Computational Complexity vs Neural Network

This app demonstrates the basic principles of computational complexity. The growth of a few typical Big-O functions are illustrated with an interactive graph, and live demonstrations of sorting algorithms can be run using randomly generated data. This app was developed at SUNY Binghamton, by Prof. Patrick H. Madden. Version 1.1 is an expansion on this, with the assistance a few students (primarily Keith Gardner, and undergrad, and Anthony Canino, an MS grad). We're working on the app as a fun project, and as a way to introduce ACM student group members to iOS programming. The source for the app is available on Github; we'll be adding more algorithms and things, and if you're interested in helping out with the development, let us know! https://github.com/keithgardner2/complexity Yay computational complexity! Yay Hartmanis and Stearns!

Learning and using neural networks in an intuitive and natural way! Intuitive and interactive tutorials. A large number of charts and interactive components are used to make learning more intuitive. 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. Good 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. 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. MORE FEATURES COMING!!!