PPT-Recurrent Neural Networks

循环神经网络 Neural Networks Recurrent Neural Networks Humans dont start their thinking from scratch every second As you read this essay you understand each word based on your understanding of previous words You dont throw everything away and start thinking from scratch again Your thoughts have persistence. 1. Recurrent Networks. Some problems require previous history/context in order to be able to give proper output (speech recognition, stock forecasting, target tracking, etc.. One way to do that is to just provide all the necessary context in one "snap-shot" and use standard learning. 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. Machine . Learning. 1. Last Time. Perceptrons. Perceptron. Loss vs. Logistic Regression Loss. Training . Perceptrons. and Logistic Regression Models using Gradient Descent. 2. Today. Multilayer Neural Networks. 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.  .  .  . 1. Table of contents. Recurrent models. Partially recurrent neural networks. . Elman networks. Jordan networks. Recurrent neural networks. BackPropagation Through Time. Dynamics of a neuron with feedback. Fall 2018/19. 7. Recurrent Neural Networks. (Some figures adapted from . NNDL book. ). Recurrent Neural Networks. Noriko Tomuro. 2. Recurrent Neural Networks (RNNs). RNN Training. Loss Minimization. Bidirectional RNNs. 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. Applications. Lectures . 14-15: . CNN. . and . RNN Details. Zhu Han. University of Houston. Thanks . Xusheng. Du and Kevin Tsai For Slide Preparation. 1. CNN outline. The convolution operation. Motivation. Dr David Wong. (With thanks to Dr Gari Clifford, G.I.T). The Multi-Layer Perceptron. single layer can only deal with linearly separable data. Composed of many connected neurons . Three general layers; . 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. Introduction. Dynamic networks. are networks . that. contain . delays. (or . integrators, for continuous-time networks. ) and that . operate on a sequence of inputs. . . In other words, . the ordering of the inputs is important. Models and applications. Outline. Sequence Data. Recurrent Neural Networks Variants. Handling Long Term Dependencies. Attention Mechanisms. Properties of RNNs. Applications of RNNs. Hands-on LSTM-supported timeseries prediction.