Machine Learning and AI Concepts

Explainable Artificial Intelligence (XAI)

• LIME (Local Interpretable Model-agnostic Explanations): Explains individual predictions by altering input data and observing output changes, creating a local surrogate model.
• SHAP (SHapley Additive exPlanations): Assigns feature importance values using cooperative game theory, providing consistent and interpretable explanations across models.
• GRAD-CAM (Gradient-weighted Class Activation Mapping): Highlights regions in input data (like images) that influence neural network predictions using gradient information.

Internet of Things (IoT)

• Definition: A network of interconnected devices with sensors, software, and connectivity, enabling data exchange and automation.
• Applications: Smart homes, industrial automation, healthcare monitoring, smart cities.
• Challenges: Data security, scalability, and interoperability.

Ensemble Learning

• Concept: Combines multiple models to improve prediction accuracy and robustness.
• Bagging: Reduces variance by training models on different data subsets (e.g., Random Forests).
• Boosting: Reduces bias by sequentially training models to correct previous errors (e.g., AdaBoost, Gradient Boosting).
• Majority Voting: Aggregates predictions from multiple models, selecting the most frequent output.

Kaggle/Colab/HuggingFace

• Kaggle: Platform for data science competitions, datasets, and collaborative projects.
• Colab: Cloud-based Jupyter notebook environment for Python with GPU/TPU support for machine learning tasks.
• HuggingFace: Library and platform for state-of-the-art pre-trained models (like transformers) in NLP and other areas.

Project Management/Scrum

• PMI/PMP: Project Management Institute (PMI) offers PMP (Project Management Professional) certification, a recognized project management credential.
• Scrum Roles: Product Owner (defines vision), Scrum Master (facilitates process), Development Team (implements solutions).
• Scrum Practices: Sprint planning, daily stand-ups, sprint reviews, and retrospectives.

Python Packages

• NumPy: Numerical computing.
• Pandas: Data manipulation.
• Matplotlib/Seaborn: Data visualization.
• Scikit-learn: Machine learning tools.
• TensorFlow/PyTorch: Deep learning frameworks.

Artificial General Intelligence (AGI)

• Definition: Theoretical AI capable of any intellectual task a human can do, demonstrating general reasoning and adaptability.
• Challenges: Ethical implications, safety, and technical feasibility.

Large Language Models (LLMs)

• LLaMa (Large Language Model Meta AI): Advanced transformer-based models by Meta, optimized for efficiency and fine-tuning.

GPU/Hardware Accelerators

• H100/A100: High-performance NVIDIA GPUs for deep learning and high-performance computing.
• L40: NVIDIA data center GPUs for AI visualization.
• RTX 4090: High-performance consumer GPU for AI and gaming.

Artificial Neural Networks (ANNs)

• Concept: Computational models inspired by biological neural networks, comprised of interconnected layers of nodes (neurons) processing data.
• Applications: Image recognition, natural language processing (NLP), recommendation systems.

Convolution

• Definition: A mathematical operation in Convolutional Neural Networks (CNNs) for extracting spatial features from data, particularly in image processing.

Activation Functions

• ReLU (Rectified Linear Unit): Efficient activation function reducing vanishing gradients.
• Tanh: Scales outputs between -1 and 1.
• Sigmoid: Outputs values between 0 and 1, suitable for binary classification.

Deep Learning Frameworks

• TensorFlow: Open-source library for large-scale machine learning, offering flexibility and production deployment.
• PyTorch: Dynamic and developer-friendly framework for deep learning research and applications.
• Keras: High-level API for building and training deep learning models, compatible with TensorFlow.

ML Libraries

• Scikit-learn: Comprehensive library for traditional machine learning, with tools for classification, regression, clustering, and preprocessing.

Software Licenses

• BSD License: Permissive, allows proprietary use with minimal restrictions.
• Apache License: Permits modifications and distribution, requiring attribution and disclosure of changes.
• MIT License: Simple and permissive, allowing reuse with attribution.
• GPL (General Public License): Requires derivative works to be open-source and licensed under GPL.

This quiz covers various essential concepts in Machine Learning and Artificial Intelligence, including Explainable AI with techniques like LIME, SHAP, and GRAD-CAM. Additionally, it explores the Internet of Things and Ensemble Learning, focusing on definitions, applications, and challenges. Test your understanding of modern AI methodologies and their implementations.