PPT-Training Neural Networks

Part 1 About me Or Nachmias No previous experience in neural networks Responsible to show the 2 nd most important lecture in the seminar References Stanford CS231 Convolution Neural Networks for Visual Recognition . Cost function. Machine Learning. Neural Network (Classification). Binary classification. . . 1 output unit. Layer 1. Layer 2. Layer 3. Layer 4. Multi-class classification . (K classes). K output units. 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. Recurrent Neural Network Cell. Recurrent Neural Networks (unenrolled). LSTMs, Bi-LSTMs, Stacked Bi-LSTMs. Today. Recurrent Neural Network Cell.  .  .  .  . Recurrent Neural Network Cell.  .  .  . 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. 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. Dongwoo Lee. University of Illinois at Chicago . CSUN (Complex and Sustainable Urban Networks Laboratory). Contents. Concept. Data . Methodologies. Analytical Process. Results. Limitations and Conclusion. Introduction 2. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Hinton’s Brief History of Machine Learning. What was hot in 1987?. Introduction to Back Propagation Neural . Networks BPNN. By KH Wong. Neural Networks Ch9. , ver. 8d. 1. Introduction. Neural Network research is are very . hot. . A high performance Classifier (multi-class). . 循环神经网络. Neural Networks. Recurrent Neural Networks. Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.. 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. Patrick . Siarry. ,. Ph.D., . Editor-in-chief. Patrick . Siarry. was born in France in 1952. He received the PhD degree from the University Paris 6, in 1986 and the Doctorate of Sciences (. Habilitation. 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.