Aloha!

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So it’s Sunday evening and I’ve been wondering what to do. After not a lot of thinking, I decided to FINALLY build a Serverless REST API on AWS.

So here we go…

We’ll be using:

  1. API Gateway for deploying and serving HTTP RESTful endpoints.

First thing’s first, let’s start by creating our DynamoDB Table which we’ll use to Store/Delete/Read, from our API.


Aloha

Architecture Description and Deployment Pattern

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Architecture of Serverless REST Services for Scale on AWS

The client requests are first received at low latency because of Cloudfront. The requests are then filtered by WAF and passed on to Cognito for both AuthZ and AuthN. The approved and valid requests are then routed by API Gateway to respective endpoint functions hosted on AWS Lambdas. Most of the GET requests are fulfilled by data cached at API Gateway. Remaining requests are taken care of by independent GET Lambda spun up for each request. These GET Lambdas fetch results by making calls to Read-Replicas thus eliminating a huge load on Master RDS. All write requests from PUT/POST/DELETE Lambdas are pushed into Kinesis Firehose to avoid failures at RDS and reduce any stress on Master RDS because of these calls. As Kinesis has a maximum retention period (7 days at the time of writing of this article) and we’d want to avoid any loss of data incase the RDS goes down, we keep a copy of the requests in Kinesis Firehose in S3 bucket. …


Aloha

In the previous post we looked at basic reasons behind the success of Deep Learning and got the understanding of commonly used activation functions. In this post we’ll be looking at mathematics behind Deep Learning. Most of the beginners in the field of Deep Learning are usually people who know (Deep Learning -Mathematics). This is probably the case because they are unable to understand the basic intuition behind the Mathematics part of Deep Learning. I’ll try to cover most of it in this post. So without wasting anymore time Lets Begin!

Derivative: The derivative of a function of a real variable measures the sensitivity to change of the function value (output value) with respect to a change in its argument (input value). …


Aloha

In the last post, I gave you an overview of Deep Learning. In this post we’ll try to focus on the main reason behind the success of Deep Learning, i.e, Neural Networks.

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Almost all the major companies use Deep Learning for tasks involving speech recognition, speech synthesis and machine translation. As far as machine translation is concerned, since deep learning encodes language to its vectorial features, it is even possible to learn translations between pairs of languages on which the model has not been trained. …


Aloha!

Welcome to this series of blogs wherein I’ll try to explain the difference between Deep Learning and Machine Learning. For this, I’ll be referring the following books :

  1. Machine Learning Probabilistic Perspective By Kevin Murphy.

Introduction

Today, Artificial intelligence(AI) is a thriving field with many practical applications and active research topics. The true challenge to artificial intelligence is to solve problems that human solve intuitively and by observing things like spoken accent and faces in an image.

The solution to the above problem is to allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of its relation to simpler concepts. By gathering knowledge from experience, this approach avoids the need for human operators to formally specify all of the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones. If we draw a graph showing how these concepts are built on top of each other, the graph is deep, with many layers. …

About

Aashray Jain

An Uncluttered Soul.

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