feat: Add platforms.qmd file with a comprehensive list of online AI platforms, dataset providers, model zoos, and related resources

.gitignore

@@ -4,3 +4,4 @@
 .venv/**
 src/_site/
 .venv_win/
+**/*_files/

src/_quarto.yml

@@ -53,6 +53,13 @@ website:
             - href: vision/tasks.qmd
               text: "Tasks"
       - section: "Image Generation Models"
+      
+      - section: "Tools and Frameworks"
+        contents:
+            - href: tools/frameworks.qmd
+              text: "Frameworks"
+            - href: tools/platforms.qmd
+              text: "Platforms"
 
 format:
   html:

src/tools/frameworks.qmd

@@ -4,115 +4,113 @@ These are all libraries and tools I use almost on daily base depending on the pr
 
 # Agent Builders
 
-**CrewAI** (CrewAI): The most advanced opensource Agents builder framework
+* **CrewAI** (CrewAI): The most advanced opensource Agents builder framework
 
-**Autogen** (Microsoft): An agent builder framework with a UI
+* **Autogen** (Microsoft): An agent builder framework with a UI
 
 # Deep Learning
 
-**TensorFlow** (Google): An open-source machine learning framework
+* **TensorFlow** (Google): An open-source machine learning framework
 
-**PyTorch** (Facebook): An open-source machine learning framework
+* **PyTorch** (Facebook): An open-source machine learning framework
 
-**Keras** (Google): A high-level neural networks API
+* **Keras** (Google): A high-level neural networks API
 
-**CNTK** (Microsoft): A deep learning framework
+* **CNTK** (Microsoft): A deep learning framework
 
 # Natural Language Processing (NLP)**
 
-**NLTK** (Stanford University): A comprehensive NLP library
+* **NLTK** (Stanford University): A comprehensive NLP library
 
-**spaCy** (Explosion AI): A modern NLP library
+* **spaCy** (Explosion AI): A modern NLP library
 
-**Stanford CoreNLP** (Stanford University): A Java library for NLP
+* **Stanford CoreNLP** (Stanford University): A Java library for NLP
 
-**Transformers** (Hugging Face): A library for natural language understanding and generation
+* **Transformers** (Hugging Face): A library for natural language understanding and generation
 
 # Computer Vision**
 
-**OpenCV** (OpenCV.org): A computer vision library
+* **OpenCV** (OpenCV.org): A computer vision library
 
-**Pillow** (Python Imaging Library): A Python imaging library
+* **Pillow** (Python Imaging Library): A Python imaging library
 
-**scikit-image** (Scikit-learn): A library for image processing
+* **scikit-image** (Scikit-learn): A library for image processing
 
-**TensorFlow Computer Vision** (Google): A computer vision library
+* **TensorFlow Computer Vision** (Google): A computer vision library
 
-**PyTorch Vision** (Facebook): A computer vision library
+* **PyTorch Vision** (Facebook): A computer vision library
 
-**Keras Applications** (Google): A collection of pre-built computer vision models
+* **Keras Applications** (Google): A collection of pre-built computer vision models
 
 # Reinforcement Learning
 
-**Gym** (OpenAI): A reinforcement learning environment
+* **Gym** (OpenAI): A reinforcement learning environment
 
-**Baselines** (OpenAI): A set of reinforcement learning algorithms
+* **Baselines** (OpenAI): A set of reinforcement learning algorithms
 
-**RLlib** (UBC): A reinforcement learning library
+* **RLlib** (UBC): A reinforcement learning library
 
-**TensorFlow Agents** (Google): A reinforcement learning library
+* **TensorFlow Agents** (Google): A reinforcement learning library
 
-**Ray RLlib** (UC Berkeley): A reinforcement learning library
+* **Ray RLlib** (UC Berkeley): A reinforcement learning library
 
 # Data Science and Analytics
 
-**Pandas** (Wes McKinney): A library for data manipulation and analysis
+* **Pandas** (Wes McKinney): A library for data manipulation and analysis
 
-**NumPy** (Travis Oliphant): A library for numerical computing
+* **NumPy** (Travis Oliphant): A library for numerical computing
 
-**Matplotlib** (John Hunter): A plotting library
+* **Matplotlib** (John Hunter): A plotting library
 
-**Scikit-learn** (David Cournapeau): A machine learning library
+* **Scikit-learn** (David Cournapeau): A machine learning library
 
-**Statsmodels** (Statsmodels.org): A statistical modeling library
+* **Statsmodels** (Statsmodels.org): A statistical modeling library
 
-**Bokeh** (Continuum Analytics): A visualization library
+* **Bokeh** (Continuum Analytics): A visualization library
 
-**Seaborn** (Michael Waskom): A statistical data visualization library
+* **Seaborn** (Michael Waskom): A statistical data visualization library
 
 # Other
 
-**SciPy** (SciPy.org): A scientific computing library
+* **SciPy** (SciPy.org): A scientific computing library
 
-**Matlab** (MathWorks): A high-level technical computing language
+* **Matlab** (MathWorks): A high-level technical computing language
 
-**Julia** (JuliaLang.org): A high-performance language for AI and ML
+* **Julia** (JuliaLang.org): A high-performance language for AI and ML
 
-**R** (R Foundation): A programming language for statistical computing
+* **R** (R Foundation): A programming language for statistical computing
 
 # MLOps
 
-**Tensorboard**
+* **Tensorboard**
 
-**AIM**
+* **AIM**
 
-**LangSmith**
+* **LangSmith**
 
-**AgentOps**
+* **AgentOps**
 
 # Runners
 
-**Ollama**
-**LLama.cpp**
-**Tranformers**
+* **Ollama**
+* **LLama.cpp**
+* **Tranformers**
 
 # Training/ Fine-Tuning
 
-**Unsloth**
+* **Unsloth**
 
-**Keras**
+* **Keras**
 
-**Torch**
+* **Torch**
 
-**OpenAI Gym**
+* **OpenAI Gym**
 
-**Stable-Baselines**
+* **Stable-Baselines**
 
 
 # Platforms - Hosting - Model Zoo
 
-**HuggingFace**
-
-**Kaggle**
-
+* **HuggingFace**
 
+* **Kaggle**

src/tools/platforms.qmd

@@ -0,0 +1,25 @@
+Here is a more comprehensive list of online AI platforms, dataset providers, model zoos, and related resources:
+
+## Online AI Platforms
+- [**Hugging Face**](https://huggingface.co/): A collaborative platform primarily focused on natural language processing (NLP), offering a range of pre-trained language models, tools, and services.
+- [**Kaggle**](https://www.kaggle.com/): A comprehensive platform for data science and artificial intelligence, hosting a wide variety of competitions with lucrative prizes.
+- [**Amazon Lex**](https://aws.amazon.com/lex/): Enables developers to build conversational chatbots quickly without deep learning expertise.
+- [**Metaflow**](https://metaflow.org/): A human-friendly Python library for building and managing real-life data science projects, originally developed at Netflix.
+- [**MLflow**](https://mlflow.org/): An open-source platform to manage the ML lifecycle, including experimentation, reproducibility, and deployment.
+- [**Comet.ml**](https://www.comet.com/site/): Enables data scientists and teams to track, compare, explain, and optimize experiments and models across the model's lifecycle.
+- [**Weights and Biases W&B**](https://wandb.ai/site): W&B is an AI developer platform that helps streamline the machine learning (ML) workflow from end to end.
+- [**Neptune.ai**](https://neptune.ai/): Manages all model building metadata in a single place.
+- [**FloydHub**](https://github.com/floydhub/): A platform for deploying deep learning models, allowing users to focus on the model while they handle deployment.
+- [**Google Cloud AI Platform**](https://cloud.google.com/products/ai/): Provides a suite of tools and services for building, deploying, and managing machine learning models at scale.
+- [**Microsoft Azure Machine Learning**](https://azure.microsoft.com/en-us/products/machine-learning/): A cloud-based platform that provides tools and services for building, deploying, and managing machine learning models.
+- [**Databricks**](https://www.databricks.com/): A unified data analytics platform that supports the entire machine learning lifecycle, from data preparation to model deployment.
+- [**Vertex AI**](https://cloud.google.com/vertex-ai): Google's managed machine learning platform that simplifies the process of building, deploying, and managing ML models.
+- [**Amazon SageMaker**](https://aws.amazon.com/sagemaker/): A fully managed machine learning service that enables developers and data scientists to build, train, and deploy ML models quickly.
+
+## Dataset Providers
+- [**Hugging Face**](https://huggingface.co/datasets): A collection of datasets hosted on the Hugging Face platform, primarily focused on natural language processing.
+- [**Kaggle**](https://www.kaggle.com/datasets): A vast repository of datasets available on the Kaggle platform.
+- [**OpenML**](https://www.openml.org/search?type=data&sort=runs&status=active): An open science platform that provides a large collection of datasets, tasks, and experiments for machine learning.
+- [**Amazon Web Services (AWS) Open Data**](https://aws.amazon.com/opendata/): A collection of publicly available datasets hosted on AWS for use in machine learning and data analysis.
+- [**Microsoft Azure Open Datasets**](https://azure.microsoft.com/en-us/products/open-datasets/): A collection of high-value public datasets hosted on Azure for use in machine learning and data analysis.
+- [**Google Cloud Public Datasets**](https://cloud.google.com/datasets): A collection of publicly available datasets hosted on Google Cloud for use in machine learning and data analysis.