One email per week, 5 links.

Do you want to keep up to date with the latest trends of machine learning, data science, and artificial intelligence?

But keeping up to date with all the blogs, podcasts, and articles is time consuming so why not let someone else curate the content for you?

With our weekly newsletter you will get 5 top stories hand-picked into your inbox every Monday with topic ranging from neural networks, deep learning, Markov chains, natural language processing, covering scientific papers, and even basics of statistics, data science, and data visualisations.

Escape the distractions of social media and own your focus. Check out the latest issue and subscribe!

AI Digest#122


RudderStack: An Open Source Segment Alternative

An Open Source Customer Data Platform built for Developers. Offering Segment API compatibility, multiple hosting options, fixed infrastructure based pricing & powerful real time transformations.

this week's favorite

Data Engineering from the Ground Up: First Data Pipeline

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.

Build, Train and Deploy A Real-World Flower Classifier of 102 Flower Types

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.

Building a Bioinformatics Web App in Python

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.

Training Large Neural Networks with Constant Memory using a New Execution Algorithm

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).

Customer Case Study: Building an end-to-end Speech Recognition model in PyTorch with AssemblyAI

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.