MNIST Muddle
Creating poorly written numeric digits using AutoEncoders and MNIST dataset. Demo website - LINK GitHub link - LINK Contents: Project aim...
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Apr 29, 20213 min read
H.O.M.L Ch-4 | Training models algo
Uncovering some of the black box algorithms in this chapter. Mainly we'll be talking about Linear Regression, Polynomial Regression,...
Apr 6, 20211 min read
Azure Cognitive services using Python & REST API | Text-to-Speech
Python script to access Azure's Text-to-Speech cognitive API. Converts all sentences from a file to respective speech audios.
Apr 3, 20216 min read
H.O.M.L Chapter-3 | Classification
Turning our attention to Classification systems. Contents: Performance Measure for Classification Precision Recall F1 score...
Mar 27, 20213 min read
H.O.M.L Ch-2 notes | End-to-end ML Project
Contents: Root mean square error vs Mean absolute error Few points on test set preparation Correlation Data cleaning caution Text and...
Feb 25, 20215 min read
H.O.M.L Chp-1 Summary | The ML Landscape
[1]Types of ML Systems.
[2]Main Challenges of Machine Learning.
[3]Testing and Validation.
Dec 27, 20201 min read
CTC Loss (part-2) | Forward pass using alpha matrix
In this post we will see how CTC Loss is efficiently calculated using Dynamic Programming. We do that by creating a 2D matrix, known as...
Dec 23, 20203 min read
CTC Loss for un-segmented Data
Its importance & where it is used: It is used for dealing with un-segmented sequence data. Such data is ubiquitous and it may not be...
Jul 12, 20203 min read
Restoring trained weights after modifying the model graph in TensorFlow
Background: Recently, I had a situation where my trained model (TF) could not run on a platform because few nodes in the graph...
Jul 5, 20203 min read
Batch Normalization - 1 | Using tf.nn.batch_normalization
See how to correctly use batch normalization in TensorFlow using tf.nn.batch_normalization. User needs to properly update the values duri...
Jun 20, 20201 min read
Post init
This is my first blog post. Ready to share the world what I learn everyday. Show love if you enjoy the posts. -Divakar
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