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Since I am a developer and enjoy learning and working on artificial intelligence and cloud projects, I decide to write this blog post to share my project on building a real-world flower classifier with TensorFlow, Amazon SageMaker and Docker.
The goal of this post is to get you doing real data engineering as quickly as possible. We will do that by creating the simplest automated data pipeline we possibly can and getting it to run on a schedule in less than 15 minutes. With that, let’s set the scene.
In this video, I will be showing you how to build a Bioinformatics web app in Python using the Streamlit library. Particularly, the web app will allow the prediction of the solubility (LogS) of molecules, which is an important property for drug discovery.
Deep Learning has changed the game in speech recognition with the introduction of end-to-end models. These models take in audio, and directly output transcriptions. Two of the most popular end-to-end models today are Deep Speech by Baidu, and Listen Attend Spell (LAS) by Google.
In this paper, we introduce a new relay-style execution technique called L2L (layer-to-layer) where at any given moment, the device memory is primarily populated only with the executing layer(s)'s footprint. The model resides in the DRAM memory attached to either a CPU or an FPGA as an entity we call eager param-server (EPS).