Paper Explanation : ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)

AlexNet famously won the 2012 ImageNet LSVRC-2012 competition by a large margin (15.3% VS 26.2% (second place) top-5 test error rates). This started the era of deep learning, bringing neural networks back into the spotlight! Problems the paper addressed To show that it is possible to successfully train a deep CNN with a large number … Continue reading Paper Explanation : ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)

Paper Explanation: Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks

What Led to Faster R-CNN? Even though networks like Fast R-CNN achieve a really high accuracy, they are very slow to be of practical use in real time. The reason for such slow speeds is the region proposal step in the architecture! Algorithms, like Selective Search (used in Fast R-CNN) take around 2 seconds per … Continue reading Paper Explanation: Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks

Paper Explanation: Fast R-CNN

Called Fast R-CNN because it's comparatively fast to train and test. Why Fast R-CNN? Even though R-CNN had achieved state of the art performance on object detection it had many problems: Slow at test-time due to independent forward passes of the CNN for each region proposal. The CNN doesn't get trained during the training of the … Continue reading Paper Explanation: Fast R-CNN

What are Generative Learning Algorithms?

I will try to make this post as light on mathematics as is possible, but a complete in depth understanding can only come from understanding the underlying mathematics! Generative learning algorithms are really beautiful! Like anything they have there advantages and disadvantages. These algorithms are a bit "tough" to understand but comparing them by the … Continue reading What are Generative Learning Algorithms?

https://www.coursera.org/account/accomplishments/specialization/certificate/HRSFQE75DAGX

My thoughts on the Deep Learning Specialization on Coursera by deeplearning.ai

Having just finished the specialization, I want to share my thoughts on how I felt about the whole journey.   Before I start, I want to mention my experience and knowledge in deep learning prior to taking the specialization. I had watched the lecture videos of the Stanford Computer Vision and deep learning course, CS … Continue reading My thoughts on the Deep Learning Specialization on Coursera by deeplearning.ai

Paper Explanation: Rich feature hierarchies for accurate object detection and semantic segmentation (R-CNN)

To what extent do the CNN classification generalise to object detection? Object detection is the task of finding the different objects in an image and classifying them (as seen in the image above). This paper is the first to show that a CNN can lead to dramatically higher object detection performance Let’s now take a moment … Continue reading Paper Explanation: Rich feature hierarchies for accurate object detection and semantic segmentation (R-CNN)

Installing TensorFlow GPU from Source on Ubuntu

In this tutorial I will be going through the process of building the latest TensorFlow from sources for Ubuntu 16.04 using Python3. In order to use TensorFlow with GPU support you must have a Nvidia graphic card with a minimum compute capability of 3.0. This is going to be a long procedure! So let's brace ourselves and … Continue reading Installing TensorFlow GPU from Source on Ubuntu