“Deep learning is making a good wave in delivering a solution to difficult problems that have been faced in the field of artificial intelligence (AI) for so many years, as quoted by Yann LeCun, Yoshua Bengio & Geoffrey Hinton.”
For a data scientist to successfully apply deep learning, they must first understand how to apply the mathematics of modeling, choose the right algorithm to fit your model to the data, and come up with the right technique to implement.
In order to get you started, we have come up with a list of deep learning algorithms needed by every data science professional.
This 26-part course consists of tutorials on how to learn web development with Django from scratch. It's designed to be very hands-on and will walk you through every step of the web development process.
The primary objectives of this course are as follows:
Is it necessary to have knowledge of JS before starting this course?
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.
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In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and co...
Adit, this series of CNN posts is truly amazing. I already knew quite a bit about CNNs but I decided to read them anyway. Thought I would leave a review and some notes on each one. :)
I was blown away by this part, learnt so much! Again...
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:
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.