Scikit-learn vs. TensorFlow vs. PyTorch vs. Keras
Machine learning libraries exist for many applications - AI-powered tools, predicting, computer vision, and classifying, to name a few. If you're looking to use these libraries to create applications or solve problems, you'll want to choose the right tool for the job. Let's take a look at the differences between some of the more frequently mentioned libraries to help you decide.
Scikit-learn vs. TensorFlow
Scikit-learn is a widely used open source machine learning library for Python. It's built on top of and integrates with commonly used libraries such as NumPy, SciPy, Matplotlib and pandas, making it accessible and versatile.
Consider scikit-learn if you're new to machine learning or developing something using non-neural network algorithms.
Consider TensorFlow if you want to use a deep learning approach in conjunction with hardware acceleration through GPUs and TPUs, or on a cluster of computers (which scikit-learn doesn't natively support).
PyTorch vs. scikit-learn
PyTorch is a deep learning software library for Python, C++ and Julia. PyTorch is primarily used for end-to-end building and training of deep neural networks with the ability to create custom models and learning algorithms.
Scikit-learn is a library for traditional machine learning algorithms used for clustering, classification, regression, etc. Scikit-learn's black-box nature makes it more accessible to those who are relatively new to machine learning.
Consider PyTorch if you're developing applications that have computationally expensive tasks, such as natural language processing, computer vision, etc. With Pytorch, you're also able to use GPU acceleration to your advantage.
Consider scikit-learn if you're developing a small, exploratory project that doesn't require a substantial amount of data. With scikit-learn your focus would not be on customization, but on the speed and user-friendliness of the machine learning algorithms.
Keras vs. scikit-learn
Keras is a high-level deep learning framework that abstracts away many of the low-level details and computations by handing them off to TensorFlow. This allows Keras to have reduced code complexity despite being a deep learning framework.
Scikit-learn has an even greater level of abstraction for common machine learning algortihms. Scikit-learn doesn't have native support for GPU computing and deep learning.
Consider Keras if your application/model has to use neural networks to learn from large amounts of data. Keras also caters for those who are fairly new to deep learning.
Consider scikit-learn if you require a model for statistical purposes, predictions, classification, or clustering. Scikit-learn works well with relatively small datasets that require general machine learning computations.
PyTorch vs. TensorFlow
PyTorch is a deep learning framework with a pythonic and object oriented approach. PyTorch has more debugging and testing options than TensorFlow.
TensorFlow is a low-level deep learning library that provides workflows to high-level APIs such as Keras - albeit with less computational power. TensorFlow is currently more widely used than PyTorch.
Consider PyTorch for its many debugging capabilities, if Python is central to your development.
Keras vs. TensorFlow
TensorFlow runs as a back-end to Keras, but also as its own standalone library for low-level computations.
Consider Keras if you're relatively new to deep learning or looking to use a high level API that makes the most of the TensorFlow framework. As with any library, Keras takes into account best practices with comparatively less complexity.
Consider TensorFlow if you require greater funtionality and performance on large datasets. TensorFlow requires you to be slightly adept with deep learning to capitalize on what it has to offer.
Keras vs. PyTorch
Keras is a deep learning software library that functions as a high-level interface for TensorFlow, resulting in comparatively slower computations with less complexity.
PyTorch is a low-level computation framework with superior debugging, pliability and performance capabilities.
Consider Keras if want to use deep learning algorithms and models with relatively small datasets. Keras is more readable and approachable for beginners.
Consider PyTorch if you have deep learning experience or want to create a custom model. PyTorch is not natively capable of running models on a browser, but you can convert models to TensorFlow.js to get around this.