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While not strictly a machine learning library, it’s well-suited for data analysis and manipulation for large data sets. Pandas is a powerful and flexible data analysis library written in Python. It’s made this list of top ten data science packages for Python because if you familiarize yourself with it, you’ll get a sense of how its innovations later evolved into the features you now see in competing libraries. While Theano announced that it would stop major developments after the release of v1.0 in 2017, you can still study it for historical reasons.
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Theano is one of the earliest open-source software libraries for deep-learning development. The most recent stable version is v1.13.1, but the new v2.0 is in beta now. If you need to process large data sets quickly, this is a library you shouldn’t ignore. It uses dataflow graphs and differentiable programming across a range of tasks, making it one of the most highly flexible and powerful machine learning libraries ever created. Originally developed by Google Brain, TensorFlow is open sourced. It specializes in numerical computation using dataflow graphs. TensorFlow is one of the most famous machine learning libraries for some very good reasons.
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Library version 7 or higher and the latest driver version are recommended, but releases in the 6s are fine too.I recommend using the Docker version if you can so it works right out of the box. I should highlight that Caffe assumes you have at least a mid-level knowledge of machine learning, although the learning curve is still relatively gentle.Īs with PyTorch, requirements depend on your operating system. They claim it can process “ over 60M images per day with a single NVIDIA K40 GPU. According to the website, Caffe’s image processing is quite astounding. It’s released under the BSD 2-Clause license, and it’s touted as one of the fastest-performing deep-learning frameworks out there. Yangqing Jia started Caffe while working on his PhD at UC Berkeley.
It usually requires the following:Ĭaffe is one of the fastest implementations of a convolutional network, making it ideal for image recognition. I recommend using the “Get Started” page for guidance. The installation is slightly more complicated than, say, Scikit-Learn. Requirements for PyTorch depend on your operating system. Or do you want to use deep learning but you’re just a beginner? Use PyTorch because Scikit-Learn doesn’t cater to deep learning. Want to use RNN for language processing? Use PyTorch because of its define-by-run feature. For instance, do you want to compute tensors faster by using a GPU, as I mentioned above? Use PyTorch because you can’t do that with NumPy. If you’re an academic or an engineer who wants an easy-to-learn package to perform these two things, PyTorch is for you. Second, it builds dynamic neural networks on a tape-based autograd system, thus allowing reuse and greater performance. First, it accelerates tensor computation using strong GPU. If you’re a beginner and want to pick up a machine learning library, Scikit-Learn is the one to start with.
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For example, a range of tutorials on the Scikit-Learn website show you how to analyze real-world data sets. Morgan and Spotify use it in their data science work.īecause Scikit-Learn has such a gentle learning curve, even the people on the business side of an organization can use it. Since then, it’s grown to over 20,000 commits and more than 90 releases.
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David Cournapeau started it as a Google Summer of Code project. Scikit-Learn is a Python module for machine learning built on top of SciPy and NumPy. Since it’s the language of choice for machine learning, here’s a Python-centric roundup of ten essential data science packages, including the most popular machine learning packages. And while there are many programming languages suited for data science and machine learning, Python is the most popular. Interest in data science has risen remarkably in the last five years.
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