Introduction to batch normalization
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Batch normalization is a technique introduced in 2015 by Google researchers to address issues like internal covariate shift and vanishing gradients. It works by normalizing the inputs to each unit to have zero mean and unit variance based on the statistics of the mini-batch. This helps the network train deeper models with higher learning rates and be less sensitive to initialization. Batch normalization is applied before the activation function of each layer during both training and inference.
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Batch normalization presentation
The document summarizes the Batch Normalization technique presented in the paper "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift". Batch Normalization aims to address the issue of internal covariate shift in deep neural networks by normalizing layer inputs to have zero mean and unit variance. It works by computing normalization statistics for each mini-batch and applying them to the inputs. This helps in faster and more stable training of deep networks by reducing the distribution shift across layers. The paper presented ablation studies on MNIST and ImageNet datasets showing Batch Normalization improves training speed and accuracy compared to prior techniques.
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深度學習 (Deep Learning) 是近年來備受重視的機器學習方法，現今許多語音與影像辨識的研究與應用，都基於深度學習的技術來完成。深度學習的工具不斷推陳出新，若你已經聽過這項技術，卻不知道該從何開始下手，這堂手把手的實務課程將會很適合你。
本課程利用六個小時的時間，介紹 Keras 這個熱門的深度學習工具，從最簡單的前饋類神經網路 (Feedforward Neural Network) 開始，用 Keras 加入各種訓練技巧 (Regularization, Early Stopping, Dropout) 以得到好的預測模型。亦介紹深度學習模型的另一個變形：捲積式類神經網路 (Convolutional Neural Network, CNN)，以完整的實務操作，讓你邁出成為深度學習訓練大師的第一步。
Using Topological Data Analysis on your BigData
Synopsis:
Topological Data Analysis (TDA) is a framework for data analysis and machine learning and represents a breakthrough in how to effectively use geometric and topological information to solve 'Big Data' problems. TDA provides meaningful summaries (in a technical sense to be described) and insights into complex data problems. In this talk, Anthony will begin with an overview of TDA and describe the core algorithm that is utilized. This talk will include both the theory and real world problems that have been solved using TDA. After this talk, attendees will understand how the underlying TDA algorithm works and how it improves on existing “classical” data analysis techniques as well as how it provides a framework for many machine learning algorithms and tasks.
Speaker:
Anthony Bak, Senior Data Scientist, Ayasdi
Prior to coming to Ayasdi, Anthony was at Stanford University where he did a postdoc with Ayasdi co-founder Gunnar Carlsson, working on new methods and applications of Topological Data Analysis. He completed his Ph.D. work in algebraic geometry with applications to string theory at the University of Pennsylvania and ,along the way, he worked at the Max Planck Institute in Germany, Mount Holyoke College in Germany, and the American Institute of Mathematics in California.
Illustrative Introductory Neural Networks
An introductory/illustrative but precise slide on mathematics on neural networks (densely connected layers).
Please download it and see its animations with PowerPoint.
*This slide is not finished yet. If you like it, please give me some feedback to motivate me.
I made this slide as an intern in DATANOMIQ Gmbh
URL: https://www.datanomiq.de/
Getting Started with Machine Learning
We present basic concepts of machine learning such as: supervised and unsupervised learning, types of tasks, how some algorithms work, neural networks, deep learning concepts, how to apply it in your work.
gan.pdf
Generative Adversarial Networks (GANs) are a class of deep learning models that are trained using an adversarial process. GANs consist of two neural networks, a generator and a discriminator, that compete against each other. The generator tries to generate new samples from a latent space to fool the discriminator, while the discriminator tries to distinguish real samples from fake ones. GANs can learn complex high-dimensional distributions and have been applied to image generation, video generation, and other domains. However, training GANs is challenging due to issues like non-convergence and mode collapse. Recent work has explored techniques like minibatch discrimination, conditional GANs, and unrolled GANs to help address these training issues.
ML Study Jams - Session 3.pptx
This document summarizes a session on classification models. It discusses classifying data by learning classification rules from labeled input data. Specifically, it covers k-nearest neighbors classification, where the target point is assigned the majority class of its k nearest neighbors. It also discusses extending linear regression to classification by using it to generate real-valued outputs, then thresholding those values to classify instances into classes 0 or 1. However, it notes that logistic regression is a better approach, using a sigmoid function to convert real-valued outputs to a probability between 0 and 1 before thresholding for classification. The document highlights key considerations for classification like dealing with multiple classes and class imbalance. It also discusses evaluating classifiers, ensemble methods, and overfitting/under
04 numerical
The document discusses numerical concerns for implementing deep learning algorithms. It covers topics like:
1) Algorithms specified with real numbers but implemented with finite bits can lead to rounding errors and instability.
2) Gradient descent, curvature, and saddle points which are important for iterative optimization.
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InfoGAN and Generative Adversarial Networks
An introductory look at Generative Adversarial Networks generally and the InfoGAN implementation specifically.
Musings of kaggler
The document discusses the author's approach to a Kaggle competition by first preparing the data. Key steps included identifying outliers using median absolute deviation, using a Kolmogorov-Smirnov test to identify different distributions in the training data, and removing data that did not need to be modeled. The author then created various features from the data and experimented with different models before creating an ensemble of models. The ensemble approach combined predictions from random forests, gradient boosted models, support vector machines, and generalized linear models through a stacking regression. This process resulted in a top ranking of 49 out of 532 for the competition.
Intelligent Ruby + Machine Learning
This document discusses machine learning concepts including algorithms, data inputs/outputs, runtimes, and trends in academia vs industry. It notes that while academia focuses on algorithm complexity, industry prioritizes data-driven approaches using large datasets. Ensemble methods combining many simple models generally perform better than single complex models. Specific ML techniques discussed include word segmentation using n-gram probabilities, perceptrons for classification, SVD for recommendations and clustering, and crowdsourcing ensembles. The key lessons are that simple models with large data outperform complex models with less data, and that embracing many small independent models through ensembles is effective.
Deep Learning with Apache MXNet (September 2017)
The document provides an overview of deep learning with Apache MXNet. It discusses key concepts like neural networks, training processes, convolutional neural networks, and Apache MXNet. It also outlines examples of using MXNet, including building a first network, using pre-trained models, learning from scratch on datasets like MNIST, and fine-tuning models on CIFAR-10. The document concludes by mentioning an example of using MXNet with Keras and a quick reference to Sockeye.
Generative adversarial network_Ayadi_Alaeddine
Generative Adversarial Networks (GANs) are a type of deep learning model used to generate realistic samples. GANs work by having two neural networks, a generator and discriminator, compete against each other. The generator tries to generate samples that look real, while the discriminator tries to tell the difference between real and generated samples. Applications of GANs include generating photo-realistic images from text descriptions, retrieving similar images from historical archives, and assisting with drug discovery.
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20200723_insight_release_plan
The product release plan outlines the company's planned releases of hardware and software products over the next few quarters. It includes the scheduled release of new workstation hardware and software, edge hardware and software, AI edge devices, vision and IoT kits, tools, and new Alpha workstation models. The plan provides a quarterly roadmap for introducing new products from Q3 to Q2 the following year.
20190827_activity_diagram
This document discusses using an M48X MCU and Jetson Nano for a home automation project. The M48X MCU controls lights and other devices via UART commands from a Jetson Nano or PC. Example commands are given to turn lights on/off, change colors, and start automated light shows or routines. Additional commands allow controlling videos, music and messages played on the Jetson Nano through the M48X MCU.
20150722 - AGV
This document describes the design of an autonomous guided vehicle (AGV) system. It outlines the main components including the order system, dispatcher, universal clock, AGVs, broker, grid map database. It then describes the functions of various components like the naive dispatcher, path planner using A* algorithm, discrete time simulation with the universal clock, AGV main flow, grid map GUI, and traffic control strategies to handle conflicts between AGVs.
20161220 - microservice
The document discusses microservices and provides definitions, examples, and considerations around this architectural style. It defines microservices as small, independent processes communicating via APIs to compose complex applications. It notes microservices allow for increased modularity, independence, and scalability compared to traditional monolithic architectures. The document also shares perspectives from experts on microservices and examples of companies using this approach.
20160217 - Overview of Vortex Intelligent Data Sharing Platform
Vortex Intelligent is an intelligent data sharing platform that provides several product suites including Vortex OpenSplice, Vortex Starter Kit, and Vortex Device. Vortex OpenSplice is a DDS library that implements DDS 1.2 and DDSI 2.1 standards. It has both free and paid versions. Vortex Starter Kit and Vortex Device target different developer types. Additional products like Vortex Insight, Vortex Cafe, Vortex Web provide visualization, Java DDS library, and JavaScript DDS library capabilities. Vortex also includes options for cloud, fog, and gateway integration.
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This document discusses strategies for IoT development. It proposes three levels of architecture - short, mid, and long term - to develop an IoT kernel and services. It also examines Advantech's IoT products and architectures, including their WebAccess SCADA system and WISE-Cloud platform. Challenges in IoT solutions are identified around connectivity, costs, reliability and time to market. Two proof-of-concept projects are proposed using edge cloud services and Modbus TCP.
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This document discusses microservices architecture and how Docker has changed application development. It covers:
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When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
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The binding of cosmological structures by massless topological defects
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
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在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样
学校原件一模一样【微信：741003700 】《(salfor毕业证书)索尔福德大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Authoring a personal GPT for your research and practice: How we created the Q...
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
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Introduction to batch normalization
- 2. Batch Normalization Distribution changes Higher learning rate Less care about initialization 2015, ICML, Google Researchers Mini-Batch
- 3. Recap When training neural networks we'll feed in observations and compare the expected output to the true output of the network. We'll then use gradient descent to update the parameters of the model in the direction which will minimize the difference between our expected (or ideal) outcome and the true outcome. In other words, we're attempting to minimize the error we observe in our model's predictions. Ultimately, gradient descent is a search among a loss function surface in an attempt to find the values for each parameter such that the loss function is minimized. In other words, we're looking for the lowest value on the loss function surface. 3 Source: jeremyjordan
- 4. Recap height age NBA? age height Contour Plot (Loss function) 4
- 7. Learning Rate Matters 7 Source: jeremyjordan
- 8. Why Feature Scaling Matter Player Age Height NBA #1 45 190 No #2 19 192 Yes #3 32 195 No #4 48 200 No #5 25 193 Yes #6 31 182 Yes #7 49 177 No #8 22 201 Yes 8
- 9. Feature Scaling (Normalization) 9 age Contour Plot (Loss function) age height Contour Plot (Loss function) height
- 10. 10 Z-Score Normalization Min-Max Normalization Source: Codecademy
- 12. 12 平均數 (Mean)
- 14. Batch, Stochastic & Mini Batch Gradient Descent 14 (Batch) Gradient Descent Stochastic Gradient Descent Mini Batch Gradient Descent (Batch = 4)
- 15. Vanishing gradient Problem 15 Source: Andre Ye
- 16. Batch Normalization Distribution changes Higher learning rate Less care about initialization 2015, ICML, Google Researchers Mini-Batch
- 17. 17 直覺 ★ 既然 feature scaling 對於 input layer 有幫助，那 hidden layer 應該也有幫助 ★ 如果你把麥克風放大器的旋鈕轉到接近0，別人就聽不到你的聲音，但如果你 把它轉到接近最大聲，你的聲音就會飽和。 ★ 現在想像一下你將這種放大器串接起來，你必須正確的設定它們，才能讓聲音 在串接的末端既響亮又清晰。你的聲音從各個放大器出來時的振幅必須和它進 入該放大器時一樣。
- 18. BN Layer c b a output Y w1 w2 w3 x = aw1 +bw2 +cw3 f(x) 18 Linear Combination Activation function
- 19. 19
- 20. 20 Before/After Activation Function Input Output Source: 莫煩
- 21. 21 Back to Paper μ and σ2 are calculated on a per-batch basis while γ and β are learned parameters used across all batches.
- 23. 23 Stable Training 2018, NIPS, MIT Internal Covariate Shift Smooth Landscape
- 24. 24 Source: How Does Batch Normalization Help Optimization?
- 25. Pros and Cons Pros ➔ Allow sub-optimal starts (weight initialization) ➔ Speed up training (Larger learning rate) ➔ Solve vanishing gradient problem ➔ Acts a regularizer (Introduce randomness) Cons ➔ Small batch size leads to unstable mean and variance (Batch >= 32) ➔ Not for RNN (Recurrent neural networks) 25
- 26. Further Reading - 什麼是 Batch Normalization 批標準化 (Youtube 5:08) - Batch Normalization - Explained (Youtube 8:48) - Batch Normalization 介紹 - (Medium in Mandarin) - Normalizing your data (specifically, input and batch normalization) 26