TensorFlow
Focus: TensorFlow is a comprehensive open-source platform for machine learning. It provides a flexible and comprehensive ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications.
Integration: TensorFlow can implement a wide range of machine learning algorithms, including those available in Sci-kit Learn, making it versatile for various applications.
Modularity: Its modular architecture allows users to deploy computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.
Parallelization: TensorFlow is optimized for high-performance numerical computation, making it suitable for large-scale machine learning tasks that require parallel processing.
Use Cases: TensorFlow is widely used in both academic research and industry for tasks such as image and speech recognition, natural language processing, and more.
Sci-kit Learn
Focus: Sci-kit Learn is a simple and efficient tool for data mining and data analysis, built on NumPy, SciPy, and matplotlib. It is primarily used for traditional machine learning techniques such as classification, regression, clustering, and dimensionality reduction.
Limitations: While excellent for classical machine learning tasks, Sci-kit Learn is not designed for deep learning or neural network architectures, which require more specialized frameworks like TensorFlow or PyTorch.
Keras
API Level: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It allows for easy and fast prototyping through user-friendly, modular, and extensible code.
Integration: Keras is tightly integrated with TensorFlow 2.0, providing a simplified interface for building and training deep learning models.
Purpose: Designed to enable fast experimentation, Keras is ideal for beginners and researchers who need to quickly prototype and test new ideas.
Performance: While Keras simplifies model building, it may not be as performant as lower-level frameworks like TensorFlow when it comes to fine-tuning and optimizing models for production.