Prabhav JainCo-founder, Commonlo… · 2y
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Deep Learning
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Intuition behind concepts. Research paper summaries and discussion. Links to tools, datasets, code and other resources.
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Deep Learning Research Review Week 1: Generative Adversarial Nets
Starting this week, I’ll be doing a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. This week I’ll begin with Generative Adversarial Networks.
Introduction
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The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)
Introduction
In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and co...
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Tutorial: Linearly separable data
The idea of linearly separable is easiest to visualize and understand in 2 dimensions. Let the two classes be represented by colors red and green.
A dataset is said to be linearly separable if it is possible to draw a line that can separate the red and green points from each other.
Here are same examples of linearly separable data:
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Deep Learning Research Review Week 2: Reinforcement Learning
This is the 2nd installment of a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. This week focuses on Reinforcement Learning.
Introduction to Reinfo...
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tutorial
Gradient Descent intuition
Imagine you're blind folded in a rough terrain, and your objective is to reach the lowest altitude. One of the best and simplest strategies you can use, is to feel the ground in every direction, and take a step in the direction where the ground is descending the fastest. If you keep repeating this process, you might end up at the lake, or even better, somewhere in the huge valley.
The rough terrain is analogous to the cost function....
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