Notes about Cutting-Edge Technologies and Everything

In a recent survey—AI Adoption in the Enterprise, which drew more than 1,300 respondents—we found significant usage of several machine learning (ML) libraries and frameworks. About half indicated they used TensorFlow or scikit-learn, and a third reported they were using PyTorch or Keras. I recently attended an interesting RISELab presentation delivered by Caroline Lemieux describing recent work on AutoPandas and automation tools that rely on program synthesis. In the course of her presentation, Lemieux reviewed usage statistics they had gathered on different deep learning frameworks and data science libraries. She kindly shared some of that data with me, which I used to draw this chart: The numbers are based on simple full-text searches of papers posted on the popular e-print service arXiv.org. Specifically, they reflect the number of papers which mention (in a full-text search) each of the frameworks. Using this metric, the two most popular deep learning frameworks among resear...

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...

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...

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...