Build Keras model using mixed and imbalanced data of medical imaging and patient data to determine the presence of Melanoma.


Build deep learning model in Keras using Sequential, Functional Keras API, and Model Subclassing

Keras Sequential Model

Sequential is the most common and simple technique to build models in Keras.

Sequential groups a linear stack of layers into a Keras Model. The Sequential model is built by passing a list of layers to the Sequential constructor

When to build Sequential models in Keras?


What are DevOps, MLOps, and AIOps? How do they help organizations with Digital Transformation?

DevOps: Integration of Development, Operation, and Quality Assurance

DevOps collaborates development, quality assurance, and operations that involve people, processes, and technology to streamline the software development and release throughput using a cycle of Continuous Integration(CI) and Continuous Deployment(CD).

CICD Pipeline(image by author)


Learn the advanced data labeling techniques: Weak Supervision and Active Learning

Photo by Firmbee.com on Unsplash
  • Why do we need advanced data labeling techniques?
  • What is Active Learning and different types of Active Learning?
  • What is Weak Supervision, and how does it work?
  • Differences between Weak Supervision and Active Learning


Label the unlabelled data using a semi-supervised Label Propagation algorithm

  • What is semi-supervised learning?
  • What is label propagation?
  • How does it work?
  • A Python implementation using sklearn

Semi-Supervised learning is a combination of supervised and unsupervised learning.

Supervised learning employs labeled data for training to learn the relationship between the input data and the target variable; however, unsupervised learning utilizes unlabeled data to identify the hidden data pattern in the input data.


A step-by-step explanation and implementation of Vision Transformer using TensorFlow 2.3


Learn hyperparameter tuning for your deep learning models using KerasTuner

  • 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
  • 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

  • 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

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

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


DevOps

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

  • 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…

Renu Khandelwal

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

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