Learn hyperparameter tuning for your deep learning models using KerasTuner

What knowledge will you gain here?

  • What are hyperparameters for a deep learning algorithm?
  • Why do we need hyperparameter optimization?
  • Different techniques for hyperparameter optimization like Grid Search, Random Search, Bayesian Optimization, Simulated Annealing, and Hyberband
  • What is KerasTuner, and how does it help with hyperparameter optimization?
  • Implementat hyperparameter optimization on Fashion MNIST dataset using a deep Convolutional Neural Network
Photo by Jane Carmona on Unsplash

To train a deep learning model on input data, we have two different types of parameters.

  • Model Parameters learned as part of neural network training like the weights and biases which change during a training job.
  • Hyperparameters govern the training…

Deep Learning

Learn how to label images using Deep Convolutional Autoencoders

You have a mix of images for different classes in a folder, how do you label these images when you are aware of the number of classes.

Here you will implement deep convolutional Autoencoders to label the Fashion MNIST dataset with just using the Fashion MNIST images.

Assumptions made for the labeling images using Autoencoders:

  • knowledge of the number of classes in the dataset
  • A few images available from each class

Overview of Autoencoder

Autoencoders consist of an Encoder and a Decoder network. The Encoder encodes the high dimension input into a lower-dimensional latent representation also referred to as the bottleneck layer. The…

Machine Learning

A quick read on how Support Vector Machines(SVM) and its usage for Multiclass classification

you will learn

  • What are Support Vector Machines?
  • Features of SVM and its application
  • Explanation of different SVM hyperparameters
  • Python implementation for Multiclass classification
image by author

SVM algorithm was proposed by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1963

Support vector machine is a supervised machine learning algorithm used for classification as well as regression. SVM’s objective is to identify a hyperplane to separate data points into two classes by maximizing the margin between support vectors of the two classes


An Easy to Understand Guide to Kubernetes- K8s, K3s, and MicroK8s

What is Kubernetes-K8s, why we need them and how Kubernetes work, what K3s and MicroK8s, and the similarities and differences between K3s and MicroK8s?

You want to run multiple instances of your deep learning solutions with several components like data. preprocessing, making inferences, and writing the information to the database or a file.

How can you easily scale and deploy your solutions?

Here are few things you want when you scale and deploy your solution

  • Efficiently utilize the hardware computational resources
  • Balance the load and distribute the network traffic to have stable deployments
  • Automatically manage mount storage system to help…

Machine Learning

An unsupervised clustering algorithm to hierarchically cluster data sharing common characteristics into distinct groups

In this article, you will learn.

  • What is Hierarchical clustering, and where is it used?
  • Two different types of Hierarchical clustering -Agglomerative and Divisive Clustering
  • How does the Hierarchical Clustering algorithm work with an understanding of different linkages and metrics?
  • What is a dendrogram?
  • Finding the optimal number of clusters from a dendrogram
  • Implement Hierarchical clustering using python

Clustering is the most common form of unsupervised learning on unlabeled data to clusters objects with common characteristics into discrete clusters based on a distance measure.

Common Clustering Algorithms are

  • Centroid based clustering like KMeans which is efficient but sensitive to initial…

Machine Learning

Find optimal cluster in K-Means clustering using Elbow method, Silhouette score, and Gap statistics.

In this article, you will gain an understanding of

  • What is K-Means clustering?
  • How does K-Means work?
  • Applications of K-Means Clustering
  • Implementation of K-Means Clusterings in Python
  • Finding optimal clusters using the Elbow method, Silhouette score, and Gap Statistics
Image by Author

K-Means clustering is a simple, popular yet powerful unsupervised machine learning algorithm. An iterative algorithm to finds groups of data with similar characteristics for an unlabeled data set into clusters.

The K-Means algorithm aims to have cohesive clusters based on the defined number of clusters, K. …

Machine Learning

Learn to Understand the Root Cause of Performance Degradation of Machine Learning Models in Production

You will learn

  • Why ML models degrade after deployment?
  • Difference between Data Drift and Concept Drift
  • Different techniques to handle model degradation

You trained an ML model with great performance metrics and then deployed it in production. The model worked great in production for some time, but your users observed the model recently is not predicting reliable results.

What must be going on with the model? Is it the model that is the issue or the data in the production that is the root cause?

We provide the data and the results during the training of a Machine Learning/Deep Learning…

Measure Statistical Distributional Similarity using Kullback–Leibler Divergence, JensenShannon divergence, and Kolmogorov–Smirnov

In this article, you will explore common techniques like KL Divergence, Jensen-Shannon divergence, and KS test used in Machine Learning to measure the similarity between distributions statistically.

Why do we need to measure similarity or divergence between probability distribution used in Machine Learning?

In Machine Learning, you will encounter probability distributions for continuous and discrete input data, outputs from models, and error calculation between the actual and the predicted output.

  • Measuring the probability distribution of all input and output features helps identify the data drift.
  • When training models, you would like to minimize the error. The error can be minimized…

Explore Deep Convolutional Autoencoders to identify Anomalies in Images.

This article is an experimental work to check if Deep Convolutional Autoencoders could be used for image anomaly detection on MNIST and Fashion MNIST.

Autoencoder in a nutshell

Functionality: Autoencoders encode the input to identify important latent feature representation. It then decodes the latent features to reconstruct output values identical to the input values.

Objective: Autoencoder’s objective is to minimize reconstruction error between the input and output. This helps autoencoders to learn important features present in the data.

Architecture: Autoencoders consists of an Encoder network and a Decoder network. The encoder encodes the high dimension input into a lower-dimensional latent representation also referred to…

Change your mindset to transform your life

We are shaped by our thoughts; we become what we think- Gautam Buddha

Photo by Hester Qiang on Unsplash

Introspection on my mindset and thought process helped me change my destiny.

Around 6 years ago, I was working with a mindset that my abilities were fixed, my learning was limited, and as a result, my growth was stagnant. I was not happy with my then-current state.

Words of the great philosopher echoed.

Knowing yourself is the beginning of all wisdom-Aristotle

When I was looking outside, I believed that someone else was responsible for my situation. I was not happy or peaceful. Introspection helped me understand that…

Renu Khandelwal

Loves learning, sharing, and discovering myself. Passionate about Machine Learning and Deep Learning

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