Artificial Neural Networks, or ANN, as they are sometimes called were among the very first Neural Network architectures. They are inspired by network of biological neurons in our brains. I find it very important to mention here that the Artificial Neural Networks are only inspired by the biological neurons and are not designed to model …
Building an Artificial Neural Network from Scratch using Keras Read More »
Convolutional Neural Networks, or CNN, as they are better known, are widely used nowadays for a variety of tasks ranging from Natural Language Processing(NLP) to Computer Vision tasks such as Image Classification and Semantic Segmentation. In this blog post, we will be building our own Convolutional Neural Network from Scratch using the Keras library. Keras …
Building a Convolutional Neural Network from Scratch using Keras Read More »
In this blog post, we will be talking about how to use the algorithm of Nearest Neighbours for Classification with Scikit Learn. In an earlier post, we discussed about how to use the Nearest Neighbours algorithm to perform a regression task using Scikit-Learn. Further, we will be dealing only with binary classification in this post, …
Introduction to Nearest Neighbours Classification with Scikit-Learn Read More »
In this blog post, we will be talking about how to use the algorithm of Nearest Neighbours in Scikit Learn, a python library for training machine learning models. Nearest Neighbours is very simple and easy to use algorithm. In this particular post, we will not be dealing with any dataset, we will simply use a …
An Introduction to the Nearest Neighbours Algorithm in Scikit-Learn Read More »
In this blog post I will be talking about and reviewing a book that I have recently gone through- “Deep Learning for Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery & More”. But first, before we get started with it, I would like to emphasise on the fact that it is really one …
Book Review: Deep Learning for the Life Sciences Read More »
The choice of activation function is very important and can greatly influence the accuracy and training time of a model. Swish is one of the new activation functions which was first proposed in 2017 by using a combination of exhaustive and reinforcement learning-based search. The authors of the research paper first proposing the Swish Activation …
Swish as an Activation Function in Neural Network Read More »
ELU, also know as Exponential Linear Unit is an activation function which is somewhat similar to the ReLU with some differences. Similar to other non-saturating activation functions, ELU does not suffer from the problem of vanishing gradients and exploding gradients. And similar to Leaky-ReLU and PReLU, and unlike ReLU, ELU does not suffer from the …
ELU as an Activation Function in Neural Networks Read More »
Rectified Linear Unit, also known as ReLU overcame some of the serious disadvantages of the earlier used activation functions such as Sigmoid and Hyperbolic Tangent activation functions. These include the exploding gradients and vanishing gradients problem. ReLU overcame these problems and it was fast and simple to calculate. However, despite all this it was far …
Leaky ReLU as an Activation Function in Neural Networks Read More »
We have already discussed about how we can build a Linear Regression model using Numpy by calculating the solutions of the Normal Equation in the last post. In this post we will be discussing about how we can train a Linear Regression model by using Tensorflow to calculate the solutions of the Normal Equation. Tensorflow …
Training a Linear Regression Model using Tensorflow Read More »
Linear Regression is one of the most basic algorithms for building Machine Learning models. It is of great importance in statistics too. It can be used to build linear regression models. There are many algorithms to train a linear regression model such as using the normal equation, gradient descent, stochastic gradient descent and batch gradient …