Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
This Edureka Recurrent Neural Networks tutorial will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story
Below are the topics covered in this tutorial:
1. Why Not Feedforward Networks?
2. What Are Recurrent Neural Networks?
3. Training A Recurrent Neural Network
4. Issues With Recurrent Neural Networks - Vanishing And Exploding Gradient
5. Long Short-Term Memory Networks (LSTMs)
6. LSTM Use-Case
Recurrent Neural Networks are popular Deep Learning models that have shown great promise to achieve state-of-the-art results in many tasks like Computer Vision, NLP, Finance and much more. Although being models proposed several years ago, RNN have gained popularity recently. In this talk, we will review how these models evolved over the years, dissection of RNN, current applications and its future.
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
Basics of RNNs and its applications with following papers:
- Generating Sequences With Recurrent Neural Networks, 2013
- Show and Tell: A Neural Image Caption Generator, 2014
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, 2015
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2015
- Deep Tracking- Seeing Beyond Seeing Using Recurrent Neural Networks, 2016
- Robust Modeling and Prediction in Dynamic Environments Using Recurrent Flow Networks, 2016
- Social LSTM- Human Trajectory Prediction in Crowded Spaces, 2016
- DESIRE- Distant Future Prediction in Dynamic Scenes with Interacting Agents, 2017
- Predictive State Recurrent Neural Networks, 2017
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Recurrent Neural Networks hold great promise as general sequence learning algorithms. As such, they are a very promising tool for text analysis. However, outside of very specific use cases such as handwriting recognition and recently, machine translation, they have not seen wide spread use. Why has this been the case?
In this presentation, we will first introduce RNNs as a concept. Then we will sketch how to implement them and cover the tricks necessary to make them work well. With the basics covered, we will investigate using RNNs as general text classification and regression models, examining where they succeed and where they fail compared to more traditional text analysis models. A straightforward open-source Python and Theano library for training RNNs with a scikit-learn style interface will be introduced and we’ll see how to use it through a tutorial on a real world text dataset
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
This Edureka Recurrent Neural Networks tutorial will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story
Below are the topics covered in this tutorial:
1. Why Not Feedforward Networks?
2. What Are Recurrent Neural Networks?
3. Training A Recurrent Neural Network
4. Issues With Recurrent Neural Networks - Vanishing And Exploding Gradient
5. Long Short-Term Memory Networks (LSTMs)
6. LSTM Use-Case
Recurrent Neural Networks are popular Deep Learning models that have shown great promise to achieve state-of-the-art results in many tasks like Computer Vision, NLP, Finance and much more. Although being models proposed several years ago, RNN have gained popularity recently. In this talk, we will review how these models evolved over the years, dissection of RNN, current applications and its future.
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
Basics of RNNs and its applications with following papers:
- Generating Sequences With Recurrent Neural Networks, 2013
- Show and Tell: A Neural Image Caption Generator, 2014
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, 2015
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2015
- Deep Tracking- Seeing Beyond Seeing Using Recurrent Neural Networks, 2016
- Robust Modeling and Prediction in Dynamic Environments Using Recurrent Flow Networks, 2016
- Social LSTM- Human Trajectory Prediction in Crowded Spaces, 2016
- DESIRE- Distant Future Prediction in Dynamic Scenes with Interacting Agents, 2017
- Predictive State Recurrent Neural Networks, 2017
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Recurrent Neural Networks hold great promise as general sequence learning algorithms. As such, they are a very promising tool for text analysis. However, outside of very specific use cases such as handwriting recognition and recently, machine translation, they have not seen wide spread use. Why has this been the case?
In this presentation, we will first introduce RNNs as a concept. Then we will sketch how to implement them and cover the tricks necessary to make them work well. With the basics covered, we will investigate using RNNs as general text classification and regression models, examining where they succeed and where they fail compared to more traditional text analysis models. A straightforward open-source Python and Theano library for training RNNs with a scikit-learn style interface will be introduced and we’ll see how to use it through a tutorial on a real world text dataset
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
Much of data is sequential – think speech, text, DNA, stock prices, financial transactions and customer action histories. Modern methods for modelling sequence data are often deep learning-based, composed of either recurrent neural networks (RNNs) or attention-based Transformers. A tremendous amount of research progress has recently been made in sequence modelling, particularly in the application to NLP problems. However, the inner workings of these sequence models can be difficult to dissect and intuitively understand.
This presentation/tutorial will start from the basics and gradually build upon concepts in order to impart an understanding of the inner mechanics of sequence models – why do we need specific architectures for sequences at all, when you could use standard feed-forward networks? How do RNNs actually handle sequential information, and why do LSTM units help longer-term remembering of information? How can Transformers do such a good job at modelling sequences without any recurrence or convolutions?
In the practical portion of this tutorial, attendees will learn how to build their own LSTM-based language model in Keras. A few other use cases of deep learning-based sequence modelling will be discussed – including sentiment analysis (prediction of the emotional valence of a piece of text) and machine translation (automatic translation between different languages).
The goals of this presentation are to provide an overview of popular sequence-based problems, impart an intuition for how the most commonly-used sequence models work under the hood, and show that quite similar architectures are used to solve sequence-based problems across many domains.
"Mainstream access to deep learning technology will greatly impact most industries over the next three to five years."
So what exactly is deep learning? How does it work? And most importantly, why should you even care?
Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.
Practical examples include:
-Vehicle, pedestrian and landmark identification for driver assistance
-Image recognition
-Speech recognition and translation
-Natural language processing
-Life sciences
-What You Will Learn
-Understand the intuition behind Artificial Neural Networks
-Apply Artificial Neural Networks in practice
-Understand the intuition behind Convolutional Neural Networks
-Apply Convolutional Neural Networks in practice
-Understand the intuition behind Recurrent Neural Networks
-Apply Recurrent Neural Networks in practice
-Understand the intuition behind Self-Organizing Maps
-Apply Self-Organizing Maps in practice
-Understand the intuition behind Boltzmann Machines
-Apply Boltzmann Machines in practice
-Understand the intuition behind AutoEncoders
-Apply AutoEncoders in practice
Lecture conducted by me on Deep Learning concepts and applications. Discussed FNNs, CNNs, Simple RNNs and LSTM Networks in detail. Finally conducted a hands-on session on deep-learning using Keras and scikit-learn.
EXPERIMENTS ON DIFFERENT RECURRENT NEURAL NETWORKS FOR ENGLISH-HINDI MACHINE ...csandit
Recurrent Neural Networks are a type of Artificial Neural Networks which are adept at dealing
with problems which have a temporal aspect to them. These networks exhibit dynamic
properties due to their recurrent connections. Most of the advances in deep learning employ
some form of Recurrent Neural Networks for their model architecture. RNN's have proven to be
an effective technique in applications like computer vision and natural language processing. In
this paper, we demonstrate the effectiveness of RNNs for the task of English to Hindi Machine
Translation. We perform experiments using different neural network architectures - employing
Gated Recurrent Units, Long Short Term Memory Units and Attention Mechanism and report
the results for each architecture. Our results show a substantial increase in translation quality
over Rule-Based and Statistical Machine Translation approaches.
Survey for recursive neural networks. Including recursive neural network (RNN), recursive autoencoder (RAE), unfolding RAE & dynamic pooling, matrix-vector RNN (MV-RNN), and recursive neural tensor network (RNTN), published by Socher et al.
Applying Deep Learning Machine Translation to Language ServicesYannis Flet-Berliac
Recurrent neural networks (RNNs) have been performing well for learning tasks for several decades now. The most useful benefit they present for this paper is their ability to use contextual information when mapping between input and output sequences.
A deep neural network for machine translation implies the use of a sequence-to-sequence model, consisting of two RNNs: an encoder that processes the input and a decoder that generates the output.
To meaningfully assess the model’s performances, texts from a translation company and thoughts from skilled experts about specialized topics will be tested.
The presentation "Extremely low-cost lower limb prostheses" focuses on innovative solutions to address the global need for affordable prosthetic devices, especially in low- and middle-income countries. It highlights the intersection of biomedical robotics with prosthetic design, underscoring the need for cost-effective technologies that can improve the quality of life for amputees.
The document analyzes the problem from a statistical perspective, reviewing literature to understand the extent of the issue, and proposes several solutions. It discusses the significance of low-cost prostheses, detailing methods like 3D printing, use of recycled materials, simplified designs, and mass production techniques to reduce costs. The presentation also examines the average annual incomes of populations in Sierra Leone, Bangladesh, and India to contextualize the affordability of prosthetics.
The work showcases various research papers, each presenting a unique approach to designing low-cost prosthetic limbs, from fully mechanical designs to advanced 3D printed, multi-axis feet. It touches upon the pros and cons of these designs, considering factors like manufacturing costs, weight, load capacity, and the need for expert personnel.
The insights provided aim to contribute to the development of prosthetic limbs that are accessible to all, especially focusing on the challenges faced by individuals in regions with limited resources.
MECHANICAL DESIGN METHODS IN ROBOTICS
Final project: Scorpion Robot Design and Assembly
-Guardian Scorpion robots are cutting-edge machines that have revolutionized various industries, from defense to manufacturing. These robots exhibit unrivaled versatility, agility, and efficiency in their operations
In this project we will meticulously reimagined each part using PTC Creo software, ensuring accuracy and performance.
Aims of Mechanical Design in Robotics
* Functions effectively and * efficiently of Robots
Performing structural and multibody simulations using SW for computer aided engineering
* Developing a design case through the course up to 3D printing
* Determining the robot's range of motions, joint mechanisms, and actuation systems.
* Ergonomics and Human-Robot Interaction
Our Team:
- Mohammad Sabouri
- Behnam Jabbari Kalkhoran
- Danial Sabzevari
-Designing A Multimodal Interface for Pediatric -Physiotherapist to manage Therapy Sessions that contain STEP WALKING tasks Interactive & Enjoyable for kids with Prosthetic Leg
- Creating an interactive walking experience for Mat based on a game using a camera, bracelets, screen, video projector, and computer.
-Pediatric physiotherapist needs a solution for finding a method which helps Mat walk correctly and encourage him to do the task with willingness
-Pediatric physiotherapist needs a systematic way also to monitor Mat’s task for further decisions
Intelligent Decision Making Assistant (IDMA) for SAL improvement.pptxMohammad Sabouri
Intelligent Decision Making Assistant (IDMA) for SAL improvement
IDMA based on AI techniques using Internet Of Things(IOT) according to movement map of Staff , Equipment and Machines
Authors:
Mohammad Sabouri- Robotics Engineering
Behnam Jabbari kalkhoran – Robotics Engineering
Loria Davide – Civil Engineering
IDEA:
An optimized methodology which leads to AI based software with the extraction of data from IOT sensors to analyze project data and provide real-time insights for intelligent decision making that leads to improved SLA in construction sites. With features like predictive analytics, resource optimization, and risk management.
Introducing the services of Iran Patent Center- PDFMohammad Sabouri
Introducing the services of Iran Patent Center
In this file, the services provided by the Patent Office of Iran to inventors in Iran are fully reviewed. This file also answers many questions of the inventors in this regard.
All material and intellectual rights of this file belong to Iran Patent Center.
Iran Patent Center:
academy@patentoffice.ir
www.patentoffice.ir
info@patentoffice.ir
www.acrrl.ir
foreign@patentoffice.ir
Introduction to Lens database -in Persian (powerful site for searching)Mohammad Sabouri
Introduction to "Lens Database" in Persian
In this PowerPoint (file) published by the Patent Center of Iran (Patent office), you will get acquainted with the Lens database. This database is a powerful site for searching for patents, articles, etc.
This tutorial is in Persian.
https://patentoffice.ir/
https://www.lens.org/
https://karafam.com/
academy@patentoffice.ir
Patent search engine
Icbme2020- Use of neural network algorithms to predict arterial blood gas ite...Mohammad Sabouri
Use of neural network algorithms to predict arterial blood gas items in trauma victims
Milad Shayan
Mohammad Sabouri
Dr. Shahram Paydar
Leila Shayan
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
27th National and 5th International Conference of Biomedical Engineering
https://sites.google.com/view/acrrl/
http://icbme.ir/
Prediction of Arterial Blood Gases(ABG) by Using Neural Network In Trauma Pat...Mohammad Sabouri
Prediction of Arterial Blood Gases(ABG) by Using Neural Network In Trauma Patients
Milad Shayan
Mohammad Sabouri
Dr. Shahram Paydar
Leila Shayan
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
https://sites.google.com/view/acrrl/
Traffic monitoring using drone_ACRRL_Shiraz UniversityMohammad Sabouri
Traffic monitoring using drone
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Winter 2019
https://sites.google.com/view/acrrl/
Robotic Introduction
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
Milad Shayan
https://sites.google.com/view/acrrl/
https://sites.google.com/view/acrrl/team/current-members/mohammad-sabouri
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Instructor: Dr. Asemani
TA: Mohammad Sabouri
https://sites.google.com/view/acrrl/
Labview1_ Computer Applications in Control_ACRRLMohammad Sabouri
Computer Applications in Control
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Instructor: Dr. Asemani
TA: Mohammad Sabouri
https://sites.google.com/view/acrrl/
Spoofing attack on PMU (Phasor measurement unit)Mohammad Sabouri
A phasor measurement unit (PMU) is a device used to estimate the magnitude and phase angle of an electrical phasor quantity (such as voltage or current) in the electricity grid using a common time source for synchronization. Time synchronization is usually provided by GPS and allows synchronized real-time measurements of multiple remote points on the grid. PMUs are capable of capturing samples from a waveform in quick succession and reconstructing the phasor quantity, made up of an angle measurement and a magnitude measurement. The resulting measurement is known as a synchrophasor. These time synchronized measurements are important because if the grid’s supply and demand are not perfectly matched, frequency imbalances can cause stress on the grid, which is a potential cause for power outages.
s.siyamak2016@gmail.com
https://sites.google.com/view/acrrl/
https://sites.google.com/view/acrrl/team/current-members/sara-siamak
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
2. ACRRL
Applied Control & Robotics Research Laboratory of
Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
3. Recurrent Neural Network
A Recurrent Neural Network (RNN) is a class of artificial neural
network that has memory or feedback loops that allow it to better
recognize patterns in data.
• Recurrent neural network (RNN) is a neural network model
proposed in the 80’s for modelling time series.
RNNs are an extension of regular artificial neural networks that add connections
feeding the hidden layers of the neural network back into themselves - these are called
recurrent connections.
4. Recurrent Neural Network
The structure of the network is similar to feedforward neural network, with t
he distinction that it allows a recurrent hidden state whose activation at each
time is dependent on that of the previous time (cycle).
5. Recurrent Neural Network
• Recurrent networks, on the other hand, take as their input not just the current input exa
mple they see, but also what they have perceived previously in time.
• This enables RNNs to have improved accuracy compared to MLPs, which only have t
he single input and no memory, RNNs can take several prior input and extrapolate out
with improved accuracy. In other words, RNNs take into consideration what it has lear
ned from prior inputs to classify the current input.
6. Some Example of Recurrent
Neural Network
The beauty of recurrent neural networks lies in their diversity of applicati
on. When we are dealing with RNNs they have a great ability to deal wit
h various input and output types.
• Sentiment Classification
• Image Captioning
• Language Translation
7. Some example of Recurrent
Neural Network
Sentiment Classification
This can be a task of simply classifying tweets into positive and negative sentiment. So he
re the input would be a tweet of varying lengths, while output is of a fixed type and size.
8. Some Example of Recurrent
Neural Network
• Image Captioning
Here, let’s say we have an image for which we need a textual description. So we have a si
ngle input – the image, and a series or sequence of words as output. Here the image might
be of a fixed size, but the output is a description of varying lengths
9. Some Example of Recurrent
Neural Network
• Language Translation
This basically means that we have some text in a particular language let’s say English, an
d we wish to translate it in French. Each language has it’s own semantics and would have
varying lengths for the same sentence. So here the inputs as well as outputs are of varying
lengths.
10. Recurrent Neural Network
So RNNs can be used for mapping inputs to outputs of varying types, lengths and are fairly
generalized in their application. Looking at their applications
11. Where to use a RNN?
• Language Modelling and Generating Text
Given a sequence of word, here we try to predict the likelihood of the next wo
rd. This is useful for translation since the most likely sentence would be the on
e that is correct.
• Machine Translation
Translating text from one language to other uses one or the other form of RN
N. All practical day systems use some advanced version of a RNN.
• Speech Recognition
Predicting phonetic segments based on input sound waves, thus formulating a
word.
12. Where to use a RNN?
• Generating Image Descriptions
A very big use case is to understand what is happening inside an image, thus
we have a good description. This works in a combination of CNN and RNN. C
NN does the segmentation and RNN then used the segmented data to recreat
e the description. It’s rudimentary but the possibilities are limitless.
• Video Tagging
This can be used for video search where we do image description of a video fr
ame by frame.
15. Mathematical Formulation
Recurrent neural networks learn from sequences. A sequence is define
d as a list of (xi,yi) pairs, where xi is the input at time i and yi is the d
esired output. Note that that is a single sequence; the entire data set co
nsists of many sequences.
16. Mathematical Formulation
In addition to the data in our data set, each time step has another input: the hidden state hi−1
from the previous time step. In this way, the recurrent neural network can maintain some int
ernal context as it progresses forward in the sequence. Thus, to summarize, at time i the rec
urren
t network has:
• Input vector xi (data)
• Output vector yi (data)
• Predicted output vector y^i(computed through forward propagation)
• Hidden state hi
17. Mathematical Formulation
When looking only at a single timestep, the recurrent network looks like a simple one-hidden-
layer feed forward network. It has an input layer for xi, an output layer for yi, and another inp
ut layer for the previous hidden state hi−1. Finally, it has one hidden layer between these. The
only unusual thing is that we have two input layers; both of the input layers are are connected
to the hidden layer as if they were really just a single layer.
Thus, we have three separate matrices of weights:
• Input-to-hidden weights Whx
• Hidden-to-hidden weights Whh
• Hidden-to-output weights Wyh
18. Mathematical Formulation
There are several things to note here. First of all, note that the predicted outputs are not su
bject to the nonlinearity. We may want to predict things other than the things in the range
of the nonlinearity, so instead we do not apply the nonlinearity. For specific use cases of r
ecurrent nets, this can be amended, and a nonlinearity specific to the problem can be chos
en. Finally, note that these equations are the same as the equations for a single hidden lay
er feed forward network, with the caveat that the input layer is broken into two pieces xi a
nd hi−1.
22. Jordan RNN
Pro: Fast to train because can be parallelized in time
Cons:
• Output transforms hidden state → nonlinear effects, information distorted
• The output dimension may be too small → information in hidden states is t
runcated
23. Elman RNN
Often referenced as the basic RNN structure and called “Vanilla” RNN
• Should see complete sequence to be trained
• Can not be parallelized by timestamps
• Has some important training difficulties….
28. RNN Problem
However, conventional RNNs have a few limitations. They are difficult to train and have a
very short-term memory, which limits their functionality. To overcome the memory limitatio
n, a newer form of RNN, known as LSTM or Long Short-term Memory networks are used.
LSTMs extend the memory RNNs to enable them to perform tasks involving longer-term m
emory.
