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This document proposes automating the evolution of neural networks through genetic algorithms. It discusses defining nodes and clusters, connecting them with weights that could operate according to quantum principles, and using genetic algorithms to generate new network architectures by combining codes from previous generations. The goal is to evolve increasingly intelligent networks, where problems could be generated by other networks through adversarial training without human input. Key challenges include defining what makes a network "intellectual" and appropriate nodes and architectures for automated evolution. Coding frameworks like Chainer and TensorFlow could support implementing and testing this approach.
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What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
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Neural network
1. Neural networks are inspired by biological neural networks in the brain and are made up of simple processing units called neurons.
2. Artificial neural networks use a layer of input neurons that receive information and pass it through connections to other neurons.
3. A neural network learns through a process of trial and error adjustment of the weights between neurons to minimize errors between the network's output and the desired output.
what is neural network....???
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The presentation gives a brief introduction to artificial neural networks, their types, their characteristics and properties, their advantages and disadvantages, their architectures, different activation functions, popular learning approaches, the gradient descent algorithm, the back propagation algorithm and the application domains.
Neural network
I think this could be useful for those who works in the field of Coputational Intelligence. Give your valuable reviews so that I can progree in my research
Neural network
The document discusses neural networks, including human neural networks and artificial neural networks (ANNs). It provides details on the key components of ANNs, such as the perceptron and backpropagation algorithm. ANNs are inspired by biological neural systems and are used for applications like pattern recognition, time series prediction, and control systems. The document also outlines some current uses of neural networks in areas like signal processing, anomaly detection, and soft sensors.
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This document provides information about the CS407 Neural Computation course. It outlines the lecturer, timetable, assessment, textbook recommendations, and covers topics from today's lecture including an introduction to neural networks, their inspiration from the brain, a brief history, applications, and an overview of topics to be covered in the course.
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- Common neural network types include perceptrons, linear networks, backpropagation networks and self-organizing maps.
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Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
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Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
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Compound A
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Compound B
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Compound C
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Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
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◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理美国佩斯大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理美国佩斯大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理美国佩斯大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理美国佩斯大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Embracing Deep Variability For Reproducibility and Replicability
Embracing Deep Variability For Reproducibility and ReplicabilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
Embracing Deep Variability For Reproducibility and Replicability
Abstract: Reproducibility (aka determinism in some cases) constitutes a fundamental aspect in various fields of computer science, such as floating-point computations in numerical analysis and simulation, concurrency models in parallelism, reproducible builds for third parties integration and packaging, and containerization for execution environments. These concepts, while pervasive across diverse concerns, often exhibit intricate inter-dependencies, making it challenging to achieve a comprehensive understanding. In this short and vision paper we delve into the application of software engineering techniques, specifically variability management, to systematically identify and explicit points of variability that may give rise to reproducibility issues (eg language, libraries, compiler, virtual machine, OS, environment variables, etc). The primary objectives are: i) gaining insights into the variability layers and their possible interactions, ii) capturing and documenting configurations for the sake of reproducibility, and iii) exploring diverse configurations to replicate, and hence validate and ensure the robustness of results. By adopting these methodologies, we aim to address the complexities associated with reproducibility and replicability in modern software systems and environments, facilitating a more comprehensive and nuanced perspective on these critical aspects.
https://hal.science/hal-04582287
Anti-Universe And Emergent Gravity and the Dark Universe
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
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Singularity
- 1. <Singularity> Automated Evolution of Neural Network By FreedTime235 Like a Rieman coordinates
- 2. To go over the Singularity All we need is to produce A.I automatically
- 3. It’s Difficult to automate it fully but We are Possible to Automate the neural network Evolution
- 4. Simply put, All we need is 1. Set What the node and the cluster is (Relu, softmax, memory like attention model… ) 2. How to evolve them (Genetic Algorithm, Monte Carlo… )
- 5. Neural Network evolutes in an analog way 199x – sparse coding Definition to automate the evolution 2006 – dropout 2012 – G.Hinton outperformed in image recognition 2014- batch norm something like that
- 6. 1. Produce Network by Genetic Algorithm (Like animal) 2 ideas to automate the evolution 2. Produce Neural network by Neural network
- 7. (1). What is the definition to be “intellectual” for the network? -how to give problem and answer to train- 3 difficulties for evolution of Neural N (2). How to evolve neural network? (3). How to define the appropriate node?
- 8. To be “intellectual” is to Recognize (1).“intellectual” for the neural network If you recognize, you know what to do, so Action does not matter (controversial)
- 9. (1).“intellectual” for the neural network How to Give training problems to the network? 1.Human makes it 2.Network makes it
- 10. (1).“intellectual” for the neural network We can make difficult problem as possible as we can 1.Human makes it And set them to network to solve (Level 1, level 2, level 3, level 4, level 5 …) Is this cat? Is this dog? Is this like Gogh? Like picaso?
- 11. (1).“intellectual” for the neural network 2.Network makes it Like adversarial networks Environment network(Problem makes) VS! Training network Problem should not be something to be understood by human, but it means it takes human for recognizing which is “intellectual” network
- 12. (1).“intellectual” for the neural network 2.Network makes it Like adversarial networks Network understands network Which human can not understand Any abstract problem is acceptable
- 13. 2.“evolve” neural network Genetic Algorithm is one of the solution (controversial)
- 14. 2.“evolve” neural network Child 1 - 001100 Child 2 - 010110 Child new - 010010 Genetic algorithm just define new generation by mixing its codes
- 15. 2.“evolve” neural network This idea is effective like a way that I show later in “technical quest”
- 16. 3.How to define a “node” <Node – activation> Relu softmax YES, non linear! Old school but, Rational.. maybe
- 17. 3.How to define a “node” <Node – activation> What about the Quantum one?
- 18. 3.How to define a “node” <Quantum activation> Hemeroff said there’s a quantum vibration inside the microtube in the cell.
- 19. 3.How to define a “node” <Quantum activation> Quantum = you know all patterns in which what happens next
- 20. Technical Quest
- 21. How can we code automated neural evolution? Coding
- 22. Coding Framework Chainer Tensorflow Beautiful Coding Suitable like this heavily calculation project Easy coding, easy to find bug, fix
- 23. Coding 2 elements Nodes and Cluster Like an activation but we need something like memory like neural turing machine Weight How to connect nodes and cluster is important
- 24. Element (1) Nodes and Cluster Nodes can be Activation function like we’ve seen Cluster is something like a group of nodes in Memory nets or neural turing machine MATRIX MATRIX weights output
- 25. Element (2) Weights How to connect, can be also quantumed
- 26. Element (2) Weights Accoding to hameroff and recent experiments, its like quentumed in microtubed in our cell
- 27. What I wanna say Like “Scientists” I don’t write thesis differential geometory, especially maniholds, how the machine learning proceeds mathematically, BUT it does not matter to explain how evolve. So I wrote it in a simple way. TY. I surely have more complex knowledges of
- 28. What I wanna say The neural nets can be evolved faster Mathematics does not improve rapidly We need freed ideas Like Einstein ... Rieman… Amari…
- 30. Appendix Google inception could be found in my method I’ve shown
- 31. Appendix Cause neural network we found, so far, is just some specific pattern of nodes and cluster.
- 32. Appendix REASONING can be found lator
- 33. THANK YOU For READING