Notes about Cutting-Edge Technologies and Everything

The software and hardware considerations they made to successfully scale AI/ML infrastructure per an excellent talk giv en by  Yangqing Jia , Facebook’s Director of AI Infrastructure, at the Scaled Machine Learning Conference. Watch Video >>> Full Article >>>

Comparing Top Deep Learning Frameworks: Deeplearning4j, PyTorch, TensorFlow, Caffe, Keras, MxNet, Gluon & CNTK Skymind bundles Deeplearning4j and Python deep learning libraries such as Tensorflow and Keras (using a managed Conda environment) in the Skymind Intelligence Layer (SKIL), which offers ETL, training and one-click deployment on a managed GPU cluster. The SKIL Community Edition is free and downloadable here . Eclipse Deeplearning4j is distinguished from other frameworks in its API languages, intent and integrations. DL4J is a JVM-based, industry-focused, commercially supported, distributed deep-learning framework that solves problems involving massive amounts of data in a reasonable amount of time. It integrates with Kafka, Hadoop and Spark using an arbitrary number of GPUs or CPUs , and it has a number you can call if anything breaks. DL4J is portable and platform neutral, rather than being optimized on a specific cloud service such as AWS, Azure or Goog...

Machine learning platforms are not the wave of the future. It's happening now. Developers need to know how and when to harness their power. Working within the ML landscape while using the right tools like Filestack can make it easier for developers to create a productive algorithm that taps into its power. The following machine learning platforms and tools — listed in no certain order — are available now as resources to seamlessly integrate the power of ML into daily tasks. 1.  H2O H2O was designed for the Python, R, and Java programming languages by H2O.ai. By using these familiar languages, this open source software makes it easy for developers to apply both predictive analytics and machine learning to a variety of situations. Available on Mac, Windows, and Linux operating systems, H2O provides developers with the tools they need to analyze data sets in the Apache Hadoop file systems as well as those in the cloud. 2.  Apache PredictionIO Developer...

The best trained soldiers can’t fulfill their mission empty-handed. Data scientists have their own weapons  —  machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts. And that’s why we interviewed data science practitioners — gurus, really  — regarding the useful tools they choose for  their  projects. The specialists we contacted have various fields of expertise and are working in such companies as Facebook and Samsung. Some of them represent AI startups (Objection Co, NEAR.AI, and Respeecher); some teach at universities (Kharkiv National University of Radioelectronics). The AltexSoft data science team joined the discussion, too. And if you’re looking for a particular type of tools, just skip to your sector of interest: Languages used in machine learning Data analytics an...

The resource below describes the following frameworks: TensorFlow  Theano Caffe MXNet Apache SystemML (incubator project)  BigDL  DistBelief Details >>>

Comparing Top Deep Learning Frameworks: Deeplearning4j, PyTorch, TensorFlow, Caffe, Keras, MxNet, Gluon & CNTK:  https://deeplearning4j.org/compare-dl4j-tensorflow-pytorch

Nice brief overview of some Machine Learning algorithms highlighting their strengths and weaknesses. Big 3 machine learning tasks, which are by far the most common ones. They are:     Regression     Classification     Clustering Details: https://elitedatascience.com/machine-learning-algorithms Here are also some observations on the top five characteristics of ML libraries that developers should consider when deciding what library to use: Programming paradigm Symbolic: Spark MLlib, MMLSpark, BigDL, CNTK, H2O.ai, Keras, Caffe2 Imperative: scikit-learn, auto sklearn, TPOT, PyTorch Hybrid: MXNet, TensorFlow Machine learning algorithms Supervised and unsupervised: Spark MLlib, scikit-learn, H2O.ai, MMLSpark, Mahout Deep learning: TensorFlow, PyTorch, Caffe2 (image), Keras, MXNet, CNTK, BigDL, MMLSpark (image and text), H2O.ai (via the deepwater plugin) Recommendation system: Spark MLlib, H2O.ai (via the sparkling-water plugin), Mah...