PPT-Training Neural networks to play checkers

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. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. 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. of Poker AI. Christopher Kramer. Outline of Information. The Challenge. Application, problem to be solved, motivation. Why create a poker machine with ANNE?. The Flop. The hypothesis. Can a Poker AI run using only an ANNE?. Abhishek Narwekar, Anusri Pampari. CS 598: Deep Learning and Recognition, Fall 2016. Lecture Outline. Introduction. Learning Long Term Dependencies. Regularization. Visualization for RNNs. Section 1: Introduction. Nitish Gupta, Shreya Rajpal. 25. th. April, 2017. 1. Story Comprehension. 2. Joe went to the kitchen. Fred went to the kitchen. Joe picked up the milk. Joe travelled to his office. Joe left the milk. Joe went to the bathroom. . Content. History of backgammon. Introduction of the backgammon. Introduction. How to Play. Irregularities and Agreement. History of Backgammon. Backgammon is one of the oldest games in existence, alongside Go and Chess. . Dongwoo Lee. University of Illinois at Chicago . CSUN (Complex and Sustainable Urban Networks Laboratory). Contents. Concept. Data . Methodologies. Analytical Process. Results. Limitations and Conclusion. Rohit. Ray. ESE 251. What are Artificial Neural Networks?. ANN are inspired by models of the biological nervous systems such as the brain. Novel structure by which to process information. Number of highly interconnected processing elements (neurons) working in unison to solve specific problems.. . Rekabdar. Biological Neuron:. The Elementary Processing Unit of the Brain. Biological Neuron:. A Generic Structure. Dendrite. Soma. Synapse. Axon. Axon Terminal. Biological Neuron – Computational Intelligence Approach:. 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. Developing efficient deep neural networks. Forrest Iandola. 1. , Albert Shaw. 2. , Ravi Krishna. 3. , Kurt Keutzer. 4. 1. UC Berkeley → DeepScale → Tesla → Independent Researcher. 2. Georgia Tech → DeepScale → Tesla. 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.