Notebook Master for Jupyter vs NeuroVis

We believe the best way to learn AI/Deep Learning/Machine learning and Data Science is to enable them to access the reproducible codes on Jupyter. Notebook lets you explore million notebooks on Github/Kaggle and more. * Wonder how to implement new state of the art papers? * How to win a Data Science competition * How to start learning Deep Learning and Machine Learning. You can explore, search, store offline to read and learn your favorite notebooks any time, everywhere, on the go. Thanks to your feedback, we add new ad free subscription version by month/year. ** Functions ** - Open with colab from github - Explore top Jupyter notebook repositories - Search the repositories, users - Import your notebooks from cloud - View and bookmark your most valuable notebooks. - Offline view mode - Beautiful rendering maths in notebook. ** Ad free subscription ** • Length of subscription: monthly/yearly • Payment will be charged to iTunes Account at confirmation of purchase • Subscription automatically renews unless auto-renew is turned off at least 24-hours before the end of the current period • Account will be charged for renewal within 24-hours prior to the end of the current period, and identify the cost of the renewal • Subscriptions may be managed by the user and auto-renewal may be turned off by going to the user’s Account Settings after purchase Enjoy notebooks and happy modeling. Contact us: saigonapps@gmail.com Term of Service and Privacy policy: http://appsaigon.com/privacy.html

The hardest part of learning CNNs isn't the math — it's not being able to picture what's actually happening. What exactly happens when the kernel slides across? Why does the feature map look like that? Why does adding one more layer help? Textbooks answer with symbols. NeuroVis shows you. Draw a digit with your finger, and a real CNN classifies it layer by layer in front of you — input tensor, convolution feature maps, activation, max pooling, and the softmax probability for each of the ten digits. Every stroke recomputes the entire chain instantly. Tap any feature map and the kernel sweeps across the input cell by cell, so you can see exactly where each output number comes from. Step through it, pause it, or just read the values off the grid. Wondering what those numbers inside the kernel actually do? Change one and watch the feature map follow. Wondering whether another layer matters? Add it and find out on the spot. And at the end, it tells you why it picked that digit and not another one — which strokes count as evidence, and which argue against it. This isn't a canned animation. It's a real model running on your phone. Fully offline, no data collected. For: students learning deep learning, engineers building intuition, and anyone who has read the formulas but still has no mental picture.