Resources - Международная научно-техническая конференция «Практическое применение технических и цифровых технологий...

Resources
1.
Alpaydin, E. (2010). Introduction to machine learning (2nd ed.).
Cambridge, MA: MIT Press.
2.
Hastie, T., Tibshirani, R., & Friedman, J. (2017). The elements of
statistical learning: Data mining, inference, and prediction (2nd ed.).
New York, NY: Springer.
3.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning.
Cambridge, MA: MIT Press.
4.
Murphy, K. P. (2012). Machine learning: A probabilistic
perspective. Cambridge, MA: MIT Press.
5.
Bishop, C. M. (2006). Pattern recognition and machine learning.
New York, NY: Springer.
6.
Chollet, F. (2018). Deep learning with Python. Shelter Island, NY:
Manning Publications.
7.
Brownlee, J. (2021). Machine learning mastery.
ANALYSIS OF PYTHON MACHINE LEARNING LIBRARIES
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The use of machine learning has become increasingly popular in recent
years, and one of the most popular programming languages for machine learning
is Python. With a wide range of libraries available for machine learning in
Python, it can be challenging to determine which one to use for a specific task.
In this article, we will analyze some of the most commonly used libraries for
machine learning in Python.
Scikit-learn
. Scikit-learn is a widely used machine learning library in
Python that provides a range of tools for classification, regression, clustering,
and more. It's known for its ease of use and well-documented API, making it a
popular choice for both beginners and experts. Scikit-learn is built on top of
NumPy and SciPy, and it integrates well with other Python libraries such as
Искусственный интеллект, методы и технологии информационной безопасности
Международная научно-техническая конференция «Практическое применение технических и
цифровых технологий и их инновационных решений», ТАТУФФ, Фергана, 4 мая 2023 г.
572
pandas and matplotlib. Scikit-learn is often used for tasks such as data
preprocessing, feature extraction, and model selection.
TensorFlow
. Developed by Google, TensorFlow is a popular machine
learning library that's used for both research and production applications. It's
known for its ability to handle large-scale machine learning workloads and for
its support of deep learning models. TensorFlow provides a high-level API
called Keras, which simplifies the process of building deep learning models.
TensorFlow also supports distributed computing, which makes it a popular
choice for large-scale machine learning applications.
PyTorch
. Developed by Facebook, PyTorch is another popular machine
learning library that's often used for research and development. It's known for its
ease of use and for its dynamic computational graph, which allows for more
flexibility in model development. PyTorch is built on top of NumPy and
integrates well with other Python libraries such as pandas and matplotlib.
PyTorch is often used for tasks such as computer vision, natural language
processing, and speech recognition.
Keras
. Built on top of TensorFlow, Keras is a high-level neural networks
API that simplifies the process of building deep learning models. It's known for
its simplicity and ease of use, making it a popular choice for beginners. Keras
provides a range of predefined neural network layers, such as convolutional and
recurrent layers, which can be easily combined to build complex models. Keras
also supports distributed computing and can run on both CPU and GPU.
Theano
. Theano is a popular machine learning library that's known for its
speed and efficiency. It provides tools for building and training deep learning
models and is often used in research settings. Theano is built on top of NumPy
and integrates well with other Python libraries such as pandas and matplotlib.
Theano provides an efficient computation engine that can run on both CPU and
GPU.
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Международная научно-техническая конференция «Практическое применение технических и
цифровых технологий и их инновационных решений», ТАТУФФ, Фергана, 4 мая 2023 г.
573
MXNet
. Developed by Apache, MXNet is a popular machine learning
library that's known for its scalability and efficiency. It supports multiple
programming languages, making it a popular choice for developers who need to
integrate machine learning into their existing workflows. MXNet provides tools
for building and training deep learning models, and it supports distributed
computing for large-scale machine learning applications. MXNet also provides
an efficient computation engine that can run on both CPU and GPU.
In conclusion, Python provides a wide range of libraries for machine
learning, each with its own strengths and weaknesses. Scikit-learn is often
considered to be a good starting point for beginners due to its ease of use and
comprehensive documentation, while TensorFlow and PyTorch are popular
choices for those working with deep learning models. Keras provides a simple
and easy-to-use API for building deep learning models, while Theano and
MXNet are popular choices for their speed and scalability. Ultimately, the
choice of which library to use will depend on the specific needs of the project at
hand.
Resources
1.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B.,
Grisel, O., ... & Vanderplas, J. (2011). Scikit-learn: Machine
learning in Python. Journal of Machine Learning Research, 12(Oct),
2825-2830.
2.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... &
Kudlur, M. (2016). TensorFlow: A system for large-scale machine
learning. In 12th USENIX Symposium on Operating Systems
Design and Implementation (OSDI 16) (pp. 265-283).
3.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito,
Z., ... & Lerer, A. (2017). Automatic differentiation in PyTorch. In
NIPS-W (pp. 1-4).
4.
Theano Development Team. (2016). Theano: A Python framework
for fast computation of mathematical expressions. arXiv preprint
arXiv:1605.02688.
5.
McKinney, W. (2010). Data structures for statistical computing in
Python. In Proceedings of the 9th Python in Science Conference
(Vol. 445, pp. 51-56).
6.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R.,
Virtanen, P., Cournapeau, D., ... & Oliphant, T. E. (2020). Array
programming with NumPy. Nature, 585(7825), 357-362.
Искусственный интеллект, методы и технологии информационной безопасности
Международная научно-техническая конференция «Практическое применение технических и
цифровых технологий и их инновационных решений», ТАТУФФ, Фергана, 4 мая 2023 г.
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