PPT-Neural Network Training:

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 Minibatch SGD Learning rate decay Adaptive methods. associationism. Associationism. David Hume (1711-1776) was one of the first philosophers to develop a detailed theory of mental processes.. Associationism. “There . is a secret tie or union among particular ideas, which causes the mind to conjoin them more frequently together, and makes the one, upon its appearance, introduce the . 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. dendrites. cell body. axon. signal. direction. colaterals. synapse. . . .. Biological Neural Networks. electrical. signal. electrical. signal. synaptic. gap. neurotransmitters. dendrite. vesicles. presynaptic. 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. Dr David Wong. (with thanks to Dr . Gari. Clifford, G.I.T). Overview. What are Artificial Neural Networks (ANNs)?. How do you construct them?. Choosing architecture. Pruning. How do you train them?.