AI ML Learning

This repo contains notebooks for a variety of AI & ML topics and models.

Local setup

1. Setup virual environment (only needs to be done once)

   python3 -m venv venv

2. Switch to the virtual environment

   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Install Jupyter extension in VSCode https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter

After completing above steps, any of the notebooks can be run like usual

Azure AI learning

This folder contains notebooks which I used for testing during my learning for AI-900 and DP-900 courses from Microsoft

Repository Map

• bengali-speech-recognition.ipynb: Notebook for Bengali speech recognition experiments.
• computer-vision-learning.ipynb: Notebook for learning computer vision concepts.
• convolution_test.ipynb: Notebook for testing convolution operations.
• cv-test.ipynb: Notebook for computer vision tests.
• detect-sleep-states.ipynb: Notebook for detecting sleep states.
• ewma_learning.ipynb: Notebook for learning Exponentially Weighted Moving Average (EWMA).
• house-rent-prediction.ipynb: Notebook for predicting house rent prices.
• langchain-test.ipynb: Notebook for testing LangChain functionalities.
• Learning_stable_diffusion.ipynb: Notebook for learning stable diffusion models.
• mnist-classifier.ipynb: Notebook for MNIST digit classification.
• neuralnet-test.ipynb: Notebook for testing neural networks.
• nlp_learning.ipynb: Notebook for learning Natural Language Processing (NLP).
• opencv_line_detection.py: Script for line detection using OpenCV.
• pose-detection-realtime.py: Script for real-time pose detection.
• pytorch_learning.ipynb: Notebook for learning PyTorch.
• random-forest.ipynb: Notebook for random forest algorithm experiments.
• regularized-regression-linear.py: Script for regularized linear regression.
• semantic-segmentation.py: Script for semantic segmentation tasks.
• svm-test.ipynb: Notebook for testing Support Vector Machines (SVM).
• azure-ai-learning/: Contains scripts and notebooks related to Azure AI learning, including text-to-speech and machine learning tasks.
• computer-vision-opencv/: Contains resources for computer vision using OpenCV, including images and convolution operations.
• datascience/: Contains data science-related resources and experiments, with subdirectories for economics, price prediction, and quantitative genetics.
• deep-learning/: Contains deep learning resources and experiments, including neural networks and ResNet models.
• langchain_learning/: Contains resources for learning LangChain, with a focus on basic chain operations.
• language-models/: Contains resources related to language models, including text summarization and RNN models.
• llama3.2-fine-tuning/: Contains resources for fine-tuning LLaMA 3.2 models.
• multi-agent-llm/: Contains resources for multi-agent large language models, with notebooks for agentic systems.
• object-detection/: Contains resources for object detection tasks, including YOLOv8 inference.
• realtime-cv-web/: Contains resources for real-time computer vision on the web, with HTML, JavaScript, and Python components.
• sample_datasets/: Contains sample datasets for experiments, including bank marketing and temperature data.
• text-to-speech/: Contains resources for text-to-speech tasks, including basic conversion and OpenAI audio models.
• time-series-forecasting/: Contains resources for time series forecasting, with a focus on sales data.