Life Cycle of a Deep Learning Computer Vision Project The life cycle of a deep learning computer vision project involves multiple stages, from defining the problem to deploying and monitoring the…

Epoch in Deep Learning In the context of Epoch in Deep Learning, an epoch is one complete pass through the entire training dataset.  During an epoch, the Deep learning algorithm…

Deep Learning Backward Propagation  In this blog, will discuss more Deep Learning Backward Propagation. Forward Propagation is based on random weights. If we use the random weights, cost/error/loss will be…

Forward and Backward Propagation in Deep Learning In the vast sea of neural networks, Forward and Backward Propagation in Deep Learning are the guiding currents that drive the learning process.…

The Data Engineers Guide Hey, this is Suraj. Data is the lifeblood of modern organizations. In an era where data volumes are skyrocketing, building a reliable data pipeline has become…

RNN(Recurrent Neural Network)  Recurrent Neural Networks (RNNs) are a type of neural network that is able to process sequential data, such as time series or natural language. They are particularly…

Introduction to Deep Learning Deep Learning is a subset of machine earning inspired by neural  networks in the human brain  It has been around for a decade, but the reason…

Dropout Layers -   Nowadays deep learning, every data scientist should have used the dropout layer at a particular moment in his or her full career of building neural networks.…

Regression and KNN - Picking parameters - This cannot be solved directly for in closed form for finding the positions of the various centers and the widths of the RBF…

Matching Techniques - Matching is the process of checking and comparing two or multiple structures to find their likenesses or comparisons. These structures will represent a wide range of objects…

  Deep and Reinforcement Learning  Deep learning has been a famous word in the machine learning world in recent times. The main goal of the deep learning algorithm is to…