This is the first installment in our blog series about deep learning. In this series, we will discuss the deep learning technology, available frameworks/tools, and how to scale deep learning using big data architecture. Machine learning is a branch of computer science that studies the algorithms that learn with the help of observational data but without explicit programming. In other words, the goal is to design algorithms that can learn automatically without the intervention of humans. In general, Machine Learning can be considered as a subfield of AI.

Apache Spark machine learning workloads can run up to 10x faster by moving them to a deep learning paradigm on GPUs, according to Databricks, which today announced that its hosted Spark service on Amazon's new GPU cloud. Databricks, the primary commercial venture behind Apache Spark, today announced that it's now supporting TensorFrames, the new Spark library based on Google's (NASDAQ: GOOG) TensorFlow deep learning framework, on its hosted Spark service, which runs on Amazon Web Services (NASDAQ: AMZM). The deep learning service will be generally available within two weeks, the company says. TensorFrames, which was unveiled this March as a technical preview, lets Spark harness TensorFlow for the purpose of programing deep neural networks, the primary computational method powering so-called "deep learning" algorithms. TensorFrames is also available to on-prem Spark users as a GitHub project, but it's not yet available for download in the Apache Spark project, which limits its usefulness for the time being.