Image Compression Using Neural Network
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An artificial neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships.
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This document provides an overview of lesson 2 of a machine learning course using Python. It discusses neural networks and their biological inspiration. It then explains how artificial neural networks work, including the basic neuron structure and how signals are received and transmitted. Finally, it introduces implementing simple neural networks in Python using NumPy for efficient data structures.International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Convolutional Neural Networks - Veronica Vilaplana - UPC Barcelona 2018
Convolutional Neural Networks - Veronica Vilaplana - UPC Barcelona 2018Universitat Politècnica de Catalunya
https://telecombcn-dl.github.io/2018-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.Artificial neural network
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
Deep Learning behind Prisma
Prisma uses deep learning techniques like neural style transfer to transform photos into artworks. Neural style transfer uses convolutional neural networks to extract features from content and style images, then finds an image that minimizes differences in these features. Early work used iterative optimization, but real-time style transfer trains a generative CNN on a dataset to synthesize stylized images with one forward pass. Prisma's offline mode likely uses a similar generative approach to enable fast stylization on mobile.
Deep Learning
This presentation is Part 2 of my September Lisp NYC presentation on Reinforcement Learning and Artificial Neural Nets. We will continue from where we left off by covering Convolutional Neural Nets (CNN) and Recurrent Neural Nets (RNN) in depth.
Time permitting I also plan on having a few slides on each of the following topics:
1. Generative Adversarial Networks (GANs)
2. Differentiable Neural Computers (DNCs)
3. Deep Reinforcement Learning (DRL)
Some code examples will be provided in Clojure.
After a very brief recap of Part 1 (ANN & RL), we will jump right into CNN and their appropriateness for image recognition. We will start by covering the convolution operator. We will then explain feature maps and pooling operations and then explain the LeNet 5 architecture. The MNIST data will be used to illustrate a fully functioning CNN.
Next we cover Recurrent Neural Nets in depth and describe how they have been used in Natural Language Processing. We will explain why gated networks and LSTM are used in practice.
Please note that some exposure or familiarity with Gradient Descent and Backpropagation will be assumed. These are covered in the first part of the talk for which both video and slides are available online.
A lot of material will be drawn from the new Deep Learning book by Goodfellow & Bengio as well as Michael Nielsen's online book on Neural Networks and Deep Learning as well several other online resources.
Bio
Pierre de Lacaze has over 20 years industry experience with AI and Lisp based technologies. He holds a Bachelor of Science in Applied Mathematics and a Master’s Degree in Computer Science.
https://www.linkedin.com/in/pierre-de-lacaze-b11026b/
Build a simple image recognition system with tensor flow
A perfect working model to detect mnist dataset using TensorFlow.
Dataset:
http://yann.lecun.com/exdb/mnist/
For code check the below GitHub links:
https://github.com/Jitudebz/psychic-pancake
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- 1. Topic : Image Compression Using Neural Network Submitted By :- Omkar Lokhande (A-68)
- 2. Content • Introduction to the Neural Network • Neural Network Structure • Neural Network Structure • Activation Function • Functions of Neural Network • Image Compression using BP Neural Network • Output of this Compression Algorithm • Other Neural Network Techniques • References
- 3. Introduction to the Neural Network • An artificial neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. • Can perform "intelligent" tasks similar to those performed by the human brain.
- 4. Neural Network Structure • A neural network is an interconnected group of neurons A Simple Neural Network
- 5. Neural Network Structure An Artificial Neuron
- 6. Activation Function Depending upon the problem variety of Activation function is used: Linear Activation function like step function Nonlinear Activation function like sigmoid function
- 7. Functions of Neural Network • Compute a known function • Approximate an unknown function • Pattern Recognition • Signal Processing • Learn to do any of the above
- 8. Image Compression using BP Neural Network [1] • Future of Image Coding(analogous to our visual system) • Narrow Channel • K-L transform • The entropy coding of the state vector hi’s at the hidden layer.
- 9. Image Compression [2] • A set of image samples is used to train the network. • This is equivalent to compressing the input into the narrow channel and then reconstructing the input from the hidden layer.
- 10. Image Compression [3] • Transform coding with multilayer Neural Network: The image to be subdivided into non- overlapping blocks of n x n pixels each. Such block represents N-dimensional vector x, N = n x n, in N-dimensional space. Transformation process maps this set of vectors into y=W (input) output=W-1y
- 11. Image Compression [4] The inverse transformation need to reconstruct original image with minimum of distortions.
- 12. Output of this Compression Algorithm
- 13. Other Neural Network Techniques • Hierarchical back-propagation neural network • Predictive Coding • Depending upon weight function we have • Hebbian learning-based image compression Wi (t + 1)= {W(t) + αhi(t)X(t)}/||Wi (t) + αhi(t)X(t)||
- 14. References • Neural networks Wikipedia (http://en.wikipedia.org/wiki/Neural_network) • Ivan Vilovic' : An Experience in Image Compression Using Neural Networks • Robert D. Dony, Simon Haykin: Neural Network Approaches to Image Compression • Constantino Carlos Reyes-Aldasoro, Ana Laura Aldeco: Image Segmentation and compression using Neural Networks • Image compression with neural networks - A survey --J. Jiang*
- 15. Thank You !