Open ajay-dhangar opened 16 hours ago
docs/ ├── README.md # Overview of the documentation structure ├── getting-started/ │ ├── introduction.md # Introduction to AIBuddies and AI │ ├── prerequisites.md # Required skills/knowledge for AI learning │ ├── setting-up-environment.md # Guide to setting up the development environment │ └── tools-and-libraries.md # Overview of important tools (TensorFlow, PyTorch, etc.) ├── ai-fundamentals/ │ ├── what-is-ai.md # Basics of AI │ ├── history-of-ai.md # Evolution of AI over time │ ├── types-of-ai/ │ │ ├── narrow-ai.md # Narrow AI explained │ │ ├── general-ai.md # General AI concepts │ │ └── super-ai.md # Future of AI and Super AI │ ├── machine-learning-vs-deep-learning.md # Comparison between ML and DL │ └── key-concepts.md # Important AI concepts like algorithms, data, models, etc. ├── machine-learning/ │ ├── introduction.md # Overview of machine learning │ ├── supervised-learning/ │ │ ├── introduction.md # What is supervised learning? │ │ ├── regression.md # Regression techniques and algorithms │ │ └── classification.md # Classification techniques and algorithms │ ├── unsupervised-learning/ │ │ ├── introduction.md # What is unsupervised learning? │ │ ├── clustering.md # Clustering techniques and algorithms │ │ └── dimensionality-reduction.md # Techniques for dimensionality reduction │ ├── reinforcement-learning/ │ │ ├── introduction.md # Introduction to reinforcement learning │ │ ├── q-learning.md # Basic Q-learning techniques │ │ └── deep-q-networks.md # Advanced techniques in reinforcement learning │ └── algorithms-and-techniques.md # Key ML algorithms and techniques ├── deep-learning/ │ ├── introduction.md # Overview of deep learning │ ├── neural-networks/ │ │ ├── introduction.md # Basics of neural networks │ │ ├── feedforward-networks.md # Feedforward neural networks explained │ │ └── backpropagation.md # How backpropagation works │ ├── convolutional-neural-networks.md # CNNs and their applications │ ├── recurrent-neural-networks.md # RNNs and sequence-based learning │ ├── transformers.md # Modern architectures for deep learning │ └── optimization-techniques.md # Training and optimization methods ├── natural-language-processing/ │ ├── introduction.md # Overview of NLP │ ├── text-preprocessing.md # Text preprocessing techniques │ ├── sentiment-analysis.md # Sentiment analysis explained │ ├── language-models/ │ │ ├── introduction.md # Overview of language models │ │ ├── transformers.md # Transformer models in NLP │ │ └── word-embeddings.md # Techniques for word embeddings │ └── applications.md # Real-world applications of NLP ├── ai-ethics-and-safety/ │ ├── introduction.md # Overview of AI ethics │ ├── responsible-ai.md # Developing AI responsibly │ ├── bias-and-fairness.md # Addressing bias and ensuring fairness │ ├── privacy.md # Protecting user privacy in AI │ └── ai-safety.md # Best practices for AI safety ├── tools-and-frameworks/ │ ├── introduction.md # Overview of popular AI tools │ ├── tensorflow.md # TensorFlow library guide │ ├── pytorch.md # PyTorch library guide │ ├── scikit-learn.md # Scikit-learn for machine learning │ └── jupyter-notebooks.md # Using Jupyter for data science ├── projects/ │ ├── index.md # Overview of AI project ideas │ ├── beginner-projects.md # AI projects for beginners │ ├── intermediate-projects.md # AI projects for intermediate learners │ ├── advanced-projects.md # Challenging AI projects for experts │ └── real-world-case-studies.md # Real-world AI use cases and projects └── style-guide.md # Documentation style guide for contributors
getting-started/
ai-fundamentals/
machine-learning/
deep-learning/
natural-language-processing/
ai-ethics-and-safety/
tools-and-frameworks/
projects/
style-guide.md
@ajay-dhangar I would like to take the initiative to work on it. Assign me the task.
so i only need to make it structured right? as show by you.
Explanation of the Folder Structure
getting-started/
: Provides a smooth entry point for newcomers to learn about the project, prerequisites, and tools setup.ai-fundamentals/
: Covers fundamental AI concepts to build a strong base, starting from the basics to more nuanced topics.machine-learning/
anddeep-learning/
: These folders separately handle ML and DL topics, giving each the depth it requires.natural-language-processing/
: Focuses on NLP, covering essential techniques and real-world applications.ai-ethics-and-safety/
: A dedicated section on ethical considerations and safety practices in AI development.tools-and-frameworks/
: Helps users get familiar with essential tools and libraries used in AI development.projects/
: Provides project ideas for practical learning, categorized by skill level.style-guide.md
: Ensures consistent formatting and content style across the documentation.