In Deep Learning, learning RBM is basic hierarchical components of the layer. In this slide, we can learn basic components of RBM (bipartite graph, Gibbs Sampling, Contrastive Divergence (1-CD), Energy function of entropy).
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Article overview: Unsupervised Learning of Visual Structure Using Predictive ...Ilya Kuzovkin
This set of slides goes over the recent article that tries to tie together the idea of predictive coding and deep learning. The main point of the article is that a generative system trained on sequential data to predict the future samples learns more "useful" representation than the usual autoencoder. The result resonates with the fact that our brain is probably using predictive mechanisms.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
Continues with Excel basics giving information on cell addressing styles and worksheet functions and their nesting. Also gives an example of precision setting
In the past 5 years "Deep Learning" has taken the tech world by storm. Not only has it achieved groundbreaking results in many academic disciplines, it is now used extensively in practical applications for everything from face detection on Facebook to real-time language translation on Skype to identifying drug targets at pharmaceutical companies. This presentation will give an overview of what deep learning is, how it works, how it's being used, and how to get started using it in your own applications.
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Article overview: Unsupervised Learning of Visual Structure Using Predictive ...Ilya Kuzovkin
This set of slides goes over the recent article that tries to tie together the idea of predictive coding and deep learning. The main point of the article is that a generative system trained on sequential data to predict the future samples learns more "useful" representation than the usual autoencoder. The result resonates with the fact that our brain is probably using predictive mechanisms.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
Continues with Excel basics giving information on cell addressing styles and worksheet functions and their nesting. Also gives an example of precision setting
In the past 5 years "Deep Learning" has taken the tech world by storm. Not only has it achieved groundbreaking results in many academic disciplines, it is now used extensively in practical applications for everything from face detection on Facebook to real-time language translation on Skype to identifying drug targets at pharmaceutical companies. This presentation will give an overview of what deep learning is, how it works, how it's being used, and how to get started using it in your own applications.
Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hier...Mad Scientists
Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations, Honglak Lee(ICML 2009)
석사과정 세미나 발표를 위해 논문을 읽고 분석한 내용입니다. CDBN은 CNN와 DBN의 장점을 결합하여 translation invariance와 computational competence를 확보하였고, probabilistic max-pooling을 통해 image restoration을 할 수 있는 undirected DBM을 구성할 수 있게 합니다.
Sogang University Machine Learning and Data Mining lab seminar, Neural Networks for newbies and Convolutional Neural Networks. This is prerequisite material to understand deep convolutional architecture.
Relational Mate Value: Consensus and Uniqueness in Romantic EavaluationsMad Scientists
Usually we define our mate value as a traits. That is called 'Classic model' in Social Exchange theory. However, in this presentation, we will introduce 'Relational model', which examines person's 'uniqueness'. In this paper the author found that as time goes by, the mate value rate will be more precise when we apply Relational Model. Instead of Classical Mate model. Thus, we are going to discuss how they measure of these traits in detail.
While I am visiting Finland, I felt a lot of things already had flew behind me. That is because I was not familiar with Finland's start-up culture. Frankly speaking, I was not familiar with start-up culture radically.
All I heard about that before is only from youtube, coursera, or books. However, when I stayed in Finland, I felt so much people in Finland already had adapted start-up culture and entrepreneurship's mindset.
This presentation represents my experience at that time in 2012 Finland, Helsinki. Especially, I want to say thank you to Fastr books, Catch box team, and Startup sauna. Without them, these presentation and what I felt won't came out like this.
I am very proud that those companies and organisation be my friend. I hope many of Korean entreprenuers read this presentation and be stimulated by themselves to grow.
Face Feature Recognition System with Deep Belief Networks, for Korean/KIISE T...Mad Scientists
I submitted KIISE Thesis that <face>, 2014.
In this presentation, I present why I use deep learning to find facial features and what is limitation of before method.
We think that Superhero movies are not cultural, not ideological, but it is totally cultural. Moreover, it delivers Americanization as if it is Globalization. We analyze this topic on cultural way, and suggest its ideal way.
[SW Maestro] Team Loclas 1-2 Final PresentationMad Scientists
Using Datamining, we classify 90% of non-logged on person's preference by their searching keywords.
It is sufficient to use because it makes useless value to useful and easy-to-performing a target marketing.
자본주의는 클래스가 있음을 인정하는 사회이나, 분명한것은 그 클래스가 민주주의가 보장하는 자유나 권리를 침해해선 안된다. 이 발표는 그러한 원칙을 어기고 있는 광고를 소개함으로써 현대 캐피탈이 생산하는 Class의 개념이 현재 민주주의가 가지고 있는 모든 이들의 권리를 침해하는 새로운 Class임을 밝히고, 분석한다
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
4. A Prac'cal Guide to Training Restricted Boltzmann Machine
• Overview
• RBM Requires 7 meta parameters to learn
– Learning Rate
– The Momentum
– The weight-cost
– The sparsity target
– The ini'al values of the weights
– The number of hidden units
– The size of each mini-batch
• But this does not explain why the decisions were made
or how minor changes will affect performance
Aug 2010, Geoffrey Hinton (University of Toronto)
A comparison of Neural Network Architectures
4
26. An Analysis of Single-Layer Networks in Unsupervised Feature Learning
• Effec've Learning Features of 1-Hidden Layer RBM
– Features (# of hidden nodes)
– Recep've Fields (Filters, Field size)
– Whitening
26
2011, Honglak Lee
There are two things we are trying to accomplish with whitening:
1. Make the features less correlated with one another.
2. Give all of the features the same variance.
Whitening has two simple steps:
1. Project the dataset onto the eigenvectors. This rotates the dataset so that there is no correlation
between the components.
2. Normalize the the dataset to have a variance of 1 for all components. This is done by simply dividing
each component by the square root of its eigenvalue.