Sophia Sarah AI ML Libraries description .pptx

about libraries used in machine learning

1. AI Ml Libraries BY SOPHIA SARAH 2023UCA1863 Lecture 4

2. Introduction to AI and ML Libraries Libraries in AI and ML are collections of pre-written codes that provide reusable functions and modules for developing intelligent systems. Popular Use Cases: Image recognition, natural language processing, data analysis, and predictive modeling.

3. TensorFlow Developed By: Google Brain Team Features: Open-source platform Supports deep learning and large-scale machine learning Tools for model building, training, and deployment Strengths: Robust for production-grade solutions Offers TensorFlow Lite for mobile and edge device

4. PyTorch Developed By: Facebook AI Research Lab Features: Dynamic computation graph Intuitive and easy to debug Supports distributed training Strengths: Preferred for research and prototyping Extensive community support

5. Scikit-Learn Focus Area: Classical Machine Learning Features: Provides tools for data preprocessing, classification, regression, and clustering Built on NumPy, SciPy, and Matplotlib Strengths: Simple and efficient for small to medium-scale projects Excellent documentation

6. NLTK (Natural Language Toolkit) Focus Area: Natural Language Processing Features: Tools for text preprocessing, tokenization, and sentiment analysis Comprehensive collection of linguistic resources (e.g., corpora) Strengths: Well-suited for academic research and teaching Easy-to-use interface for NLP tasks

7. Keras Focus Area: Deep Learning Features: High-level API built on TensorFlow Facilitates fast experimentation Modular and user-friendly Strengths: Ideal for beginners in AI/ML Offers pre-trained models

8. OpenCV Focus Area: Computer Vision Features: Provides algorithms for image processing, video analysis, and computer vision tasks Supports multiple programming languages (C++, Python, Java, etc.) Strengths: Highly optimized for real-time applications Extensive library of vision algorithms

9. Other Notable Libraries XGBoost : Focus: Gradient boosting framework for structured data. Use Cases: Ranking, classification, and regression tasks. Pandas: Focus: Data manipulation and analysis. Use Cases: Handling large datasets, data cleaning. Matplotlib & Seaborn: Focus: Visualization tools. Use Cases: Data visualization and exploratory data analysis.

10. Other Notable Libraries Fast.ai: Focus: High-level library for deep learning built on PyTorch . Use Cases: Quick model prototyping and experimentation. Hugging Face Transformers: Focus: State-of-the-art NLP models. Use Cases: Machine translation, text classification, and question answering.

11. THANK YOU