Perceptron to Multi-layered Feedforward Neural Network

This article will help you understand the transition of AI from classical machine learning to deep learning, starting from the basics of machine learning with its major - supervised and unsupervised learning to different regularization and optimization techniques.

Improving an Artificial Neural Network with Regularization and Optimization

In this article, we will discuss regularization and optimization techniques that are used by programmers to build a more robust and generalized neural network. We will study the most effective regularization techniques like L1, L2, Early Stopping, and Drop out which help for model generalization.

Introduction to Artificial Neural Networks for Beginners

Introduction to Artificial Neural Networks for Beginners. Understanding the concepts of Neural Networks.

Demystify Employee Leaving with Machine Learning

Demystify Employee Leaving with Machine Learning. Creation and Evaluation of Handful of Machine Learning Models for Leave Prediction. I will share recent work in the human resource domain to bring some predictive power to any firm struggling to retain their employees.

Optimizers Optimizers are algorithms , change the attributes of the neural network

Optimizers are algorithms or methods used to change the attributes of the neural network such as weights and learning rates in order to reduce the losses.

Machine Learning: Similarities With Human Decision Making

Machine Learning: Similarities With Human Decision Making. Machine Learning as we know have evolved immensely in the last few decades.

Deep-fake Detection using OpenCV and MTCNN

In this article, we will discuss how to identify fake from real ones. It includes breaking down videos into a frame, detecting the faces from real and fake videos, crop the faces, and analyzing it. Deep-fake Detection Using OpenCV and MTCNN

My findings on using Machine Learning for sports betting

One afternoon, in the middle of my holidays the thought of using machine learning to predict football results in the premier leagues came to my mind.

The Ultimate Beginner’s Guide to TensorFlow

The Ultimate Beginner’s Guide to TensorFlow: In this tutorial, we will cover TensorFlow in enough depth so that you can train machine learning models from scratch!

Math Behind Artificial Neural Networks

Artificial neural networks seen to be useful in many applications in recent times like prediction, classification, recognition,translation…

Introduction to Neural Networks

There has been hype about artificial intelligence, machine learning, and neural networks for quite a while now. This will not be a math-heavy introduction because I just want to build the idea here.

Reducing the Artificial Neural Network complexity by transforming your data

A practical example in a hard-to-classify dataset. The need to reduce the complexity of a model can arise from multiple factors, often to reduce the computational requirements.

Convolutional Neural Networks

Basic fundamentals of CNN. CNN’s are a special type of ANN which accepts images as inputs. Below is the representation of a basic neuron of an ANN which takes as input X vector.

How Big Tech use Machine Learning?

Machine learning is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Artificial Neural Networks- An intuitive approach

To build a machine learning algorithm, usually you’d define an architecture (e.g. Logistic regression, Support Vector Machine, Neural Network) and train it to learn parameters.

Learning Rates and Best Practices for Deep Learning

Learning Rates and Best Practices for Deep Learning. Explore best practices for creating deep learning models in Keras and finding the optimal learning rate.

Artificial Neural Networks- An intuitive approach

A continuation of an earlier article. Perceptrons take inputs , scale/multiple them with weights , sum them up and then pass them through an activation function to obtain a result.

The Weird and Wonderful World of AI Art

While the vast majority of developments in AI technology have centered around practical solutions such as self-driving cars and facial recognition, there's a growing number of artists using AI systems to develop new ideas for artistic projects and generate entirely unique pieces of work.

The Key Differences Between Rule-Based AI And Machine Learning

Rule-based systems and machine learning models are widely utilized to make conclusions from data. Both of these approaches have advantages and disadvantages. Several corporations are implementing and exploring tasks related to artificial intelligence to automate business processes, upgrade product improvement and to enhance market experiences. This blog provides some of the crucial points that should be considered before doing investment in any of the techniques.

Comparison between Logistic Regression and Neural networks

I recently learned about logistic regression and feed forward neural networks and how either of them can be used for classification.