neural networks

An implementation from scratch with Python and NumPyPhoto by Jeswin Thomas on UnsplashAs with many things in life, the best way to learn something is to do it yourself. I learned Neural Networks by myself, and what I...

In Deep Learning, a Convolutional Neural Network (also known as CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing images and visual imagery. To this end, ConvNets have several...

From Synthetic Gradients to Capsule Networks, Conservation Laws for Network Training & Multi-Agent Generative ModelsWelcome to the end of February edition of the ‚Machine-Learning-Collage‘ series, where I...

IntroductionOne of the biggest challenges that deep learning faces today is the addition of newer labels to the neural models without altering the architecture or storing previous datasets. The issues generally revolve...

In a new paper, a research team lead by Geoffrey Hinton combines the strengths of five advances in neural networks — Transformers, Neural Fields, Contrastive Representation Learning, Distillation and Capsules — to...

source: PyImageSearchIntroductionWriting a simple feedforward neural network is probably the first step in your journey towards mastering deep learning. Today, there are countless libraries and frameworks available to...

IntroductionMomentum is a widely-used strategy for accelerating the convergence of gradient-based optimization techniques. Momentum was designed to speed up learning in directions of low curvature, without becoming...

EyeAttend – Facial Recognition based Attendance System from scratch. – A complete approach. (Part-II)IntroductionGreetings readers, welcome to second part of the series EyeAttend. In this blog, we will go through...

Conversational AI is a set of technologies enabling human-like interactions between humans and devices based on the most natural interfaces for us: speech and natural language. Systems based on conversational AI can...

Artificial Intelligence EssentialsA Brief Introduction to Convolutional Neural Network Architecture and the Most Common LayersFigure 1. Photo by David Travis on UnsplashConvolutional Neural Networks are a family of...

Authors: Keshav Agrawal, Ayush KanodiaFundamentally, machine learning models are function approximators. For example, in trying to predict the salary of an individual from their education, we are trying to approximate...

https://medium.com/media/3ac8c6a047a88cb95bde78e9d58eca01/hrefWith autonomous vehicles taking off in the past several years, I wanted to explore one aspect that was necessary to create a good autonomous vehicle system,...

In the previous post, we scratched at the basics of Deep Learning where we discussed Deep Neural Networks with Keras. As a code along with the example, we looked at the MNIST Handwritten Digits Dataset:You can check...

Optimizing inference on CPU in the Upcoming Apache MXNet 2.0Authors: Adam Grygielski, Bartłomiej Gawrych, Sylwester FraczekIntroductionDeep Learning inference is the process of deploying a trained neural network to...

Artificial Intelligence (AI) has become a common phrase in current day and it no longer surprises us what some of the AI based systems used in our daily lives can achieve. However, this is a result of decades of...

When simplicity and ease of use starts holding you backPhoto by Barn Images on UnsplashFor people getting started with deep learning, the Keras toolbox has no equal. It has everything you need, with the confusing...

Creating a perceptron network: From zero to heroNeural Networks Series — Chapter 1, Part 2Preface noteThis story is part of a series I am creating about neural networks and it is the second part of a chapter...