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Convolutional Neural Networks For All | Part I

The mentor-curated study guide to survive the Coursera Deep Learning Specialization course 4 The first three courses of the Coursera Deep Learning Specialization were bearably tough, but then came course 4. So many great topics and concepts! But countless times stopping the videos, note taking, and lecture rewatching led us, a […]

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Real Time Object Detection with TensorFlow Detection Model

Recently,I completed the course 4, “ Convolutional Neural Network” which is offered by deeplearning.ai via Coursera as a part of “Deep Learning Specialization”. Andrew Ng, Kian Katanforoosh and Younes Bensouda Mourri offer a great deal of practical and theoretical knowledge in this specialization. Therefor I highly recommend to anyone who […]

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Can a simple CNN work as well as Facial Recognition for differentiating Redheads?

Simple Convolutional Neural Networks (CNN’s) work incredibly well at differentiating images, but can it work just as well at differentiating faces? Facial Recognition does of course use CNN’s in their algorithm, but they are much more complex, making them more effective at differentiating faces. In my small but fun project, […]

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A deeper understanding of NNets (Part 1) — CNNs

Introduction Deep Learning and AI were the buzz words for 2016; by the end of 2017, they have become more frequent and more confusing. So lets try and understand everything one at a time. We will look into the heart of Deep Learning i.e. Neural Networks (NNets). Most variants of […]

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Auto-Regressive Generative Models (PixelRNN, PixelCNN++)

Authors : Harshit Sharma, Saurabh Mishra Generative models are a subset of unsupervised learning wherein given some training data we generate new samples/data from the same distribution. There are two ways to model this distribution, with the most efficient and popular of them being Auto-Regressive models, Auto-Encoders and GANs. The basic difference between […]