TensorFlow is a powerful open source library for deep learning, developed by the Google Brain team. It allows users to build and train complex neural network models for a variety of tasks, including image recognition, natural language processing, and time series forecasting. TensorFlow is highly scalable and can be used for both research and production environments.
Keras is a high-level API for building and training deep learning models. It is built on top of TensorFlow and is designed to be easy to use and intuitive for developers who are new to deep learning. Keras allows users to quickly prototype and experiment with different architectures and hyperparameters, making it a popular choice for researchers and data scientists.
PyTorch is another popular open source library for deep learning. It is developed by Facebook AI Research and is designed to be flexible and easy to use. PyTorch allows users to build complex neural network models and perform computations on tensors, a data structure similar to matrices. PyTorch is known for its support for dynamic computational graphs, which allow users to build models on the fly and modify them during training.
scikit-learn is a machine learning library for Python that is widely used in the data science community. It offers a wide range of algorithms for classification, regression, clustering, and dimensionality reduction, along with tools for model evaluation and selection. scikit-learn is designed to be easy to use and can be integrated with other libraries, such as NumPy and Pandas, to create powerful data analysis pipelines.
All of the above are written in python. Pytorch is also ported for JAVA and C++. scikit-learn is also written in Python, but it is focused on traditional machine learning algorithms rather than deep learning.
A difference is the level of abstraction provided by the libraries. TensorFlow and PyTorch offer low-level APIs that allow users to build and customize their own neural network architectures, while Keras provides a higher-level API that allows users to quickly build and train pre-defined architectures. scikit-learn offers a more general-purpose API for traditional machine learning algorithms.
In terms of performance, TensorFlow, Keras, and PyTorch are all optimized for training deep learning models on large datasets and can be used to build models that can run on GPUs and TPUs. scikit-learn is optimized for smaller datasets and can run on CPUs, but it may not be as efficient for larger datasets.
Hope this overview helps to find the model builder you want to go after 🙂