Artificial Intelligence

CNN Explainer Learn Convolutional Neural Network (CNN) in your browser! https://poloclub.github.io/cnn-explainer/

AI/DL Certificates: No one cares about certificates in our business. Period. If you learn something while you get the certificate, sure. Get one print out and post it on your wall then. It does make an office less boring. AI/DL Degrees: If you do interesting projects, and you gain understanding and skills out of the projects, then an AI degree is good for you. Otherwise, no one cares about your degrees. What about a PhD or a master? That depends on the quality of your publications, see below. AI/DL Projects: If you actually do the projects, but not copying from "10-lines to do object detection" you see on-line, and you actually understand the principles of the techniques you are using, then those projects would be great experience for you. Otherwise, no one cares if you had done 1000 projects in your life. AI/DL Publications: If the writing process makes you think of a novel technique in the field, absolutely! Or you gain fundamental understanding of a class of algorithms when you research the background, sure that matters a lot! That's why graduates who has past publications are usually strong candidates for many big companies ML department. (Make sure you can answer questions about your own publication though!) AI/DL Blog Posts You Write: If the quality of the blog is like a paper, sure. But if you just copy from "10-lines to do object detection" , no one cares. Great Programming Skill: If that means you have source-level understanding of a major deep learning framework, wooooh, I would love to see your resume! What if you know 7 different ways to do string manipulation in python? That certainly is an interesting programming skill, but programming skill does not equal to ML skill. So again, no one cares. In a nutshell, what matters most is whether you genuinely understand the field. Credits: Arthur Chan

'How to Deploy Machine Learning Models', great article! https://christophergs.com/machine%20learning/2019/03/17/how-to-deploy-machine-learning-models/

📢 CNN resources! 👉🏼 Convolutional Neural Network Tutorial: From Basic to Advanced: here. 👉🏼 A Beginner's Guide To Understanding Convolutional Neural Networks: here. 👉🏼 An intuitive guide to Convolutional Neural Networks: here. Research Papers: 👉🏼 Gradient-Based Learning Applied to Document Recognition: here. 👉🏼 Backpropagation Applied to Handwritten Zip Code Recognition: here. 👉🏼 Convolutional Networks for Images, Speech, and Time-Series: here. Visualization: 👉🏼 Watch the full video: here.

Deep Learning with Pytorch by Prof.Yann LeCun (CNN Founder) This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. GitHub Link: https://atcold.github.io/pytorch-Deep-Learning/ YouTube Playlist: https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq

Python Data Science Handbook This one contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks. which is well structured and useful for learning Data Science. Beginners friendly https://jakevdp.github.io/PythonDataScienceHandbook/

Research Paper on NumPy🥳 https://www.nature.com/articles/s41586-020-2649-2

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