This document provides an overview of the Shogun Machine Learning Toolbox. It discusses that Shogun is a machine learning toolkit that supports a broad range of machine learning algorithms, including support vector machines and kernels. It has been used in various projects involving areas like gene prediction, splice site prediction, and sensor fusion. The document demonstrates support vector classification and discusses features of Shogun like its architecture, history, multitask learning capabilities, and Python integration.
1) The document presents a new associative memory model called SOINN-AM that is designed for online incremental learning in noisy environments.
2) SOINN-AM uses a self-organizing approach where nodes are generated and eliminated autonomously as data is learned, avoiding issues of determining node numbers beforehand like other models.
3) Experiments show SOINN-AM outperforms other associative memory models on incremental learning of new data, many-to-many association, and robustness to noise.
This document summarizes recent research on applying self-attention mechanisms from Transformers to domains other than language, such as computer vision. It discusses models that use self-attention for images, including ViT, DeiT, and T2T, which apply Transformers to divided image patches. It also covers more general attention modules like the Perceiver that aims to be domain-agnostic. Finally, it discusses work on transferring pretrained language Transformers to other modalities through frozen weights, showing they can function as universal computation engines.
Detect helmet impacts in NFL games using videos and player tracking data. A two-stage pipeline involves helmet detection followed by classification of detections as impacts or non-impacts. Post-processing includes temporal non-maximum suppression using tracking results to reduce false positives. Multiple models are ensembled and thresholds tuned on a validation set for best performance.
Public-Key Identification Schemes Based on Multivariate PolynomialsCassius Puodzius
The document outlines a 3-pass identification scheme based on multivariate quadratic polynomials (MQ). It begins with preliminaries on identification schemes and the MQ problem. The MQ-based scheme is then described, using a string commitment function for Peggy to commit to her secret key and for Victor to verify Peggy knows the secret key. The scheme relies on the bilinear property of G(x,y)=F(x+y)-F(x)-F(y) to split the secret key into shares using a cut-and-choose technique.
This document outlines a 30-hour Machine Learning with Python course divided into 5 modules. Module I covers basic Python, Pandas, Matplotlib, and linear regression. Module II focuses on logistic regression, probability, Bayes theorem, and parallel computing. Module III includes clustering, decision trees, and random forests. Module IV presents unstructured text, support vector machines. Module V deals with neural networks, convolutional neural networks for image classification, and recurrent neural networks with LSTM to build a chatbot.
This document presents a method for detecting and localizing video duplicates in large video repositories. It proposes modeling the duplicate likelihood using a Gaussian process that accounts for degradation between original and duplicate frames. It approximates the likelihood function using multi-indexed locality search to prune unlikely sequence matches. Simulation results on a 116 hour repository show the approach achieves high accuracy while scaling efficiently to large datasets. Future work aims to further improve efficiency to handle repositories with tens of thousands of hours of video.
Updated version here:
https://www.slideshare.net/xavigiro/hate-speech-in-pixels-detection-of-offensive-memes-towards-automatic-moderation-205809641
This work addresses the challenge of hate speech detection in Internet memes, and attempts using visual information to automatically detect hate speech, unlike any previous work of our knowledge. Memes are pixel-based multimedia documents that contain photos or illustrations together with phrases which, when combined, usually adopt a funny meaning. However, hate memes are also used to spread hate through social networks, so their automatic detection would help reduce their harmful societal impact. Our results indicate that the model can learn to detect some of the memes, but that the task is far from being solved with this simple architecture. While previous work focuses on linguistic hate speech, our experiments indicate how the visual modality can be much more informative for hate speech detection than the linguistic one in memes. In our experiments, we built a dataset of 5,020 memes to train and evaluate a multi-layer perceptron over the visual and language representations, whether independently or fused.
1) The document presents a new associative memory model called SOINN-AM that is designed for online incremental learning in noisy environments.
2) SOINN-AM uses a self-organizing approach where nodes are generated and eliminated autonomously as data is learned, avoiding issues of determining node numbers beforehand like other models.
3) Experiments show SOINN-AM outperforms other associative memory models on incremental learning of new data, many-to-many association, and robustness to noise.
This document summarizes recent research on applying self-attention mechanisms from Transformers to domains other than language, such as computer vision. It discusses models that use self-attention for images, including ViT, DeiT, and T2T, which apply Transformers to divided image patches. It also covers more general attention modules like the Perceiver that aims to be domain-agnostic. Finally, it discusses work on transferring pretrained language Transformers to other modalities through frozen weights, showing they can function as universal computation engines.
Detect helmet impacts in NFL games using videos and player tracking data. A two-stage pipeline involves helmet detection followed by classification of detections as impacts or non-impacts. Post-processing includes temporal non-maximum suppression using tracking results to reduce false positives. Multiple models are ensembled and thresholds tuned on a validation set for best performance.
Public-Key Identification Schemes Based on Multivariate PolynomialsCassius Puodzius
The document outlines a 3-pass identification scheme based on multivariate quadratic polynomials (MQ). It begins with preliminaries on identification schemes and the MQ problem. The MQ-based scheme is then described, using a string commitment function for Peggy to commit to her secret key and for Victor to verify Peggy knows the secret key. The scheme relies on the bilinear property of G(x,y)=F(x+y)-F(x)-F(y) to split the secret key into shares using a cut-and-choose technique.
This document outlines a 30-hour Machine Learning with Python course divided into 5 modules. Module I covers basic Python, Pandas, Matplotlib, and linear regression. Module II focuses on logistic regression, probability, Bayes theorem, and parallel computing. Module III includes clustering, decision trees, and random forests. Module IV presents unstructured text, support vector machines. Module V deals with neural networks, convolutional neural networks for image classification, and recurrent neural networks with LSTM to build a chatbot.
This document presents a method for detecting and localizing video duplicates in large video repositories. It proposes modeling the duplicate likelihood using a Gaussian process that accounts for degradation between original and duplicate frames. It approximates the likelihood function using multi-indexed locality search to prune unlikely sequence matches. Simulation results on a 116 hour repository show the approach achieves high accuracy while scaling efficiently to large datasets. Future work aims to further improve efficiency to handle repositories with tens of thousands of hours of video.
Updated version here:
https://www.slideshare.net/xavigiro/hate-speech-in-pixels-detection-of-offensive-memes-towards-automatic-moderation-205809641
This work addresses the challenge of hate speech detection in Internet memes, and attempts using visual information to automatically detect hate speech, unlike any previous work of our knowledge. Memes are pixel-based multimedia documents that contain photos or illustrations together with phrases which, when combined, usually adopt a funny meaning. However, hate memes are also used to spread hate through social networks, so their automatic detection would help reduce their harmful societal impact. Our results indicate that the model can learn to detect some of the memes, but that the task is far from being solved with this simple architecture. While previous work focuses on linguistic hate speech, our experiments indicate how the visual modality can be much more informative for hate speech detection than the linguistic one in memes. In our experiments, we built a dataset of 5,020 memes to train and evaluate a multi-layer perceptron over the visual and language representations, whether independently or fused.
This document provides an overview of neural networks and backpropagation algorithms. It discusses how neural networks are inspired by biological brains and how they can be used to perform complex classification tasks. The key topics covered include perceptrons, Adaline networks, multi-layer perceptrons, backpropagation for training multi-layer networks, and an example of how backpropagation works to minimize error in a simple two-layer network.
Comparison of Semantic Similarity Measures for NDVC Detection Using Semantic ...Wesley De Neve
This document compares semantic similarity measures for detecting near-duplicate video clips (NDVCs) using semantic features. It finds that semantic NDVC detection is most effective when similarity is measured using tag statistics from Flickr, rather than WordNet-based measures that are limited to concepts in the English WordNet. Experiments show lower NDVR (better detection) using tag co-occurrence statistics compared to semantic similarity measures based on WordNet concepts and hierarchies.
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.
Deep learning for molecules, introduction to chainer chemistryKenta Oono
1) The document introduces machine learning and deep learning techniques for predicting chemical properties, including rule-based approaches versus learning-based approaches using neural message passing algorithms.
2) It discusses several graph neural network models like NFP, GGNN, WeaveNet and SchNet that can be applied to molecular graphs to predict characteristics. These models update atom representations through message passing and graph convolution operations.
3) Chainer Chemistry is introduced as a deep learning framework that can be used with these graph neural network models for chemical property prediction tasks. Examples of tasks include drug discovery and molecular generation.
Huge-Scale Molecular Dynamics Simulation of Multi-bubble NucleiHiroshi Watanabe
This document summarizes a molecular dynamics simulation of multi-bubble nuclei using the K computer supercomputer. The simulation directly observed the interaction and Ostwald ripening of billions of bubbles without hierarchical modeling assumptions. Analysis of bubble size distributions and scaling behaviors over time validated predictions of Lifshitz-Slyozov-Wagner theory, demonstrating the simulation captured relevant multi-scale and multi-physics phenomena. The huge-scale simulation was necessary to study bubble population dynamics and obtain reliable statistical results.
The attribute that should be tested at the root of the decision tree is the attribute that results in the maximum information gain, or minimum entropy, when used to split the training data. In other words, the attribute that best separates the data according to the target classes. This attribute will create "purer" nodes with respect to the target classes.
This document provides an overview of multi-dimensional RNNs and some architectural issues and recent results related to them. It begins with an introduction to RNNs compared to feedforward neural networks, and solutions like LSTM and GRU to address the vanishing gradient problem. It then discusses several generalizations of the simple RNN architecture, including directionality with BRNN/BLSTM, dimensionality with MDRNN/MDLSTM, and directionality + dimensionality with MDMDRNN. It also covers hierarchical subsampling with HSRNN. The document concludes by summarizing some recent examples that apply these ideas, such as 2D LSTM for scene labeling, as well as new ideas like ReNet, PyraMiD-LSTM, and Grid LSTM.
An introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, RNNs (if time permits), and the CLT/AUT/fixed-point theorems, along with code samples in Java and TensorFlow.
This document summarizes backpropagation and multi-layer feedforward neural networks. It describes how backpropagation can be used to train multi-layer networks by propagating errors backward from the output to adjust weights. The algorithm initializes weights randomly and then iterates over training examples, propagating input forward and error backward to update weights. Cross-validation is used to avoid overfitting by stopping training when validation error stops improving. The document also provides details on the image recognition problems and how the code can be modified to implement different network structures and target outputs.
A more deeper talk on the Transformer architecture from the webinar at NTR
https://www.ntr.ai/webinar/transformery
Google slides version: https://docs.google.com/presentation/d/1dIadh_nIszxXG8-672vJmvFGT6jBp0mOqzNV4g3e2Lc/edit?usp=sharing
1) Instance based learning and case-based reasoning (CBR) provide frameworks for incorporating learning into k-nearest neighbors (kNN) classification.
2) CBR formalizes kNN into five phases: preprocessing training data, retrieving similar cases, reusing solutions, revising solutions if needed, and retaining lessons.
3) Key challenges for CBR include reducing the cost of case matching, automatically generating distance functions tailored to problems, and extracting explanations from cases.
A talk on Transformers at GDG DevParty
27.06.2020
Link to Google Slides version: https://docs.google.com/presentation/d/1N7ayCRqgsFO7TqSjN4OWW-dMOQPT5DZcHXsZvw8-6FU/edit?usp=sharing
The document summarizes Tiark Rompf's talk on using the Delite framework to build domain-specific languages (DSLs) that can be optimized and compiled to different low-level architectures. It provides examples of existing DSLs created with Delite for machine learning, data querying, graph analysis, and collections. The talk discussed how DSLs allow writing programs at a high-level that can then be optimized and generated into high-performance code.
Deep learning is a machine learning technique that uses neural networks with multiple hidden layers between the input and output layers to model high-level abstractions in data. It can perform complex pattern recognition and feature extraction through multiple transformations of the input data. Deep learning techniques like deep neural networks, convolutional neural networks, and deep belief networks have achieved significant performance improvements in areas like computer vision, speech recognition, and natural language processing compared to traditional machine learning methods.
The document introduces two approaches to chemical prediction: quantum simulation based on density functional theory and machine learning based on data. It then discusses using graph-structured neural networks for chemical prediction on datasets like QM9. It presents Neural Fingerprint (NFP) and Gated Graph Neural Network (GGNN) models for predicting molecular properties from graph-structured data. Chainer Chemistry is introduced as a library for chemical and biological machine learning that implements these graph convolutional networks.
Chainer is a deep learning framework which is flexible, intuitive, and powerful.
This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
An introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, and GANs, along with a simple yet complete neural network.
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.
Chainer is a deep learning framework which is flexible, intuitive, and powerful. This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
This document provides an overview and tutorial for PyTorch, a popular deep learning framework developed by Facebook. It discusses what PyTorch is, how to install it, its core packages and concepts like tensors, variables, neural network modules, and optimization. The tutorial also outlines how to define neural network modules in PyTorch, build a network, and describes common layer types like convolution and linear layers. It explains key PyTorch concepts such as defining modules, building networks, and how tensors and variables are used to represent data and enable automatic differentiation for training models.
The slides shown here have been used for talks given to scientists in informal contexts.
Python is introduced as a valuable tool for both producing and evaluating data.
The talk is essentially a guided tour of the author's favourite parts of the Python ecosystem. Besides the Python language itself, NumPy and SciPy as well as Matplotlib are mentioned.
A last part of the talk concerns itself with code execution speed. With this problem in sight, Cython and f2py are introduced as means of glueing different languages together and speeding Python up.
The source code for the slides, code snippets and further links are available in a git repository at
https://github.com/aeberspaecher/PythonForScientists
L Fu - Dao: a novel programming language for bioinformaticsJan Aerts
The document introduces Dao, a new programming language for bioinformatics. It discusses Dao's key features like optional typing, native support for concurrent programming, an LLVM-based JIT compiler, simple C interfaces, and the ClangDao tool for wrapping C/C++ libraries. An example demonstrates using thread tasks and futures for concurrent programming. The document outlines future plans to develop BioDao, an open source project providing bioinformatics modules to the Dao language.
This document provides an overview of neural networks and backpropagation algorithms. It discusses how neural networks are inspired by biological brains and how they can be used to perform complex classification tasks. The key topics covered include perceptrons, Adaline networks, multi-layer perceptrons, backpropagation for training multi-layer networks, and an example of how backpropagation works to minimize error in a simple two-layer network.
Comparison of Semantic Similarity Measures for NDVC Detection Using Semantic ...Wesley De Neve
This document compares semantic similarity measures for detecting near-duplicate video clips (NDVCs) using semantic features. It finds that semantic NDVC detection is most effective when similarity is measured using tag statistics from Flickr, rather than WordNet-based measures that are limited to concepts in the English WordNet. Experiments show lower NDVR (better detection) using tag co-occurrence statistics compared to semantic similarity measures based on WordNet concepts and hierarchies.
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.
Deep learning for molecules, introduction to chainer chemistryKenta Oono
1) The document introduces machine learning and deep learning techniques for predicting chemical properties, including rule-based approaches versus learning-based approaches using neural message passing algorithms.
2) It discusses several graph neural network models like NFP, GGNN, WeaveNet and SchNet that can be applied to molecular graphs to predict characteristics. These models update atom representations through message passing and graph convolution operations.
3) Chainer Chemistry is introduced as a deep learning framework that can be used with these graph neural network models for chemical property prediction tasks. Examples of tasks include drug discovery and molecular generation.
Huge-Scale Molecular Dynamics Simulation of Multi-bubble NucleiHiroshi Watanabe
This document summarizes a molecular dynamics simulation of multi-bubble nuclei using the K computer supercomputer. The simulation directly observed the interaction and Ostwald ripening of billions of bubbles without hierarchical modeling assumptions. Analysis of bubble size distributions and scaling behaviors over time validated predictions of Lifshitz-Slyozov-Wagner theory, demonstrating the simulation captured relevant multi-scale and multi-physics phenomena. The huge-scale simulation was necessary to study bubble population dynamics and obtain reliable statistical results.
The attribute that should be tested at the root of the decision tree is the attribute that results in the maximum information gain, or minimum entropy, when used to split the training data. In other words, the attribute that best separates the data according to the target classes. This attribute will create "purer" nodes with respect to the target classes.
This document provides an overview of multi-dimensional RNNs and some architectural issues and recent results related to them. It begins with an introduction to RNNs compared to feedforward neural networks, and solutions like LSTM and GRU to address the vanishing gradient problem. It then discusses several generalizations of the simple RNN architecture, including directionality with BRNN/BLSTM, dimensionality with MDRNN/MDLSTM, and directionality + dimensionality with MDMDRNN. It also covers hierarchical subsampling with HSRNN. The document concludes by summarizing some recent examples that apply these ideas, such as 2D LSTM for scene labeling, as well as new ideas like ReNet, PyraMiD-LSTM, and Grid LSTM.
An introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, RNNs (if time permits), and the CLT/AUT/fixed-point theorems, along with code samples in Java and TensorFlow.
This document summarizes backpropagation and multi-layer feedforward neural networks. It describes how backpropagation can be used to train multi-layer networks by propagating errors backward from the output to adjust weights. The algorithm initializes weights randomly and then iterates over training examples, propagating input forward and error backward to update weights. Cross-validation is used to avoid overfitting by stopping training when validation error stops improving. The document also provides details on the image recognition problems and how the code can be modified to implement different network structures and target outputs.
A more deeper talk on the Transformer architecture from the webinar at NTR
https://www.ntr.ai/webinar/transformery
Google slides version: https://docs.google.com/presentation/d/1dIadh_nIszxXG8-672vJmvFGT6jBp0mOqzNV4g3e2Lc/edit?usp=sharing
1) Instance based learning and case-based reasoning (CBR) provide frameworks for incorporating learning into k-nearest neighbors (kNN) classification.
2) CBR formalizes kNN into five phases: preprocessing training data, retrieving similar cases, reusing solutions, revising solutions if needed, and retaining lessons.
3) Key challenges for CBR include reducing the cost of case matching, automatically generating distance functions tailored to problems, and extracting explanations from cases.
A talk on Transformers at GDG DevParty
27.06.2020
Link to Google Slides version: https://docs.google.com/presentation/d/1N7ayCRqgsFO7TqSjN4OWW-dMOQPT5DZcHXsZvw8-6FU/edit?usp=sharing
The document summarizes Tiark Rompf's talk on using the Delite framework to build domain-specific languages (DSLs) that can be optimized and compiled to different low-level architectures. It provides examples of existing DSLs created with Delite for machine learning, data querying, graph analysis, and collections. The talk discussed how DSLs allow writing programs at a high-level that can then be optimized and generated into high-performance code.
Deep learning is a machine learning technique that uses neural networks with multiple hidden layers between the input and output layers to model high-level abstractions in data. It can perform complex pattern recognition and feature extraction through multiple transformations of the input data. Deep learning techniques like deep neural networks, convolutional neural networks, and deep belief networks have achieved significant performance improvements in areas like computer vision, speech recognition, and natural language processing compared to traditional machine learning methods.
The document introduces two approaches to chemical prediction: quantum simulation based on density functional theory and machine learning based on data. It then discusses using graph-structured neural networks for chemical prediction on datasets like QM9. It presents Neural Fingerprint (NFP) and Gated Graph Neural Network (GGNN) models for predicting molecular properties from graph-structured data. Chainer Chemistry is introduced as a library for chemical and biological machine learning that implements these graph convolutional networks.
Chainer is a deep learning framework which is flexible, intuitive, and powerful.
This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
An introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, and GANs, along with a simple yet complete neural network.
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.
Chainer is a deep learning framework which is flexible, intuitive, and powerful. This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
This document provides an overview and tutorial for PyTorch, a popular deep learning framework developed by Facebook. It discusses what PyTorch is, how to install it, its core packages and concepts like tensors, variables, neural network modules, and optimization. The tutorial also outlines how to define neural network modules in PyTorch, build a network, and describes common layer types like convolution and linear layers. It explains key PyTorch concepts such as defining modules, building networks, and how tensors and variables are used to represent data and enable automatic differentiation for training models.
The slides shown here have been used for talks given to scientists in informal contexts.
Python is introduced as a valuable tool for both producing and evaluating data.
The talk is essentially a guided tour of the author's favourite parts of the Python ecosystem. Besides the Python language itself, NumPy and SciPy as well as Matplotlib are mentioned.
A last part of the talk concerns itself with code execution speed. With this problem in sight, Cython and f2py are introduced as means of glueing different languages together and speeding Python up.
The source code for the slides, code snippets and further links are available in a git repository at
https://github.com/aeberspaecher/PythonForScientists
L Fu - Dao: a novel programming language for bioinformaticsJan Aerts
The document introduces Dao, a new programming language for bioinformatics. It discusses Dao's key features like optional typing, native support for concurrent programming, an LLVM-based JIT compiler, simple C interfaces, and the ClangDao tool for wrapping C/C++ libraries. An example demonstrates using thread tasks and futures for concurrent programming. The document outlines future plans to develop BioDao, an open source project providing bioinformatics modules to the Dao language.
Numba: Array-oriented Python Compiler for NumPyTravis Oliphant
Numba is a Python compiler that translates Python code into fast machine code using the LLVM compiler infrastructure. It allows Python code that works with NumPy arrays to be just-in-time compiled to native machine instructions, achieving performance comparable to C, C++ and Fortran for numeric work. Numba provides decorators like @jit that can compile functions for improved performance on NumPy array operations. It aims to make Python a compiled and optimized language for scientific computing by leveraging type information from NumPy to generate fast machine code.
Keras with Tensorflow backend can be used for neural networks and deep learning in both R and Python. The document discusses using Keras to build neural networks from scratch on MNIST data, using pre-trained models like VGG16 for computer vision tasks, and fine-tuning pre-trained models on limited data. Examples are provided for image classification, feature extraction, and calculating image similarities.
Deep Learning And Business Models (VNITC 2015-09-13)Ha Phuong
Deep Learning and Business Models
Tran Quoc Hoan discusses deep learning and its applications, as well as potential business models. Deep learning has led to significant improvements in areas like image and speech recognition compared to traditional machine learning. Some business models highlighted include developing deep learning frameworks, building hardware optimized for deep learning, using deep learning for IoT applications, and providing deep learning APIs and services. Deep learning shows promise across many sectors but also faces challenges in fully realizing its potential.
Deep Dive on Deep Learning (June 2018)Julien SIMON
This document provides a summary of a presentation on deep learning concepts, common architectures, Apache MXNet, and infrastructure for deep learning. The agenda includes an overview of deep learning concepts like neural networks and training, common architectures like convolutional neural networks and LSTMs, a demonstration of Apache MXNet's symbolic and imperative APIs, and a discussion of infrastructure for deep learning on AWS like optimized EC2 instances and Amazon SageMaker.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session.
Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
This document describes a digit recognition application that uses a convolutional neural network (CNN) to recognize handwritten digits in real time. It provides requirements to run the application, steps for development including downloading a dataset, defining and training the CNN model, and using OpenCV for real-time prediction. The CNN architecture achieves over 99% test accuracy on the Fashion MNIST dataset. The application allows users to draw digits in front of their webcam to see real-time predictions.
Monteverdi 2.0 - Remote sensing software for Pleiades images analysisotb
Monteverdi 2.0 is a remote sensing software for analysis of Pleiades satellite images that has been improved over time. It began as small demonstration tools but has evolved into a full platform. The latest version, Monteverdi 2.0, has been completely reworked using QT for a modern interface and focuses on processing images through command line applications. Further updates are planned to add more advanced visualization, database management, and processing capabilities.
Secure Kernel Machines against Evasion AttacksPluribus One
This document summarizes research on developing more secure machine learning classifiers. It discusses how gradient-based and surrogate model approaches can be used to evade existing classifiers. The researchers then propose several techniques for building more robust classifiers, including using infinity-norm regularization, cost-sensitive learning, and modifying kernel parameters. Experiments on handwritten digit and spam filtering datasets show the proposed approaches improve security against evasion attacks compared to standard support vector machines.
Camp IT: Making the World More Efficient Using AI & Machine LearningKrzysztof Kowalczyk
Slides from the introductory lecture I gave for students at Camp IT 2019. I tried to cover artificial inteligence, machine learning, most popular algorithms and their applications to business as broadly as possible - for in-depth materials on the given topics, see links and references in the presentation.
This document provides an overview and introduction to deep learning. It discusses key concepts such as neural networks, hidden layers, activation functions, cost functions, and gradient descent. Specific deep learning applications are highlighted, including computer vision, speech recognition, and recommendation systems. Deep learning frameworks like TensorFlow and concepts like convolutional neural networks (CNNs) and generative adversarial networks (GANs) are also explained at a high level. The document aims to introduce attendees to the main ideas and terminology within deep learning.
this is simple internship report of machine learning ,in which there is project of facemask detection using machine learning and python libraries.
slide also contain information about the center of internship and what all things where taught during the internship.
You can also contact Rohan sir for more further internship details.
hope this ppt helps you!!!!!thank you!!!!!
This presentation introduces Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, followed by an Angular application that uses TypeScript in order to replicate the Tensorflow playground.
Configuring Mahout Clustering Jobs - Frank Scholtenlucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
For more than a decade internet search engines have helped users find documents they are looking for. However, what if users aren't looking for anything specific but want a summary of a large document collection and want to be surprised? One solution to this problem is document clustering. Clustering algorithms group documents that have similar content. Real-life examples of clustering are clustered search results of Google news, or tag clouds which group documents under a shared label. Apache Mahout is a framework for scalable machine learning on top of Apache Hadoop and can be used for large scale document clustering. This talk introduces clustering in general and shows you step-by-step how to configure Mahout clustering jobs to create a tag cloud from a document collection. This talk is suitable for people who have some experience with Hadoop and perhaps Mahout. Knowledge of clustering is not required.
[html5jロボット部 第7回勉強会] Microsoft Cognitive Toolkit (CNTK) OverviewNaoki (Neo) SATO
The Microsoft Cognitive Toolkit (CNTK) is Microsoft's open-source deep learning toolkit. It expresses neural networks as computational graphs composed of simple building blocks, supporting various network types and applications. CNTK is production-ready with state-of-the-art accuracy, efficiency, and scalability to multiple GPUs and servers.
The document discusses Acceleo, a code generation tool from Eclipse. It provides an overview of Acceleo's history and capabilities. Key points include: (1) Acceleo allows generating code from models using templates based on the MTL standard; (2) A prototype demonstrates generating an Android app from a model using Acceleo templates; (3) Templates can be customized and extended to override default generation behavior. The tutorial aims to help beginners, experienced Acceleo users, and Android developers learn how to build code generators with Acceleo.
1. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
The SHOGUN Machine Learning Toolbox 2.0
(and its python interface)
S¨ren Sonnenburg, Gunnar R¨tsch, Sebastian Henschel,
o a
Christian Widmer,Jonas Behr, Alexander Zien, Fabio De Bona,
Alexander Binder, Christian Gehl, and Vojtech Franc
GSoC students: Sergey Lisitsyn, Heiko Strathmann, many more...
fml
2. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
What is Shogun?
Machine Learning Toolkit
Broad range of ML algorithms (600 classes)
Large-scale algorithms (up to 50 million examples)
Core written in C++ (> 190, 000 lines of code)
SWIG bindings (support for 8 target languages)
Used in many projects
Gene starts: ARTS [7]
Splice sites: mSplicer [5]
Sensor fusion (private sector)
...many more (see google scholar)!
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3. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Architecture
SWIG - Simple Wrapper Interface Generator
Bindings to a growing number of languages!
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Typemaps!!
4. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Shogun’s history
Project started 1999
Early focus on large-scale SVMs and Kernels
GSoC significantly pushed project forward
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5. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Machine Learning - Learning from Data
What is Machine Learning and what can it do for you?
What is ML?
AIM: Learning from empirical data!
Applications
speech and handwriting recognition
medical diagnosis, bioinformatics
computer vision, object recognition
stock market analysis
network security, intrusion detection . . .
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6. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Machine Learning - Learning from Data
What is Machine Learning and what can it do for you?
What is ML?
AIM: Learning from empirical data!
Applications
speech and handwriting recognition
medical diagnosis, bioinformatics
computer vision, object recognition
stock market analysis
network security, intrusion detection . . .
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7. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Support Vector Machines
Support Vector Machine (SVMs)
SVM primal
n
1 2
min w 2 +C max 1 − yi w xi , 0)
w 2
i=1
regularizer = robustness
loss = error on train data
Training: Solve optimization problem pics/msklogo.p
8. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Support Vector Machines
Support Vector Machine (SVMs)
SVM primal
n
1 2
min w 2 +C max 1 − yi w xi , 0)
w 2
i=1
regularizer = robustness
loss = error on train data
Training: Solve optimization problem pics/msklogo.p
9. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Support Vector Machines
SVM with Kernels
SVM dual
k(xi ,xj )
n n n
1
max − αi αj yi yj xT xj
i )− αi ,
α 2
i=1 j=1 i=1
s.t. 0 ≤ αi ≤ C ∀i ∈ {1, n}
Kernel: Similarity measure; generalization of dot product
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Corresponds to dot product in higher dimensional space
10. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Demo:
Support Vector Classification
Task: separate 2 clouds of points in 2D
Simple code example: SVM Training
lab = BinaryLabels(labels)
train_xt = RealFeatures(features)
gk = GaussianKernel(train_xt, train_xt, width)
svm = LibSVM(10.0, gk, lab)
svm.train()
test_examples = RealFeatures(test_features)
out = svm.apply(test_examples)
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11. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
SVMs and Kernels
Provides generic interface to 11 SVM solvers
Established implementations for solving SVMs with kernels
More recent developments: Fast linear SVM solvers
Kernels for Real-valued Data (in demo)
Linear Kernel, Polynomial Kernel, Gaussian Kernel
String Kernels
Applications in Bioinformatics [4, 8, 10]
Intrusion Detection
Heterogeneous Data Sources
M
Combined kernel: K (x, z) = i=1 βi · Ki (x, z)
βi can be learned using Multiple Kernel Learning [6, 2]
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12. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Beyond Classification
(a) GP regression (b) Structured Output (c) Multitask Learning
Regression: Labels are real values (think least squares)
Structured Output Learning: Predict complex structures
Multitask Learning: Solve several related problems
simultaneuously pics/msklogo.p
13. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Multitask Learning
Example: Learn movie user preferece
Multitask Learning: Jointly learn models for different countries
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Couple related models more strongly
14. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Multitask Learning
Example: Learn movie user preferece
Multitask Learning: Jointly learn models for different countries
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Couple related models more strongly
15. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Multitask Learning
Example: Learn movie user preferece
Multitask Learning: Jointly learn models for different countries
pics/msklogo.p
Couple related models more strongly
16. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Multitask Learning
Example: Learn movie user preferece
Multitask Learning: Jointly learn models for different countries
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Couple related models more strongly
17. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Regularization-based MTL
Multitask Learning is often implemented using regularization:
T T 2A
Graph-regularizer: s=1 t=1 w s − wt s,t
Keeps model parameters similar
Based on given similarity matrix A
n
L2,1 -regularizer: W 2,1 = i=1 wi
Selects common sub-space
Allows any wt in that sub-space
Clustered MTL:
Unknown task relationship
Identifies similar tasks
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18. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Multitask Learning:
MTL Training
feat, labels = ... # Shogun Data objects
task_one = Task(0,10)
task_two = Task(10,20)
group = TaskGroup()
group.append_task(task_one)
group.append_task(task_two)
mtlr = MultitaskL12(0.1,0.1,feat,labels,group)
mtlr.train()
Efficient LibLinear-style solver Graph-reg SVM [9]
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10 other MTL methods (based on SLEP[3]/MALSAR[1])
19. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Structured Output Learning
Complex outputs
Similar framework, different loss function
Bundle-methods: state of the art solvers!
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20. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Other methods
(d) Sparse/L1 methods (e) Gaussian processes (f) Dim-reduct
...and much more I can’t talk about! pics/msklogo.p
21. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Python integration
Python integration
Serialization
Matrix integration
No-copy data wrapping
Rapid prototyping with directors
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22. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Python integration
pythonic interaction with shogun objects
m_real = array(in_data, dtype=float64, order=’F’)
f_real = RealFeatures(m_real)
# slicing
print f_real[0:3, 1]
# operators
f_real += f_real
f_real *= f_real
f_real -= f_real
# no copy
a = RealFeatures()
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a.frombuffer(feats, False)
23. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Python integration: Directors
Simple code example: SVM Training
class ExampleLinearKernel(DirectorKernel):
def __init__(self):
DirectorKernel.__init__(self, True)
def kernel_function(self, idx_a, idx_b):
seq1 = self.get_lhs().get_feature_vector(idx_a)
seq2 = self.get_rhs().get_feature_vector(idx_b)
return numpy.dot(seq1, seq2)
k = ExampleLinearKernel()
svm = SVMLight()
svm.set_kernel(k)
svm.train(train_data)
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24. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
How to get started
Dive into Shogun
Visit our website
Source on github (fork-me!)
Documentation available
Many python examples (> 200)
Debian Package, MacPorts
Active Mailing-List
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25. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
When is SHOGUN for you?
You want to work with SVMs (11 solvers to choose from)
You want to work with Kernels (35 different kernels)
⇒ Esp.: String Kernels / combinations of Kernels
You’re interested recent ML developments (MTL, Structured
Output)
You have large scale computations to do (up to 50 million)
You use one of the following languages:
Python, Octave/MATLAB, R, Java, C#, Ruby, Lua, C++
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26. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Contributors
Original authors: Gunnar Raetsch, Soeren Sonnenburg, Christian Widmer,
Alexander Binder, Alexander Zien, Marius Kloft, Sebastian Henschel, Christian Gehl,
Jonas Behr.
Integrated Code:
Alex Smola (prloqo), Antoine Bordes (LaRank), Thorsten Joachims (SVMLight),
Chin-Chung Chang and Chih-Jen Lin (LIBSVM), Chih-Jen Lin (LibLinear), Vojtech
Franc (LibOCAS), Leon Bottou (SGD SVM), Vikas Sindhwani (SVMLin), Jieping Ye
and Jun Liu (SLEP), Jiayu Zhou and Jieping Ye (MALSAR)
GSoC alumni:
Heiko Strathmann (both 2011 and 2012), Sergey Lisitsyn (both 2011 and 2012),
Chiyuan Zhang (2012), Fernando Iglesias (2012), Viktor Gal (2012), Michal Uricar
(2012), Jacob Walker (2012), Evgeniy Andreev (2012), Baozeng Ding (2011), Alesis
Novik (2011), Shashwat Lal Das (2011)
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27. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
Thank you!
Thank you for your attention!!
For more information, visit:
Implementation http://www.shogun-toolbox.org
More machine learning software http://mloss.org
Machine Learning Data http://mldata.org
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28. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
References I
Zhou Jiayu, Jianhui Chen, and Jieping Ye.
User Manual MALSAR : Multi-tAsk Learning via Structural
Regularization.
Technical report, Arizona State University, 2012.
M. Kloft, U. Brefeld, S. Sonnenburg, P. Laskov, K.R. M¨ller, and A. Zien.
u
Efficient and accurate lp-norm multiple kernel learning.
Advances in Neural Information Processing Systems, 22(22):997–1005,
2009.
Jun Liu, Shuiwang Ji, and Jieping Ye.
SLEP : Sparse Learning with Efficient Projections.
2011.
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29. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
References II
G. Schweikert, A. Zien, G. Zeller, J. Behr, C. Dieterich, C.S. Ong,
P. Philips, F. De Bona, L. Hartmann, A. Bohlen, et al.
mGene: Accurate SVM-based gene finding with an application to
nematode genomes.
Genome research, 19(11):2133, 2009.
Gabriele Schweikert, Alexander Zien, Georg Zeller, Jonas Behr, Christoph
Dieterich, Cheng Soon Ong, Petra Philips, Fabio De Bona, Lisa Hartmann,
Anja Bohlen, Nina Kr¨ger, S¨ren Sonnenburg, and Gunnar R¨tsch.
u o a
mGene: accurate SVM-based gene finding with an application to
nematode genomes.
Genome research, 19(11):2133–43, November 2009.
S. Sonnenburg, G. R¨tsch, C. Sch¨fer, and B. Sch¨lkopf.
a a o
Large scale multiple kernel learning.
The Journal of Machine Learning Research, 7:1565, 2006.
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30. Introduction Machine Learning Dry is all theory: Live Demo SVMs and Kernels Beyond Binary Classification Python integration
References III
S Sonnenburg, A Zien, and G R¨tsch.
a
ARTS: accurate recognition of transcription starts in human.
Bioinformatics, 2006.
S. Sonnenburg, A. Zien, and G. R¨tsch.
a
ARTS: accurate recognition of transcription starts in human.
Bioinformatics, 22(14):e472, 2006.
C Widmer, M Kloft, N G¨rnitz, and G R¨tsch.
o a
Efficient Training of Graph-Regularized Multitask SVMs.
In ECML 2012, 2012.
C. Widmer, J. Leiva, Y. Altun, and G. Raetsch.
Leveraging Sequence Classification by Taxonomy-based Multitask
Learning.
In Research in Computational Molecular Biology, pages 522–534.
Springer, 2010. pics/msklogo.p