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Keras vs scikit-learn: What are the differences?

Key Differences between Keras and scikit-learn

Keras and scikit-learn are both popular libraries used for machine learning tasks. However, there are several key differences between them:

  1. Neural Networks vs Traditional Machine Learning Algorithms: Keras focuses on deep learning and neural networks, while scikit-learn is a general-purpose machine learning library that covers various traditional algorithms such as linear regression, decision trees, and support vector machines. Keras provides a higher-level interface for building and training neural networks, making it easier to work with complex architectures.

  2. Backend Support: Keras is a high-level neural networks library that allows users to choose their preferred backend, such as TensorFlow, Theano, or CNTK, providing flexibility and compatibility with different hardware and software setups. On the other hand, scikit-learn is built on top of NumPy, SciPy, and matplotlib and does not support different backends.

  3. Level of Abstraction: Keras offers a higher level of abstraction, allowing users to define and train neural networks with less code and complexity. It provides a user-friendly API that simplifies the process of building and experimenting with deep learning models. On the other hand, scikit-learn provides a lower-level interface that requires more manual intervention, giving users finer control over the machine learning algorithms and their parameters.

  4. Domain-specific Features: Keras is primarily designed for deep learning tasks, providing extensive support for convolutional neural networks, recurrent neural networks, and other popular architectures. It includes various prebuilt layers, activation functions, and optimization algorithms specifically tailored for deep learning workflows. In contrast, scikit-learn covers a wide range of machine learning tasks, including classification, regression, clustering, and dimensionality reduction. It offers a comprehensive set of tools for feature extraction, model evaluation, and cross-validation.

  5. Community and Ecosystem: Keras and scikit-learn have different communities and ecosystems. Keras has a strong community focused on deep learning research and applications, which has contributed to the development of various pre-trained models, tutorials, and resources. It is often used in academic and research settings. Scikit-learn, on the other hand, has a larger and more established community that covers a broader spectrum of machine learning algorithms and applications. It is widely used in industry and has extensive documentation and support.

In Summary, Keras is a high-level neural networks library focusing on deep learning tasks, providing backend flexibility, higher level of abstraction, and domain-specific features. On the other hand, scikit-learn is a general-purpose machine learning library covering various traditional algorithms, offering a lower-level interface, and having a larger, diverse community and ecosystem.

Decisions about Keras and scikit-learn
Fabian Ulmer
Software Developer at Hestia · | 3 upvotes · 52.9K views

For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.

We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.

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A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

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Pros of Keras
Pros of scikit-learn
  • 8
    Quality Documentation
  • 7
    Supports Tensorflow and Theano backends
  • 7
    Easy and fast NN prototyping
  • 26
    Scientific computing
  • 19
    Easy

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Cons of Keras
Cons of scikit-learn
  • 4
    Hard to debug
  • 2
    Limited

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What is Keras?

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

What is scikit-learn?

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

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What are some alternatives to Keras and scikit-learn?
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
MXNet
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
Postman
It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide.
Postman
It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide.
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