The fall of RNN / LSTM We fell for Recurrent neural networks (RNN), Long-short term memory (LSTM), and all their variants. Now it is time to drop them! It is the year 2014 and LSTM and RNN mak...

Tracking with deep networks Tracking is the process of locating a user selected object in different frames as it moves around the scene. It has a variety of uses such as human-computer interaction...

You are the product Social networks and mind games: ethical concerns and ideas for proper usage from: https://www.nuvi.com/blog/2017-social-trends-facebook-fake-news-and-instant-articles/ ...

Artificial Intelligence, AI in 2018 and beyond Or how machine learning is evolving into AI These are my opinions on where deep neural network and machine learning is headed in the larger fi...

2017: an year in review How we can raise again from the lowest lows in 2018 and later~ This year 2017 was a strange year. I tried to spend a great deal of time trying to understand what made thi...

Predictive neural networks for reinforcement learning By Lukasz Burzawa, Abhishek Chaurasia and Eugenio Culurciello We used predictive neural network like CortexNet to show that they can spee...

Learning and performing in the real world AKA Reinforcement Learning In order to interact with a complex environment, living entities need to produce a “correct” sequence of actions to achieve...

Deep Neural Network Capsules A recent paper on Capsules has many important insights for revolutionizing learning in Deep Neural Networks. Let us see why! Interesting characteristics of capsule...

Neural Networks Building Blocks Neural networks are made of smaller modules or building blocks, similarly to atoms in matter and logic gates in digital circuits. Once you know what the blocks are...

layout: post title: ‘Segmenting, localizing and counting object instances in an image’ — Segmenting, localizing and counting object instances in an image When we visually perceive the world, w...