PPT-Training a Neural Network to Recognize Phage Major Capsid Proteins

Author Michael Arnoult San Diego State University Mentors Victor Seguritan Anca Segall and Peter Salamon Department of Biology San Diego State University Methods Bacteriophages are the single most abundant biological entity on earth and influence every environment in which bacteria exist There are no current algorithms which reliably analyze phage structural protein sequences and predict their function . 9/30/2010. The role of Artificial Neural Networks in Phage Research . What is an Artificial Neural Network?. Mathematical and computational model. Motivated by biological neurons. Trained by using features to learn patterns and commonalities. HIV infected T-cell. Viral Structure. not . cells. small . infectious particles . w/. DNA or RNA enclosed . in a protein coat . (capsid). in . some cases. , a membranous envelope. Viral Genomes. genomes . CAP5615 Intro. to Neural Networks. Xingquan (Hill) Zhu. Outline. Multi-layer Neural Networks. Feedforward Neural Networks. FF NN model. Backpropogation (BP) Algorithm. BP rules derivation. Practical Issues of FFNN. Table of Contents. Part 1: The Motivation and History of Neural Networks. Part 2: Components of Artificial Neural Networks. Part 3: Particular Types of Neural Network Architectures. Part 4: Fundamentals on Learning and Training Samples. Week 5. Applications. Predict the taste of Coors beer as a function of its chemical composition. What are Artificial Neural Networks? . Artificial Intelligence (AI) Technique. Artificial . Neural Networks. Ashutosh. Pandey and . Shashank. . S. rikant. Layout of talk. Classification problem. Idea of gradient descent . Neural network architecture. Learning a function using neural network. Backpropagation algorithm. By, . . Sruthi. . Moola. Convolution. . Convolution is a common image processing technique that changes the intensities of a pixel to reflect the intensities of the surrounding pixels. A common use of convolution is to create image filters. Perceptron. x. 1. x. 2. x. D. w. 1. w. 2. w. 3. x. 3. w. D. Input. Weights. .. .. .. Output:. . sgn. (. w. x. . b). Can incorporate bias as component of the weight vector by always including a feature with value set to 1. The Gory Details. (Or, how to be a helicopter parent to a neural network). (Or, why AI is not about to be solved any time soon). Outline. Optimization. Mini-batch SGD. Learning rate decay. Adaptive methods. Daniel Boonzaaier. Supervisor – Adiel Ismail. April 2017. Content. Project Overview. Checkers – the board game. Background on Neural Networks. Neural Network applied to Checkers. Requirements. Project Plan. Dr. Abdul Basit. Lecture No. 1. Course . Contents. Introduction and Review. Learning Processes. Single & Multi-layer . Perceptrons. Radial Basis Function Networks. Support Vector and Committee Machines. Goals for this Unit. Basic. understanding of Neural Networks and how they work. Ability to use Neural Networks to solve real problems. Understand when neural networks may be most appropriate. Understand the strengths and weaknesses of neural network models. Roi. . Livni. , Shai . Shalev-Shwartz. . Ohad. Shamir. Remainder on neural networks. Neural network = A direct graph (usually acyclic) where each vertex corresponds to a neuron.. A Neuron = A weighted sum of its predecessor neurons + activation function . Learn to build neural network from scratch.. Focus on multi-level feedforward neural networks (multi-level . perceptrons. ). Training large neural networks is one of the most important workload in large scale parallel and distributed systems.