OtherLibraries.ipynb

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    "# Machine Learning with scikit-learn"
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    "## Machine Learning Libraries\n",
    "\n",
    "There are many software libraries available for machine learning.  Some of them are listed below."
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    "### For General Machine Learning\n",
    "\n",
    "* **[scikit-learn](http://scikit-learn.org/)**: Free Software (BSD License). The topic of this course\n",
    "* **[Spark MLLib](https://spark.apache.org/mllib/)**: Free Software (Apache License 2.0). Spark based machine learning with interfaces to Java, Scala, Python, and R. MLlib fits into Spark's APIs and interoperates with NumPy in Python and R libraries. You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it easy to plug into Hadoop workflows.\n",
    "* **[mlpack](https://www.mlpack.org/)**: Free Software (3-clause\n",
    "BSD license;  Mozilla Public License v2.0; Boost Software License, version 1.0). A fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. mlpack provides these algorithms as simple command-line programs, Python bindings, and C++ classes which can then be integrated into larger-scale machine learning solutions.\n",
    "* **[Accord.NET Framework](http://accord-framework.net/)**: Free Software (LGPLv2.1) The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. \n",
    "* **[WEKA](https://www.cs.waikato.ac.nz/ml/weka/)**: Free Software (GPL). Data Mining Software in Java. Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. \n",
    "* **[Shogun](http://shogun-toolbox.org/)**: Free Software (GPLv3). Shogun is among the oldest of machine learning libraries, but continues to be well maintained and optimized. Shogun was created in 1999 and written in C++. Via SWIG, Shogun can be used in Java, Python, C#, Ruby, R, Lua, Octave, and Matlab. Shogun is designed for unified large-scale learning for a broad range of feature types and learning settings, like classification, regression, or explorative data analysis.\n",
    "* **[Torch](http://torch.ch/)** and **[PyTorch](https://pytorch.org/)**: Free Software (custom BSD-ish license). Torch is a scientific computing framework with wide support for machine learning algorithms, emphasizing GPU computation. Torch is based on the scripting language Lua, PyTorch is Python bindings to the underlying engine and C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community."
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    "### For Deep Learning\n",
    "\n",
    "* **[TensorFlow](https://www.tensorflow.org/)**: Free Software (Apache 2.0 open source license). TensorFlow is an open source software library for numerical computation using data flow graphs. TensorFlow implements what are called data flow graphs, where batches of data (\"tensors\") can be processed by a series of algorithms described by a graph. The movements of the data through the system are called \"flows\". Graphs can be assembled with C++ or Python and can be processed on CPUs or GPUs.\n",
    "* **[Theano](http://deeplearning.net/software/theano/)**: Free Software (BSD License). Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data. It was written at the LISA lab to support rapid development of efficient machine learning algorithms. Theano is named after the Greek mathematician, who may have been Pythagoras’ wife. \n",
    "* **[Keras](https://keras.io/)**: Free Software (MIT License). Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Keras allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). It Supports both convolutional networks and recurrent networks, as well as combinations of the two. Runs seamlessly on CPU and GPU.\n",
    "* Apache MXNet\n",
    "* **[Caffe](http://caffe.berkeleyvision.org/)**: Free Software (BSD 2-Clause License). Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Bindings for Python and MATLAB are part of the library.\n",
    "* **[Chainer](https://chainer.org/)**: Free Software (MIT License). Chainer supports CUDA computation and runs on multiple GPUs with little effort. Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures. Forward computation can include any control flow statements of Python without lacking the ability of backpropagation. "
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    "### Cloud Focused\n",
    "\n",
    "* **[Amazon SageMaker](https://aws.amazon.com/sagemaker)**: Commercial. Amazon SageMaker provides fully managed instances running Jupyter notebooks for training data exploration and preprocessing. These notebooks are pre-loaded with CUDA and cuDNN drivers for popular deep learning platforms, Anaconda packages, and libraries for TensorFlow, Apache MXNet, Chainer, and PyTorch.\n",
    "* **[Google Cloud Machine Learning](https://cloud.google.com/products/ai/)**: Commercial. Google Cloud Machine Learning (ML) Engine is a managed service that allows developers and data scientists to build and bring machine learning models to production. Cloud ML Engine offers training and prediction services, which can be used together or individually. Cloud ML provides access to Python libraries TensorFow, Keras, XGBoost, and scikit-learn.\n",
    "* **[Azure ML Studio](https://studio.azureml.net/)**: Commercial and proprietary. Azure ML Studio allows Microsoft Azure users to create and train models, then turn them into APIs that can be consumed by other services. A wide range of algorithms are available, from both Microsoft and third parties. A free-of-cost trial allows evaluation for eight hours."
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   "source": [
    "Back to intro...\n",
    "\n",
    "<a href=\"WhatIsML.ipynb\">What Is Machine Learning?: <img src=\"img/open-notebook.png\" align=\"right\"/></a>"
   ]
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