A quick guide to exploring various ways to encounter Vanishing or Exploding Gradient problem while training Neural Network using Gradient Descent Algorithm

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You must definitely have encountered the problem when training a model is getting slower for a very Deep Neural Network. This phenomenon happens prominently during the backpropagation training (using Gradient Descent) of the DNNs, wherein, each parameter’s gradient error is propagated along its way to the lower layers of the…

Part 1: Overview of Azure Cognitive Vision services to process and classify images from the Computer Vision domain of Deep Learning

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The space of cognitive services has grown up too fast with the advent of various pre-trained services provided by various public cloud providers such as Azure, AWS, or Google Cloud. …

High-level Solution Architecture based on Microsoft Azure PaaS

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Problem Context

The problem is adapted from the Mountkirk Games case study from the GCP exam. As stated — they require a new game, which is expected to be very popular. …

The gist of Machine Learning Process frameworks — KDD, CRISP-DM


Very rarely we formally discuss various data analysis process frameworks that are useful to manage our Data Analytics and Machine Learning project lifecycle. The gist of any data analysis and data mining framework is about — Data collection, data preprocessing, analyzing to find hidden insights and storytelling. …

Understanding the importance of Azure Databricks Service’s capabilities for Bigdata processing and implementing Machine Learning solutions.

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This post will briefly describe the rich features of Azure Databricks managed service and its capabilities. As a Machine Learning engineer, if you are equipped with this tool arsenal, then it will take your development skills to new heights.

Let’s first understand what is this Databricks as-a-service offering by Microsoft…

A brief overview of what, why, and how for Azure Synapse Analytics



Synapse Analytics is an integrated platform service from Microsoft Azure that combines the capabilities of data warehousing, data integrations, ETL pipelines, analytics tools & services, the scale for big-data capabilities, visualization & dashboards.

Overcoming business loss by incorporating reliability framework for application’s solution architecture


The business relies on reliable access to the data and applications to fulfill the customer’s need. If the critical system is unavailable for the end-users then it can result in eventual revenue loss to the business. …

Assume breach is obvious and use the Zero Trust model to overcome security challenges


In this second post on Cloud Well-Architected Framework, I will discuss in detail the Security pillar of the Cloud Well-Architected framework. I will elaborate on the concept of defense-in-depth for secure architecture, examples of tools and processes offered by the Public Could providers to secure the infrastructure, application, and data…

Improve application performance by identifying performance bottlenecks


Performance Efficiency Pillar of Well Architect Framework allows you to know about the factors that impact performance & stability of the application which has an unpredictable load. In this scenario, the application may have sporadic high-traffic, unknown bottlenecks, or a congested backend database. …

Part 5: An introduction to AML Environments and Compute Targets


At this stage, we have the business problem, data from the problem domain, enough experiments, experiment metadata, and the trained model. Now as a developer of MLOps engineer we are still unclear about how to manage the various environments to support model deployment. …

Pankaj Jainani

I’m here to learn new stuff on Artificial Intelligence, Cloud Computing and Nature | LinkedIn: http://www.linkedin.com/in/p-jainani | Twitter: @pankajjainani

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