Notes about Cutting-Edge Technologies and Everything

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...

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...