Now that we got data from MongoDB and loaded into Postgres with some transformation, let’s put the data back into MongoDB. This post is the continuation of the previous post about ingesting data from MongoDB. You should totally start from How to Ingest Data From MongoDB with Node.js. The aim …

Data ingestion from a NoSQL database often involves the denormalisation of their schema-less data before loading into a relational database. In this post, we will try to grab the restaurant data from MongoDB, denormalise the collection into a parent and a child table, and load them into Postgres. This exercise …

In the last post, we built AlexNet with Keras. This is the second part of AlexNet building. Let’s rewrite the Keras code from the previous post (see Building AlexNet with Keras) with TensorFlow and run it in AWS SageMaker instead of the local machine. AlexNet is in fact too heavy …

As the legend goes, the deep learning networks created by Alex Krizhevsky, Geoffrey Hinton and Ilya Sutskever (now largely know as AlexNet) blew everyone out of the water and won Image Classification Challenge (ILSVRC) in 2012. This heralded the new era of deep learning. AlexNet is the most influential modern …

TensorFlow offers both high- and low-level APIs for Deep Learning. Coding in TensorFlow is slightly different from other machine learning frameworks. You first need to define the variables and architectures. This is because the entire code is executed outside of Python with C++ and the python code itself is just …

The most basic neural network architecture in deep learning is the dense neural networks consisting of dense layers (a.k.a. fully-connected layers). In this layer, all the inputs and outputs are connected to all the neurons in each layer. Keras is the high-level APIs that runs on TensorFlow (and CNTK or …