In this blog we will be focusing on what are convolution neural networks and how do they work. The Convolution Neural Network or CNN as it is popularly known is the most commonly used deep learning algorithm. …

In this project we will be solving about loan prediction problem where we have to predict whether based on customer details loan should be approved or not. Here is the glance of the data.

What is a Tensorflow ? Tensorflow is a software library or framework, designed by the Google team to implement machine learning and deep learning concepts in the easiest manner. It combines the computational algebra of optimization techniques for easy calculation of many mathematical expressions. This is a official website of TensorFlow : www.tensorflow.org Let us…

In this blog we will be doing a project based on image classification where our problem statement describe us to classifies the images into two categories i.e. Emergency & Non-Emergency vehicle which is a binary classification problem and we will be solving using neural network. Before diving deep into this…

In this blog we will be studying about how Deep Learning techniques can be applied to study about unstructured data like images or text. Our main focus or agenda in this blog will be to understand what are images and how we can work with image data. …

In this blog we will understand about the various optimizers and loss functions which are most commonly used while training our neural networks. The prerequisite for this is to have basic understanding of how gradient descent algorithm works so, I would highly recommend you all to refer my previous blog…

In this blog we will learn about the activation function which are most widely used in Deep Learning. Before jumping to the point lets recap in short about the basic architecture of neural network and understand it’s working in short. For simplicity purpose consider the multilayer perceptron.

In this article we will be learning about following things. Perceptron. Bias and Weights concepts in neuron. Basic difference between step and sigmoid function. Multilayer perceptron. Forward and Backward Propagation. Gradient Descent So why are we waiting lets dig dive and understand the concepts behind all the above terms used…

The Challenge of Underfitting and Overfitting in Machine Learning. Your ability to explain this in a non-technical and easy-to-understand manner might well decide your fit for the data science role! Even when we’re working on a machine learning project, we often face situations where we are encountering unexpected performance or error rate differences between the training set and the…