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IPython Notebook(s) demonstrating deep learning functionality.IPython Notebook(s) demonstrating scikit-learn functionality.

https://github.com/donnemartin/data-science-ipython-notebooksTags | machine-learning deep-learning data-science big-data aws tensorflow theano caffe scikit-learn kaggle spark mapreduce hadoop matplotlib pandas numpy scipy keras |

Implementation | Python |

License | Public |

Platform | Windows Linux |

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! π)

machine-learning deep-learning tensorflow pytorch keras matplotlib aws kaggle pandas scikit-learn torch artificial-intelligence neural-network convolutional-neural-networks tensorflow-tutorials python-data ipython-notebook capsule-network"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

machine-learning deep-learning text-analytics classification clustering natural-language-processing computer-vision data-science spacy nltk scikit-learn prophet time-series-analysis convolutional-neural-networks tensorflow keras statsmodels pandas deep-neural-networksXLearning is a convenient and efficient scheduling platform combined with the big data and artificial intelligence, support for a variety of machine learning, deep learning frameworks. XLearning is running on the Hadoop Yarn and has integrated deep learning frameworks such as TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning has the satisfactory scalability and compatibility.Besides the distributed mode of TensorFlow and MXNet frameworks, XLearning supports the standalone mode of all deep learning frameworks such as Caffe, Theano, PyTorch. Moreover, XLearning allows the custom versions and multi-version of frameworks flexibly.

hadoop tensorflow caffe mxnet yarnThis is the list of published articles on medium.com π¬π§, habr.com π·πΊ, and jqr.com π¨π³. Icons are clickable. Also, links to Kaggle Kernels (in English) are given. This way one can reproduce everything without installing a single package. Assignments will be announced each week. Meanwhile, you can pratice with demo versions. Solutions will be discussed in the upcoming run of the course.

machine-learning data-analysis data-science pandas algorithms numpy scipy matplotlib seaborn plotly scikit-learn kaggle-inclass vowpal-wabbit ipynb docker mathDistributed Deep Learning with Apache Spark and Keras. Distributed Keras is a distributed deep learning framework built op top of Apache Spark and Keras, with a focus on "state-of-the-art" distributed optimization algorithms. We designed the framework in such a way that a new distributed optimizer could be implemented with ease, thus enabling a person to focus on research. Several distributed methods are supported, such as, but not restricted to, the training of ensembles and models using data parallel methods.

machine-learning deep-learning apache-spark data-parallelism distributed-optimizers keras optimization-algorithms tensorflow data-science hadoopThis is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.

spark pyspark data-analysis mllib ipython-notebook notebook ipython data-science machine-learning big-data bigdataPractice and tutorial-style notebooks covering wide variety of machine learning techniques

numpy statistics pandas matplotlib regression scikit-learn classification principal-component-analysis clustering decision-trees random-forest dimensionality-reduction neural-network deep-learning artificial-intelligence data-science machine-learning k-nearest-neighbours naive-bayesThis repository contains lecture transcripts and homework assignments as Jupyter Notebooks for the first of three Kadenze Academy courses on Creative Applications of Deep Learning w/ Tensorflow. It also contains a python package containing all the code developed during all three courses. The first course makes heavy usage of Jupyter Notebook. This will be necessary for submitting the homeworks and interacting with the guided session notebooks I will provide for each assignment. Follow along this guide and we'll see how to obtain all of the necessary libraries that we'll be using. By the end of this, you'll have installed Jupyter Notebook, NumPy, SciPy, and Matplotlib. While many of these libraries aren't necessary for performing the Deep Learning which we'll get to in later lectures, they are incredibly useful for manipulating data on your computer, preparing data for learning, and exploring results.

jupyter-notebook neural-network tensorflow deep-learning mooc dockerfile machine-learning tutorial workshopKeras 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. Being able to go from idea to result with the least possible delay is key to doing good research.

deep-learning tensorflow theano neural-networks machine-learning data-scienceWelcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.

deep-learning machine-learning artificial-intelligence data-science reinforcement-learning kubernetes tensorflow pytorch keras mxnet caffe ai dl ml k8sThis repository was initially created to submit machine learning assignments for Siraj Raval's online machine learning course. The purpose of the course was to learn how to implement the most common machine learning algorithms from scratch (without using machine learning libraries such as tensorflow, PyTorch, scikit-learn, etc). Although that course has ended now, I am continuing to learn data science and machine learning from other sources such as Coursera, online blogs, and attending machine learning lectures at University of Toronto. Sticking to the theme of implementing machine learning algortihms from scratch, I will continue to post detailed notebooks in python here as I learn more.

machine-learning statistical-concepts siraj-raval machine-learning-algorithms machine-learning-from-scratchThis GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository instead. What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.

machine-learning machine-learning-algorithms logistic-regression data-science data-mining scikit-learn neural-networkI just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competitionauto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. All of these projects are ready for production. These projects all have prediction time in the 1 millisecond range for a single prediction, and are able to be serialized to disk and loaded into a new environment after training.

machine-learning data-science automated-machine-learning gradient-boosting scikit-learn machine-learning-pipelines machine-learning-library production-ready automl lightgbm analytics feature-engineering hyperparameter-optimization deep-learning xgboost keras deeplearning tensorflow artificial-intelligenceThis library is the official extension repository for the python deep learning library Keras. It contains additional layers, activations, loss functions, optimizers, etc. which are not yet available within Keras itself. All of these additional modules can be used in conjunction with core Keras models and modules. As the community contributions in Keras-Contrib are tested, used, validated, and their utility proven, they may be integrated into the Keras core repository. In the interest of keeping Keras succinct, clean, and powerfully simple, only the most useful contributions make it into Keras. This contribution repository is both the proving ground for new functionality, and the archive for functionality that (while useful) may not fit well into the Keras paradigm.

keras theano tensorflow machine-learning deep-learning neural-networks data-scienceMMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.MMLSpark requires Scala 2.11, Spark 2.1+, and either Python 2.7 or Python 3.5+. See the API documentation for Scala and for PySpark.

machine-learning spark cntk pyspark azure microsoft-machine-learning microsoft mlTensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters. By combining salient features from deep learning framework TensorFlow and big-data frameworks Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers.TensorFlowOnSpark was developed by Yahoo for large-scale distributed deep learning on our Hadoop clusters in Yahoo's private cloud.

tensorflow spark yahoo machine-learning cluster featuredTel-Aviv Deep Learning Bootcamp is an intensive (and free!) 5-day program intended to teach you all about deep learning. It is nonprofit focused on advancing data science education and fostering entrepreneurship. The Bootcamp is a prominent venue for graduate students, researchers, and data science professionals. It offers a chance to study the essential and innovative aspects of deep learning. Participation is via a donation to the A.L.S ASSOCIATION for promoting research of the Amyotrophic Lateral Sclerosis (ALS) disease.

gpu nvidia docker-image machine-learning deep-learning data-science cuda-kernels kaggle-competition cuda pytorch pytorch-tutorials pytorch-tutorial bootcamp meetup kaggle kaggle-scripts pycudaThis repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. Run the code using the Jupyter notebooks available in this repository's notebooks directory.

scikit-learn numpy jupyter-notebook matplotlib pandasKeras 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. Being able to go from idea to result with the least possible delay is key to doing good research. Read the documentation at Keras.io.

deep-learning tensorflow neural-networks machine-learning data-science
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