation, Mars, Agriculture	Python	TensorFlow	Agriculture	Simulated water data; preprocessing involves water usage tracking, recycling optimization, and conservation strategies using TensorFlow
pytorch_agricultural_monitoring.py	Agricultural monitoring using PyTorch	Agricultural Monitoring, PyTorch, Machine Learning	Python	PyTorch	Agriculture	Remote sensing data; preprocessing includes image classification, vegetation index calculation, and anomaly detection using PyTorch
pytorch_archaeological_exploration.py	Archaeological exploration using PyTorch	Archaeological Exploration, PyTorch, Machine Learning	Python	PyTorch	Archaeology	Geospatial data; preprocessing involves terrain analysis, site detection, and predictive modeling using PyTorch
pytorch_construction_site_management.py	Construction site management using PyTorch	Construction Management, PyTorch, Machine Learning	Python	PyTorch	Construction	Site monitoring data; preprocessing includes object detection, progress tracking, and safety analysis using PyTorch
pytorch_dietary_recommendation_system_nutrition.py	Dietary recommendation system using PyTorch	Dietary Recommendation, Nutrition, PyTorch	Python	PyTorch	Nutrition	Nutritional datasets; preprocessing involves user profiling, recommendation generation, and feedback analysis using PyTorch
pytorch_disaster_response.py	Disaster response using PyTorch	Disaster Response, PyTorch, Machine Learning	Python	PyTorch	Disaster Management	Emergency response data; preprocessing includes incident detection, resource allocation, and predictive modeling using PyTorch
pytorch_environmental_monitoring.py	Environmental monitoring using PyTorch	Environmental Monitoring, PyTorch, Machine Learning	Python	PyTorch	Environmental Science	Sensor data; preprocessing involves data aggregation, anomaly detection, and trend analysis using PyTorch
pytorch_industrial_inspection.py	Industrial inspection using PyTorch	Industrial Inspection, PyTorch, Machine Learning	Python	PyTorch	Industry	Inspection data; preprocessing includes defect detection, classification, and reporting using PyTorch
pytorch_logistics_delivery.py	Logistics and delivery optimization using PyTorch	Logistics, Delivery, PyTorch, Optimization	Python	PyTorch	Logistics	Delivery data; preprocessing involves route optimization, delivery scheduling, and performance analysis using PyTorch
pytorch_manufacturing_assembly.py	Manufacturing assembly optimization using PyTorch	Manufacturing, Assembly, PyTorch, Optimization	Python	PyTorch	Manufacturing	Assembly line data; preprocessing includes process monitoring, defect detection, and optimization using PyTorch
pytorch_mining_operations.py	Mining operations monitoring using PyTorch	Mining Operations, PyTorch, Machine Learning	Python	PyTorch	Mining	Operational data; preprocessing involves equipment monitoring, anomaly detection, and process optimization using PyTorch
pytorch_precision_agriculture.py	Precision agriculture using PyTorch	Precision Agriculture, PyTorch, Machine Learning	Python	PyTorch	Agriculture	Agricultural data; preprocessing includes crop monitoring, yield prediction, and resource optimization using PyTorch
pytorch_precision_livestock_farming.py	Precision livestock farming using PyTorch	Precision Livestock, PyTorch, Machine Learning	Python	PyTorch	Agriculture	Livestock data; preprocessing includes health monitoring, behavior analysis, and productivity optimization using PyTorch
pytorch_renewable_energy_maintenance.py	Renewable energy maintenance using PyTorch	Renewable Energy, Maintenance, PyTorch	Python	PyTorch	Energy	Maintenance data; preprocessing includes fault detection, performance monitoring, and predictive maintenance using PyTorch
pytorch_search_and_rescue.py	Search and rescue operations using PyTorch	Search and Rescue, PyTorch, Machine Learning	Python	PyTorch	Emergency Services	Search data; preprocessing involves object detection, route planning, and operation coordination using PyTorch
pytorch_space_exploration.py	Space exploration using PyTorch	Space Exploration, PyTorch, Machine Learning	Python	PyTorch	Space Science	Space mission data; preprocessing includes image analysis, trajectory prediction, and resource management using PyTorch
pytorch_surveillance_security.py	Surveillance and security using PyTorch	Surveillance, Security, PyTorch, Machine Learning	Python	PyTorch	Security	Surveillance data; preprocessing includes object detection, behavior analysis, and threat detection using PyTorch
pytorch_traffic_management.py	Traffic management using PyTorch	Traffic Management, PyTorch, Machine Learning	Python	PyTorch	Transportation	Traffic data; preprocessing includes flow analysis, congestion detection, and optimization using PyTorch
pytorch_underwater_exploration.py	Underwater exploration using PyTorch	Underwater Exploration, PyTorch, Machine Learning	Python	PyTorch	Marine Science	Sonar and image data; preprocessing includes object detection, environment mapping, and exploration planning using PyTorch
pytorch_waste_management.py	Waste management using PyTorch	Waste Management, PyTorch, Machine Learning	Python	PyTorch	Environmental Science	Waste data; preprocessing involves classification, recycling optimization, and process monitoring using PyTorch
pytorch_wildfire_monitoring.py	Wildfire monitoring using PyTorch	Wildfire Monitoring, PyTorch, Machine Learning	Python	PyTorch	Environmental Science	Satellite and sensor data; preprocessing includes fire detection, spread prediction, and risk assessment using PyTorch
pytorch_wind_turbine_fault_detection_renewable_energy.py	Wind turbine fault detection using PyTorch	Wind Turbine, Fault Detection, PyTorch	Python	PyTorch	Energy	Wind turbine data; preprocessing includes vibration analysis, fault detection, and maintenance scheduling using PyTorch
random_forest_nutritional_value_estimation_nutrition.py	Estimating nutritional value using random forests	Random Forest, Nutritional Value, Machine Learning	Python	scikit-learn	Nutrition	Nutritional data; preprocessing involves feature extraction, model training, and value estimation using scikit-learn
rl_meal_planning_optimization_nutrition.py	Meal planning optimization using reinforcement learning	Meal Planning, Reinforcement Learning, Nutrition	Python	Stable Baselines3, Gym	Nutrition	Meal data; preprocessing includes nutrient analysis, reward function design, and policy optimization using Stable Baselines3 and Gym
rl_waste_management_optimization_environmental_science.py	Waste management optimization using reinforcement learning	Waste Management, Reinforcement Learning, Environmental Science	Python	Stable Baselines3, Gym	Environmental Science	Waste management data; preprocessing involves process modeling, reward signal design, and policy optimization using Stable Baselines3 and Gym
rust_questions.rs	Basic questions and exercises for learning Rust	Rust, Exercises	Rust	None	Education	Learning exercises for Rust programming
rust_tutorial1.rs	Introductory tutorial for Rust programming	Rust, Tutorial	Rust	None	Education	Basic programming in Rust
scikit_learn_health_insurance_fraud_detection_machine_learning.py	Health insurance fraud detection using scikit-learn	Health Insurance, Fraud Detection, scikit-learn	Python	scikit-learn	Healthcare	Insurance data; preprocessing involves feature extraction, anomaly detection, and fraud classification using scikit-learn
sklearn_nutritional_content_analysis_nutrition.py	Nutritional content analysis using scikit-learn	Nutritional Content, Analysis, scikit-learn	Python	scikit-learn	Nutrition	Nutritional data; preprocessing involves feature extraction, normalization, and content analysis using scikit-learn
sklearn_water_quality_monitoring_environmental_science.py	Water quality monitoring using scikit-learn	Water Quality, Monitoring, scikit-learn	Python	scikit-learn	Environmental Science	Water quality data; preprocessing includes data aggregation, feature extraction, and quality assessment using scikit-learn
solar_energy_prediction_simulated.py	Simulated prediction of solar energy production	Solar Energy, Prediction, Machine Learning	Python	scikit-learn	Energy	Simulated solar energy data; preprocessing involves feature extraction, model training, and energy prediction using scikit-learn
tensorflow_caloric_intake_prediction_nutrition.py	Caloric intake prediction using TensorFlow	Caloric Intake, Prediction, TensorFlow	Python	TensorFlow	Nutrition	Caloric data; preprocessing involves feature extraction, model training, and intake prediction using TensorFlow
tensorflow_smart_irrigation_system_agriculture.py	Smart irrigation system using TensorFlow	Smart Irrigation, Agriculture, TensorFlow	Python	TensorFlow	Agriculture	Irrigation data; preprocessing involves sensor data collection, environmental modeling, and irrigation control using TensorFlow
xgboost_air_quality_forecasting_environmental_science.py	Air quality forecasting using XGBoost	Air Quality, Forecasting, XGBoost	Python	XGBoost	Environmental Science	Air quality data; preprocessing involves feature extraction, model training, and quality prediction using XGBoost
xgboost_food_price_prediction_nutrition.py	Food price prediction using XGBoost	Food Price, Prediction, XGBoost	Python	XGBoost	Nutrition	Food price data; preprocessing involves feature extraction, model training, and price prediction using XGBoost

Community Goals

The Pixel Pioneers Community aims to:

• Keep pace with the rapidly evolving field of AI and machine learning.
• Enhance members' machine learning skills through collaborative learning.
• Foster collective intelligence by sharing knowledge and encouraging questions.
• Promote personal and professional growth in the AI landscape.

Getting Started

To get started with the tutorials and projects in this repository, follow these steps:

1. Clone the repository:

   git clone https://github.com/ShaliniAnandaPhD/PIXEL-PIONEERS-TUTORIALS.git

2. Navigate to the project directory of your choice.
3. Follow the instructions provided in each project's README file.

Contributing

We welcome contributions from the community! If you would like to contribute a tutorial or project, please follow these guidelines:

1. Fork the Repository:

   • Go to the repository on GitHub and click the "Fork" button in the top right corner.

2. Clone Your Fork:

   git clone https://github.com/YOUR-USERNAME/PIXEL-PIONEERS-TUTORIALS.git

3. Create a New Branch:

   cd PIXEL-PIONEERS-TUTORIALS
   git checkout -b your-branch-name

4. Make Your Changes:

   • Add your tutorial or project to the appropriate directory.
   • Ensure your code is well-documented and follows the project's coding standards.
   • Include a README file with clear instructions and any necessary dependencies.

5. Commit Your Changes:

   git add .
   git commit -m "Add description of your changes"

6. Push Your Changes:

   git push origin your-branch-name

7. Create a Pull Request:

   • Go to the original repository on GitHub.
   • Click the "New Pull Request" button.
   • Select your branch and submit a pull request with a detailed description of your changes.

License

This repository is licensed under the MIT License. See the LICENSE file for more details.

Join the Community

Join our vibrant community of machine learning enthusiasts on our Discord server: Pixel Pioneers Community

Let's learn, grow, and innovate together in the exciting world of AI and machine learning!

Community Guidelines

Be Respectful and Inclusive

• Treat everyone with respect: Harassment, discrimination, and exclusionary behavior are not tolerated. Any form of disrespectful behavior will be addressed immediately. Members who engage in harassment or discrimination will be warned or removed from the community, depending on the severity of the offense.
• Be welcoming to new members: Help new members feel included and valued. Offer assistance and encourage them to participate in discussions and projects.

Share Knowledge

• Share your expertise and knowledge: Help others by answering questions and providing feedback. Encourage curiosity and foster a learning environment.
• Be open to learning from others: Collaboration is key to collective growth. Respect different perspectives and be willing to learn from others.

Collaborate and Communicate

• Work together on projects: Share your progress and seek input from the community. Collaboration leads to better solutions and new ideas.
• Communicate clearly and constructively: Use clear, concise language in discussions and code reviews. Provide constructive feedback and be open to receiving it.

Follow the Code of Conduct

• Adhere to the community's code of conduct: Maintain a positive and professional demeanor in all interactions, both online and offline. Respect the rules and guidelines set forth by the community.

Creating a Pull Request

1. Describe Your Changes: Provide a clear and concise description of your changes in the pull request.
2. Reference Relevant Issues: If your pull request addresses an issue, include a reference to the issue.
3. Request Reviews: Ask for reviews from other community members to get feedback on your changes.
4. Make Improvements: Be open to feedback and make necessary improvements based on the reviews.

By following these guidelines, we can create a supportive and productive environment for everyone involved. Let's work together to advance our knowledge and skills in AI and machine learning!