The document summarizes a deep learning programming course for artificial intelligence. The course covers topics like machine learning, deep learning, convolutional neural networks, recurrent neural networks, and applications of deep learning in medicine. It provides an overview of each week's topics, including an introduction to AI and machine learning in week 3, deep learning in week 4, and applications of AI in medicine in week 5.
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.
This document discusses the history and implementation of regression tree models. It begins by covering early tree models from the 1960s-1980s like CART and GUIDE. It then discusses more modern unified frameworks using modular packages in R like partykit and mob models. The document provides an example using a Bradley-Terry tree to model preferences from paired comparisons. It concludes by discussing potential extensions to deep learning methods.
Brief introduction of neural network including-
1. Fitting Tool
2. Clustering data with a self-organising map
3. Pattern Recognition Tool
4. Time Series Toolbox
Using Deep Learning to Find Similar DressesHJ van Veen
Report by Luís Mey ( https://www.linkedin.com/in/lu%C3%ADs-gustavo-bernardo-mey-97b38927/ ) on Udacity Machine Learning Course - Final Project: Use Deep Learning to Find Similar Dresses.
The document summarizes a deep learning programming course for artificial intelligence. The course covers topics like machine learning, deep learning, convolutional neural networks, recurrent neural networks, and applications of deep learning in medicine. It provides an overview of each week's topics, including an introduction to AI and machine learning in week 3, deep learning in week 4, and applications of AI in medicine in week 5.
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.
This document discusses the history and implementation of regression tree models. It begins by covering early tree models from the 1960s-1980s like CART and GUIDE. It then discusses more modern unified frameworks using modular packages in R like partykit and mob models. The document provides an example using a Bradley-Terry tree to model preferences from paired comparisons. It concludes by discussing potential extensions to deep learning methods.
Brief introduction of neural network including-
1. Fitting Tool
2. Clustering data with a self-organising map
3. Pattern Recognition Tool
4. Time Series Toolbox
Using Deep Learning to Find Similar DressesHJ van Veen
Report by Luís Mey ( https://www.linkedin.com/in/lu%C3%ADs-gustavo-bernardo-mey-97b38927/ ) on Udacity Machine Learning Course - Final Project: Use Deep Learning to Find Similar Dresses.
This document provides an overview of machine learning and the scikit-learn library. It discusses predictive modeling using historical data to build executable models for making predictions on new data. It describes how scikit-learn provides machine learning algorithms and tools through a simple API using Python, NumPy and SciPy. It highlights improvements in scikit-learn 0.15, including reduced training times for ensemble methods and optimized memory usage. It demos income classification using scikit-learn with Census data in an IPython notebook.
This document provides an overview of the gradient boosted machines (GBM) package in R. It begins with an outline of the presentation and then defines GBM as an algorithm that combines multiple decision trees through gradient boosting and iteration to minimize residuals. It notes that GBM can perform classification or regression tasks and has competitive performance, robustness, and the ability to handle different loss functions. The document then discusses GBM's decision tree structure, performance advantages over other algorithms, tuning parameters, and tools for analyzing fitted GBM models. Code examples are also provided to demonstrate fitting and evaluating a GBM model on a dataset.
A brief introduction to deep learning, providing rough interpretation to deep neural networks and simple implementations with Keras for deep learning beginners.
TensorFlow and Keras are popular deep learning frameworks. TensorFlow is an open source library for numerical computation using data flow graphs. It was developed by Google and is widely used for machine learning and deep learning. Keras is a higher-level neural network API that can run on top of TensorFlow. It focuses on user-friendliness, modularization and extensibility. Both frameworks make building and training neural networks easier through modular layers and built-in optimization algorithms.
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
TensorFlow in 3 sentences
Barbara Fusinska provides a high-level overview of TensorFlow in 3 sentences or less. She demonstrates how to build a computational graph for classification tasks using APIs like tf.nn and tf.layers. Barbara encourages attendees to get involved with open source TensorFlow communities on GitHub and through tools like Docker containers.
Introduction to Machine Learning in Python using Scikit-LearnAmol Agrawal
This document outlines a proposed workshop on machine learning in Python using the Scikit-Learn module. The workshop will introduce machine learning concepts and how to use Scikit-Learn to implement supervised and unsupervised machine learning algorithms for classification, regression, dimensionality reduction, and clustering. It will provide example code notebooks and exercises for participants to get hands-on experience applying machine learning to real-world examples and incorporating machine learning into their own work.
Brief presentation about keras framework. The propose of this presentation is to give some ideas about how it works and its main functionalities. In addition, is also shown a function to create different models from a config file.
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Altoros
1. The elements of Neural Networks: Weights, Biases, and Gating functions
2. MNIST (Hand writing recognition) using simple NN in TensorFlow (Introduce Tensors, Computation Graphs)
3. MNIST using Convolution NN in TensorFlow
4. Understanding words and sentences as Vectors
5. word2vec in TensorFlow
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
Introduction to Deep Learning with Pythonindico data
A presentation by Alec Radford, Head of Research at indico Data Solutions, on deep learning with Python's Theano library.
The emphasis of the presentation is high performance computing, natural language processing (using recurrent neural nets), and large scale learning with GPUs.
Video of the talk available here: https://www.youtube.com/watch?v=S75EdAcXHKk
An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka
The document provides an introduction to supervised machine learning and pattern classification. It begins with an overview of the speaker's background and research interests. Key concepts covered include definitions of machine learning, examples of machine learning applications, and the differences between supervised, unsupervised, and reinforcement learning. The rest of the document outlines the typical workflow for a supervised learning problem, including data collection and preprocessing, model training and evaluation, and model selection. Common classification algorithms like decision trees, naive Bayes, and support vector machines are briefly explained. The presentation concludes with discussions around choosing the right algorithm and avoiding overfitting.
Introduction to Machine Learning with Python and scikit-learnMatt Hagy
PyATL talk about machine learning. Provides both an intro to machine learning and how to do it with Python. Includes simple examples with code and results.
The document discusses deep learning concepts without requiring advanced degrees. It introduces StoreKey, a Python package for scientific computing on GPUs and deep learning research. It covers basics like variables, tensors, and autograd in Python. Predictive models discussed include linear regression, logistic regression, and convolutional neural networks. Linear regression fits a line to data to predict unobserved values. Logistic regression predicts binary outcomes by fitting data to a logit function. A convolutional neural network example is shown with input, output, and hidden layers for classification problems.
This document discusses randomized algorithms for solving regression problems on large datasets in parallel and distributed environments. It begins by motivating the need for methods that can perform "vector space analytics" at very large scales beyond what is possible with traditional graph and matrix algorithms. Randomized regression algorithms are introduced as an approach that is faster, simpler to implement, implicitly regularizes to avoid overfitting, and is inherently parallel. The document then outlines how randomized regression can be implemented in shared memory, message passing, MapReduce, and fully distributed environments.
This document provides an overview of VAE-type deep generative models, especially RNNs combined with VAEs. It begins with notations and abbreviations used. The agenda then covers the mathematical formulation of generative models, the Variational Autoencoder (VAE), variants of VAE that combine it with RNNs (VRAE, VRNN, DRAW), a Chainer implementation of Convolutional DRAW, other related models (Inverse DRAW, VAE+GAN), and concludes with challenges of VAE-like generative models.
PyTorch is one of the most widely used deep learning library in python community. In this talk I will cover the basic to advanced guide to implement deep learning model using PyTorch. My goal is to introduce PyTorch and show how to use it for deep learning project.
Nick McClure gave an introduction to neural networks using Tensorflow. He explained the basic unit of neural networks as operational gates and how multiple gates can be combined. He discussed loss functions, learning rates, and activation functions. McClure also covered convolutional neural networks, recurrent neural networks, and applications such as image captioning and style transfer. He concluded by discussing resources for staying up to date with advances in machine learning.
How to Build a Neural Network and Make PredictionsDeveloper Helps
Lately, people have been really into neural networks. They’re like a computer system that works like a brain, with nodes connected together. These networks are great at sorting through big piles of data and figuring out patterns to solve hard problems or guess stuff. And you know what’s super cool? They can keep on learning forever.
Creating and deploying neural networks can be a challenging process, which largely depends on the specific task and dataset you’re dealing with. To succeed in this endeavor, it’s crucial to possess a solid grasp of machine learning concepts, along with strong programming skills. Additionally, a deep understanding of the chosen deep learning framework is essential. Moreover, it’s imperative to prioritize responsible and ethical usage of AI models, especially when integrating them into real-world applications.
Learn from : https://www.developerhelps.com/how-to-build-a-neural-network-and-make-predictions/
This document provides an overview of machine learning and the scikit-learn library. It discusses predictive modeling using historical data to build executable models for making predictions on new data. It describes how scikit-learn provides machine learning algorithms and tools through a simple API using Python, NumPy and SciPy. It highlights improvements in scikit-learn 0.15, including reduced training times for ensemble methods and optimized memory usage. It demos income classification using scikit-learn with Census data in an IPython notebook.
This document provides an overview of the gradient boosted machines (GBM) package in R. It begins with an outline of the presentation and then defines GBM as an algorithm that combines multiple decision trees through gradient boosting and iteration to minimize residuals. It notes that GBM can perform classification or regression tasks and has competitive performance, robustness, and the ability to handle different loss functions. The document then discusses GBM's decision tree structure, performance advantages over other algorithms, tuning parameters, and tools for analyzing fitted GBM models. Code examples are also provided to demonstrate fitting and evaluating a GBM model on a dataset.
A brief introduction to deep learning, providing rough interpretation to deep neural networks and simple implementations with Keras for deep learning beginners.
TensorFlow and Keras are popular deep learning frameworks. TensorFlow is an open source library for numerical computation using data flow graphs. It was developed by Google and is widely used for machine learning and deep learning. Keras is a higher-level neural network API that can run on top of TensorFlow. It focuses on user-friendliness, modularization and extensibility. Both frameworks make building and training neural networks easier through modular layers and built-in optimization algorithms.
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
TensorFlow in 3 sentences
Barbara Fusinska provides a high-level overview of TensorFlow in 3 sentences or less. She demonstrates how to build a computational graph for classification tasks using APIs like tf.nn and tf.layers. Barbara encourages attendees to get involved with open source TensorFlow communities on GitHub and through tools like Docker containers.
Introduction to Machine Learning in Python using Scikit-LearnAmol Agrawal
This document outlines a proposed workshop on machine learning in Python using the Scikit-Learn module. The workshop will introduce machine learning concepts and how to use Scikit-Learn to implement supervised and unsupervised machine learning algorithms for classification, regression, dimensionality reduction, and clustering. It will provide example code notebooks and exercises for participants to get hands-on experience applying machine learning to real-world examples and incorporating machine learning into their own work.
Brief presentation about keras framework. The propose of this presentation is to give some ideas about how it works and its main functionalities. In addition, is also shown a function to create different models from a config file.
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Altoros
1. The elements of Neural Networks: Weights, Biases, and Gating functions
2. MNIST (Hand writing recognition) using simple NN in TensorFlow (Introduce Tensors, Computation Graphs)
3. MNIST using Convolution NN in TensorFlow
4. Understanding words and sentences as Vectors
5. word2vec in TensorFlow
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
Introduction to Deep Learning with Pythonindico data
A presentation by Alec Radford, Head of Research at indico Data Solutions, on deep learning with Python's Theano library.
The emphasis of the presentation is high performance computing, natural language processing (using recurrent neural nets), and large scale learning with GPUs.
Video of the talk available here: https://www.youtube.com/watch?v=S75EdAcXHKk
An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka
The document provides an introduction to supervised machine learning and pattern classification. It begins with an overview of the speaker's background and research interests. Key concepts covered include definitions of machine learning, examples of machine learning applications, and the differences between supervised, unsupervised, and reinforcement learning. The rest of the document outlines the typical workflow for a supervised learning problem, including data collection and preprocessing, model training and evaluation, and model selection. Common classification algorithms like decision trees, naive Bayes, and support vector machines are briefly explained. The presentation concludes with discussions around choosing the right algorithm and avoiding overfitting.
Introduction to Machine Learning with Python and scikit-learnMatt Hagy
PyATL talk about machine learning. Provides both an intro to machine learning and how to do it with Python. Includes simple examples with code and results.
The document discusses deep learning concepts without requiring advanced degrees. It introduces StoreKey, a Python package for scientific computing on GPUs and deep learning research. It covers basics like variables, tensors, and autograd in Python. Predictive models discussed include linear regression, logistic regression, and convolutional neural networks. Linear regression fits a line to data to predict unobserved values. Logistic regression predicts binary outcomes by fitting data to a logit function. A convolutional neural network example is shown with input, output, and hidden layers for classification problems.
This document discusses randomized algorithms for solving regression problems on large datasets in parallel and distributed environments. It begins by motivating the need for methods that can perform "vector space analytics" at very large scales beyond what is possible with traditional graph and matrix algorithms. Randomized regression algorithms are introduced as an approach that is faster, simpler to implement, implicitly regularizes to avoid overfitting, and is inherently parallel. The document then outlines how randomized regression can be implemented in shared memory, message passing, MapReduce, and fully distributed environments.
This document provides an overview of VAE-type deep generative models, especially RNNs combined with VAEs. It begins with notations and abbreviations used. The agenda then covers the mathematical formulation of generative models, the Variational Autoencoder (VAE), variants of VAE that combine it with RNNs (VRAE, VRNN, DRAW), a Chainer implementation of Convolutional DRAW, other related models (Inverse DRAW, VAE+GAN), and concludes with challenges of VAE-like generative models.
PyTorch is one of the most widely used deep learning library in python community. In this talk I will cover the basic to advanced guide to implement deep learning model using PyTorch. My goal is to introduce PyTorch and show how to use it for deep learning project.
Nick McClure gave an introduction to neural networks using Tensorflow. He explained the basic unit of neural networks as operational gates and how multiple gates can be combined. He discussed loss functions, learning rates, and activation functions. McClure also covered convolutional neural networks, recurrent neural networks, and applications such as image captioning and style transfer. He concluded by discussing resources for staying up to date with advances in machine learning.
How to Build a Neural Network and Make PredictionsDeveloper Helps
Lately, people have been really into neural networks. They’re like a computer system that works like a brain, with nodes connected together. These networks are great at sorting through big piles of data and figuring out patterns to solve hard problems or guess stuff. And you know what’s super cool? They can keep on learning forever.
Creating and deploying neural networks can be a challenging process, which largely depends on the specific task and dataset you’re dealing with. To succeed in this endeavor, it’s crucial to possess a solid grasp of machine learning concepts, along with strong programming skills. Additionally, a deep understanding of the chosen deep learning framework is essential. Moreover, it’s imperative to prioritize responsible and ethical usage of AI models, especially when integrating them into real-world applications.
Learn from : https://www.developerhelps.com/how-to-build-a-neural-network-and-make-predictions/
This document provides an overview of data wrangling techniques using Scikit-learn in Python. It discusses how to handle large datasets, explore dataset characteristics, optimize experiment speed, generate new features, detect outliers, and more. It also covers important Scikit-learn concepts like classes, estimators, predictors, transformers, and models. Specific techniques like hashing tricks, sparse matrices, and parallel processing using multiple CPU cores are explained to help process large, unpredictable datasets efficiently.
This document provides an overview of data structures and their implementation in C++. It discusses how data structures are used to organize data efficiently to allow for faster programs. Specific data structures covered include arrays, linked lists, stacks, queues, trees and graphs. The document also explains how to select the appropriate data structure based on the operations needed and resource constraints. It emphasizes that each data structure has costs and benefits and no single structure is best for all situations.
Machine Learning for Incident Detection: Getting StartedSqrrl
This presentation walks you through the uses of machine learning in incident detection and response, outlining some of the basic features of machine learning and specific tools you can use.
Watch the presentation with audio here: https://www.youtube.com/watch?v=4pArapSIu_w
The document provides an overview of machine learning, including definitions, types of machine learning algorithms, and the machine learning process. It defines machine learning as using algorithms to learn from data and make predictions. The main types discussed are supervised learning (classification, regression), unsupervised learning (clustering, association rules), and deep learning using neural networks. The machine learning process involves gathering data, feature engineering, splitting data into training/test sets, selecting an algorithm, training a model, validating it on a validation set, and testing it on a held-out test set. Key enablers of machine learning like large datasets and computing power are also mentioned.
A good foundation has been established for both data mining research and genuine
application based data mining. The current functionality of EMADS is limited
to classification and Meta-ARM. The research team is at present working towards
increasing the diversity of mining tasks that EMADS can address. There are many
directions in which the work can (and is being) taken forward. One interesting direction
is to build on the wealth of distributed data mining research that is currently
available and progress this in an MAS context. The research team are also enhancing
the system’s robustness so as to make it publicly available. It is hoped that once
the system is live other interested data mining practitioners will be prepared to contribute
algorithms and data.
Data structures cs301 power point slides lecture 01shaziabibi5
This lecture covers data structures and their implementation in C++. It discusses how data structures organize data to make programs more efficient. Common data structures that will be covered include dynamic arrays, linked lists, stacks, queues, trees and graphs. The lecture emphasizes that each data structure has costs and benefits depending on the problem, and the goal is to select the most appropriate structure. It also introduces arrays as a basic built-in data structure in many languages and how dynamic arrays can be used when the size is unknown at compile time.
This document provides an overview of machine learning concepts from the first lecture of an introduction to machine learning course. It discusses what machine learning is, examples of tasks that can be solved with machine learning, and key concepts like supervised vs. unsupervised learning, hypothesis spaces, searching hypothesis spaces, generalization, and model complexity.
Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things.
Scaling Deep Learning Algorithms on Extreme Scale Architecturesinside-BigData.com
This document summarizes a presentation on scaling deep learning algorithms on extreme scale architectures. It discusses challenges in using deep learning, a vision for machine/deep learning R&D including novel algorithms, and the MaTEx toolkit which supports distributed deep learning on GPU and CPU clusters. Sample results show strong and weak scaling of asynchronous gradient descent on Summit. Fault tolerance needs and the impact of deep learning on other domains are also covered.
This document provides an overview of computer vision techniques including classification and object detection. It discusses popular deep learning models such as AlexNet, VGGNet, and ResNet that advanced the state-of-the-art in image classification. It also covers applications of computer vision in areas like healthcare, self-driving cars, and education. Additionally, the document reviews concepts like the classification pipeline in PyTorch, data augmentation, and performance metrics for classification and object detection like precision, recall, and mAP.
The document provides information about artificial neural networks and different machine learning techniques. It discusses supervised learning paradigms like the perceptron and backpropagation. It also covers unsupervised learning, reinforcement learning, and concepts like training samples, epochs, batch size, and overfitting/underfitting. Key algorithms and applications are described for supervised, unsupervised, and reinforcement learning.
The document provides information about artificial neural networks and different machine learning techniques. It discusses supervised learning paradigms like the perceptron and backpropagation. It also covers unsupervised learning, reinforcement learning, and concepts like training samples, epochs, batch size, and overfitting/underfitting. Key algorithms and applications are described for supervised, unsupervised, and reinforcement learning.
This document provides an overview of machine learning techniques for classification with imbalanced data. It discusses challenges with imbalanced datasets like most classifiers being biased towards the majority class. It then summarizes techniques for dealing with imbalanced data, including random over/under sampling, SMOTE, cost-sensitive classification, and collecting more data. [/SUMMARY]
The 3TU.Datacentrum repository of research data hosts datasets as well as other objects representing measuring devices, locations, time periods and the like. Virtually all metadata is in rdf so the repository can be approached as an rdf graph. We will show how this is implemented with Fedora Commons, heavily leaning on rdf queries and xslt2.0. As a result of this architecture, it is relatively easy to make the repository linked-data-enabled by generating OAI/ORE resource maps.
While most of the metadata is rdf, most of the data is in NetCDF. Although not very well known in the library world, this is very popular format in various fields of science and engineering. It comes with its own data server Opendap which offers a rich API to interact with the data. Our repository is therefore a hybrid Fedora + Opendap setup and we will show how the two are integrated into a unified view and how they are kept in sync on ingest.
This was presented at the ELAG conference, Palma de Mallorca 2012.
Start machine learning in 5 simple stepsRenjith M P
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
3. Machine Teaching
“Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal
Education”
D 屬於 𝔻,同時可以透過A投射到Θ。
我們已經知道在Θ有一組參數𝜃 ∗是可以
明確找出在𝔻當中特定的dataset,我們
能不能找出這個A-1
目標:
如果可以找出一組特定資料對應到最佳模型
,我們就能拿這些資料來訓練出另外一台機
器。
4. Distilling knowledge in a neural network
Teacher net
student net
劉阿帆
劉阿華
金阿武
Ground truth
Results can use:
1. Softmax with
temperature
2. Logits (better)
results
loss
loss
Teacher nets are thought to lean
much “empirical” information which
would contain in the categories not
activated.
Perturbation the logits
would be better.
“Deep Model Compression: Distilling
Knowledge from Noisy Teachers”
https://arxiv.org/pdf/1610.09650.pdf
5. Teaching assistant
“Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between
Student and Teacher”, https://arxiv.org/pdf/1902.03393.pdf
1. When the power of teachers increase,
the student did not have more power.
2. Enlarging the size of student, the power
is still not increased.
Teacher net Student net
Teaching assistant
net 1
Teaching assistant
net 2
Traditional
method
Directly
training
Using several intermediated size
neural networks (TA nets)
6. Dataset Distillation – CVPR2018
The comparism of model distillation and dataset
distillation.
“Data Distillation: Towards Omni-Supervised Learning”, CVPR2018
Status:
If you have only limited labeled dataset, and much unlabeled
dataset
Labeled
dataset
Model
1Model
1
Model
n
Train several models using the same
dataset
unlabele
d dataset
Model
1Model
1
Model
n
Labeling the unlabeled dataset using existing
models. Hence, you will have several copy answers
with the same dataset.
unlabele
d dataset
unlabele
d dataset
labeled
dataset
New
model
Training a
new model
omni-supervised learning
7. Data Distillation – MIT & FAIR & UC
Berkley
https://openreview.net/forum?id=Sy4lojC9tm
x = {d, t}
d => features
T => label
Learning
rate is
learnable
𝑥
We will train these
tensors. The number
of the the tensors
must be larger than
the “target classes”
𝜃0
The 𝜃0 is sampled
from specific
distribution.
You can sample this
every time or just
using fixed weights.
target
answe
r
predicted
answer
loss
x
𝜃1 loss
Adjust the q
Adjust the 𝑥
p(q0):
1. Random init
2. Fixed init
3. pretrained
8. Data distillation - applications
Poisoning images
Fixed initialization
Random initialization
10. The environment setting
• Tensorflow
– Version : 1.13.0
– CUDA 10
– Python 3.6
• Why Dataset module
– The dataset manipulating often cost 80% of the source code.
– Some skills is regular, such as the “training-validation-test” datasets
creating, dataset mapping, etc.
– These routing operations can be merge into some module
11. The basic concept of the Dataset module
• Class
– Dataset
• The basic container for storage data for further usage
– Iterator
• Access the data
• Functions
– make_make_one_shot_iterator: the elements will be used only once; needn’t initialization.
– make_initializable_iterator : the dataset can be reused by setting new parameters
– Options
• Providing the information of tf.data.Dataset
– FixedLengthRecordDataset
• Mainly designing for binary filesTFRecordDataset
• Handling the data with TFRecorder
– TextLineDataset
• Handling the text data
12. Dataset architecture
dataset
Element 1
Element 2
Element 3
……..
Element n
Each element has the same structure, like:
(Img 1, label 1)
(img 2, label 2)
……
(img n, label n)
The Dataset module use
pieces of whole dataset
1. We need to cut the whole
data into small pieces.
2. tf.data.Dataset.from_tensor
_slices help use to
complete this mission
which will unfold the
tensors by dimension 0.
for example:
Shape: (3,4,5)
from_tensor_slices
Shape: (4,5)
Shape: (4,5)
Shape: (4,5)
You can do anything, like
creating data batch or
mapping the piecies into
some functions.
Because this is the
smallest unit.
https://www.tensorflow.org/guide/datasets
https://www.tensorflow.org/api_docs/python/tf/data/Dataset
15. The other practical operators
• map()
– Transforming the input tensors to other tensor by specific function (usually use lambda simply)
• repeat()
– Since the iterator will stop at the end, if we want to train for many epochs:
dataset = dataset.repeat(10) #repeat dataset 10 times, you can train this for 10 epochs
dataset = dataset.repeat() #repeat infinity times. this would save the work of re-initialize dataset.
• shuffle()
– randomly shuffle dataset would be needed for each epoch, so
dataset = dataset.shuffle(buffer_size=100) # large buffering size makes more random
• tf.contrib.data.shuffle_and_repeat
– repeat() will give infinity accessing right. But shuffle_and_repeat() can give the shuffle function
before the next repeating.
dataset = dataset.apply(tf.contrib.data.shuffle_and_repeat(buffer_size=100))
• batch()
– setting the element fetch numbers
• dataset = dataset.batch(5, True) # fetch 5 elements per time. abandon the last batch
– 因為最後一個Batch常常都是未滿batch size的數量,例如上面的例子就是有可能會少於5個。如果不捨棄就用False(default)
16. Dataset Prefetch
• The issue to the original dataset module is the computing
resources wasting
https://www.tensorflow.org/guide/performance/datasets
if we asynchronized the thread,
the idle time of CPU/GPU would
be less
How to
dataset = dataset.batch(batch_size=FLAGS.batch_size)
return dataset
dataset = dataset.batch(batch_size=FLAGS.batch_size)
dataset =
dataset.prefetch(buffer_size=FLAGS.prefetch_buffer_size)
return dataset