Core Machine Learning & Deep Learning Libraries

These libraries form the foundation of AI development, supporting model creation, training, optimization, and deployment. They are essential for custom model design, including deep neural networks, gradient descent pipelines, and tensor manipulation.

Tools:

• TensorFlow – An open-source ML library from Google with wide adoption in research and production. Supports model building, distributed training, and deployment via TensorFlow Lite and TensorFlow.js.

• PyTorch – A dynamic deep learning framework from Meta, preferred for its flexibility and Pythonic interface. Ideal for research, NLP, and computer vision.

• scikit-learn – A go-to library for classical ML models like decision trees, SVMs, and clustering. It excels in preprocessing, feature selection, and model evaluation.

• NumPy – Provides array operations and numerical computing for all scientific Python libraries. Forms the core of vectorized data processing.

• SciPy – Built on NumPy for advanced linear algebra, optimization, and statistical functions. Frequently used in engineering and scientific ML models.