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Neural Network

lab

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Technique Supports

Report an issue

Create an issue in the Issues block. We will fix it as soon as possible.

Require a feature

Create an issue in the Issues block. We will evaluate it, if accepted, it will be implemented in the future version.

Opinions & Discussions

Create a new discussion or join a discussion in the Discussions block.

More info

Find more info in the Wiki block.

Features

Learning and using neural networks in an intuitive and natural way!

Playgound: Lab & Missions

lab mission

Visualized Neural Network Lab. Open the black box of neural networks with simplified datasets and full visualization, gain more intuition.

Learning with mission like gaming. In the missions, you will run into many key concepts in neural networks, complete the challenges and acquire a deep understanding.

Put neural network lab in your pocket. Variety of datasets: 2D and 3D datasets both with regression and classification. Get the dataset just with a tapping. Do experiments at anytime anywhere.

Tutorials

tutorial tutorial

Intuitive and interactive tutorials. A large number of charts and interactive components are used to make learning more intuitive.

Charts & Animations

chart conv2d

Well visualized neural network knowledges. Key concepts and dynamics are showed with charts and animations. Activations, regularizations, loss functions, classifications, Embedding, CNN1d, CNN2d, RNN and more to come.

Models & Editor

models inception textcnn python

Visualized Deep Learning Models. Learn the most classic models in an intuitive way that has never been seen before, and quickly master cutting-edge technology. Through modular diagrams, understand how data changes from input to output and how it changes at each step. There are detailed documents for each module, and even reference papers, source codes and animations.

Visual neural network model editor. Quickly build a model by dragging your fingers. What you see is what you get: Through real-time calculation and display of the output of each step, the construction of the model has never been so intuitive and efficient; real-time display of various errors, including parameters/input types and value compatibility, etc., abandoning inefficient debugging.

About

Technique supports and discussions for the Neural Network app.

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