Neural network
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This document provides an introduction to neural networks. It discusses key concepts like activation functions, network classifications like feedforward and feedback networks, and training processes like forward and backward propagation. It also outlines some advantages of neural networks like their ability to model complex patterns, handle noisy data, and benefit from parallel processing. Disadvantages include the large amounts of data and computational power required. The document concludes by mentioning some recent achievements in applications like AlphaGo Zero and word embeddings.
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From neural networks to deep learning
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Deep LearningフレームワークChainerと最近の技術動向
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The Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intelligence)
The Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intelligence)Universitat Politècnica de Catalunya
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.Deep Learning & Tensor flow: An Intro
A presentation I created for an interview. Gives a high level over view of Deep learning concepts from the ground up.
Deep Learning and Automatic Differentiation from Theano to PyTorch
Inquisitive minds want to know what causes the universe to expand, how M-theory binds the smallest of the small particles or how social dynamics can lead to revolutions. In recent centuries, developments in science and technology brought us closer to explore the expanding universe, discover unknown particles like bosons or find out how and why a society interacts and reacts. To explain the fascinating phenomena of nature, Natural scientists develop complex 'mechanistic models' of deterministic or stochastic nature. But the hard question is how to choose the best model for our data or how to calibrate the model given the data.
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Watch the video: https://wp.me/p3RLHQ-hQQ
Learn more: https://github.com/probprog/CSCS-summer-school-2017
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
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Neural network
- 1. NEURAL NETWORKS Name - Shivam Malviya Roll No. - 18EC01044
- 2. Table Of Content ● Introduction ● Activation Functions ● Classifications ● Training ● Advantages ● Disadvantages ● Achievements ● Conclusion ● References
- 3. Inspiration In 1943, Warren McCulloch, a neurophysiologist, and a young mathematician, Walter Pitts, wrote a paper on how neurons might work. The first artificial neural network was invented in 1958 by psychologist Frank Rosenblatt, it is called Perceptron.
- 5. Neuron x1 x2 xn w1 w2 wn a = activation(z) z = w0 + w1 x1 + w2 x2 + …… + wn xn = wT x w0 x0 = 1
- 6. x w a = σ(z) {x Linear z = wT x Non - Linear Mapping a = σ(z)Mapping Neuron
- 8. Activation Functions ReLU - Rectified Linear Unit Leaky ReLU
- 10. Classification Based on connection patterns Feedforward NN Feedback NN
- 11. Training Loss Function - The output of loss function tells us how well our neural network model the given dataset. Loss Function in Regression x y L = (errors)2 ∑ m m = No. of data points
- 12. Training Loss Function in Classification L = - (ylog(p)+(1−y)log(1−p)) m m = No. of data points x1 x2 ∑ y = 1 y = 0
- 13. Training Forward Propagation Direction of flow of calculation W[1] W[2] W[3] a[L] = Activation of L - layer a[0] a[1] a[2] a[3]
- 14. Training Backward Propagation Direction of flow of calculation W[1] W[2] W[3] ∂L ∂a[3] ∂L ∂W[3] ∂L ∂a[2] ∂L ∂W[1] ∂L ∂a[1] ∂L ∂W[2]
- 15. Advantages Deep Neural Network Shallow Neural Network Medium Neural Network Traditional Machine Learning Algorithm Data Performance ● It can capture very complex patterns ● Universal Approximation Theorem
- 17. Advantages ● High tolerance to noisy data ● Ability to work with incomplete knowledge
- 18. ● Requires a lot of data Disadvantages
- 19. ● Require a huge computational power Disadvantages ● Takes a lot of time to train GPU
- 20. ● Unexplained behaviour of a neural network Disadvantages
- 21. Applications ● Deepmind’s AlphaGo Zero ➢ No. of atoms in the observable universe = 1080 ➢ No. of board positions in the chess = 10120 ➢ No. of board positions in the go = 10170
- 24. Conclusion ● Neural Networks have ability to perform tasks at which humans are good and computers are bad. ● Other algorithms may perform better in easy tasks.
- 25. References ● Image Colorization with Deep Convolutional Neural Networks By : Jeff Hwang and You Zhou ● Graph illustrating the impact of data available on performance of traditional machine learning algorithms. - Research Gate. By : Benoit Gallix ● Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. By : David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis ● Efficient Estimation of Word Representations in Vector Space. By : Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
- 26. THANK YOU