The document describes an end-to-end memory network model that can reason over long-term memories. The model uses an external memory component that can be read from and written to. It incorporates reasoning with attention over the memory through multiple "hops". The model is evaluated on bAbI tasks that require multi-step reasoning by attending to multiple supporting facts stored in the memory. The model achieves good performance on the challenging bAbI tasks through its ability to focus attention on the most relevant memory vectors.
Deep Learning Interview Questions And Answers | AI & Deep Learning Interview ...Simplilearn
This Deep Learning interview questions and answers presentation will help you prepare for Deep Learning interviews. This presentation is ideal for both beginners as well as professionals who are appearing for Deep Learning, Machine Learning or Data Science interviews. Learn what are the most important Deep Learning interview questions and answers and know what will set you apart in the interview process.
Some of the important Deep Learning interview questions are listed below:
1. What is Deep Learning?
2. What is a Neural Network?
3. What is a Multilayer Perceptron (MLP)?
4. What is Data Normalization and why do we need it?
5. What is a Boltzmann Machine?
6. What is the role of Activation Functions in neural network?
7. What is a cost function?
8. What is Gradient Descent?
9. What do you understand by Backpropagation?
10. What is the difference between Feedforward Neural Network and Recurrent Neural Network?
11. What are some applications of Recurrent Neural Network?
12. What are Softmax and ReLU functions?
13. What are hyperparameters?
14. What will happen if learning rate is set too low or too high?
15. What is Dropout and Batch Normalization?
16. What is the difference between Batch Gradient Descent and Stochastic Gradient Descent?
17. Explain Overfitting and Underfitting and how to combat them.
18. How are weights initialized in a network?
19. What are the different layers in CNN?
20. What is Pooling in CNN and how does it work?
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.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Learn more at: https//www.simplilearn.com
Deep Learning Tutorial | Deep Learning Tutorial For Beginners | What Is Deep ...Simplilearn
This presentation about Deep Learning is designed for beginners who want to learn Deep Learning from scratch. We will look at where Deep Learning is applied and what exactly this term means. We'll see how Deep Learning, Machine Learning, and AI are different and why Deep Learning even came into the picture. We will then proceed to look at Neural Networks, which are the core of Deep Learning. Before we move into the working of Neural Networks, we'll cover activation and cost functions. The video will also introduce you to the most popular Deep Learning platforms. We wrap it up with a demo in TensorFlow to predict if a person receives a salary above or below 50k. Now, let us get started and understand Deep Learning in detail.
Below topics are explained in this Deep Learning presentation:
1. Applications of Deep Learning
2. What is Deep Learning
3. Why is Deep Learning important
4. What are Neural Networks
5. Activation function
6. Cost function
7. How do Neural Networks work
8. Deep Learning platforms
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
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. 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:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement Deep Learning algorithms, understand Neural Networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional Neural Networks, Recurrent Neural Networks, training deep networks and high-level interfaces
4. Build Deep Learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of Artificial Neural Networks
6. Troubleshoot and improve Deep Learning models
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Episodic Memory Reader: Learning What to Remember for Question Answering from...LGCNSairesearch
2019/09/05
LGCNS AI Tech Talk for NLU (feat.KorQuAD)
- KAIST 한문수, 강민기 님
- Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data
Iterative Multi-document Neural Attention for Multiple Answer PredictionClaudio Greco
Slides for the presentation of the paper "Iterative Multi-document Neural Attention for Multiple Answer Prediction" at the Deep Understanding and Reasoning: A challenge for Next-generation Intelligent Agents (URANIA) workshop, held in the context of the AI*IA 2016 conference.
Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning...Simplilearn
This presentation about Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We'll also implement a neural network manually. Finally, we'll code a neural network in Python using TensorFlow.
Below topics are explained in this Deep Learning with Python presentation:
1. What is Deep Learning
2. Biological versus Artificial Intelligence
3. What is a Neural Network
4. Activation function
5. Cost function
6. How do Neural Networks work
7. How do Neural Networks learn
8. Implementing the Neural Network
9. Gradient descent
10. Deep Learning platforms
11. Introduction to TensoFlow
12. Implementation in TensorFlow
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:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Deep Learning Interview Questions And Answers | AI & Deep Learning Interview ...Simplilearn
This Deep Learning interview questions and answers presentation will help you prepare for Deep Learning interviews. This presentation is ideal for both beginners as well as professionals who are appearing for Deep Learning, Machine Learning or Data Science interviews. Learn what are the most important Deep Learning interview questions and answers and know what will set you apart in the interview process.
Some of the important Deep Learning interview questions are listed below:
1. What is Deep Learning?
2. What is a Neural Network?
3. What is a Multilayer Perceptron (MLP)?
4. What is Data Normalization and why do we need it?
5. What is a Boltzmann Machine?
6. What is the role of Activation Functions in neural network?
7. What is a cost function?
8. What is Gradient Descent?
9. What do you understand by Backpropagation?
10. What is the difference between Feedforward Neural Network and Recurrent Neural Network?
11. What are some applications of Recurrent Neural Network?
12. What are Softmax and ReLU functions?
13. What are hyperparameters?
14. What will happen if learning rate is set too low or too high?
15. What is Dropout and Batch Normalization?
16. What is the difference between Batch Gradient Descent and Stochastic Gradient Descent?
17. Explain Overfitting and Underfitting and how to combat them.
18. How are weights initialized in a network?
19. What are the different layers in CNN?
20. What is Pooling in CNN and how does it work?
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.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Learn more at: https//www.simplilearn.com
Deep Learning Tutorial | Deep Learning Tutorial For Beginners | What Is Deep ...Simplilearn
This presentation about Deep Learning is designed for beginners who want to learn Deep Learning from scratch. We will look at where Deep Learning is applied and what exactly this term means. We'll see how Deep Learning, Machine Learning, and AI are different and why Deep Learning even came into the picture. We will then proceed to look at Neural Networks, which are the core of Deep Learning. Before we move into the working of Neural Networks, we'll cover activation and cost functions. The video will also introduce you to the most popular Deep Learning platforms. We wrap it up with a demo in TensorFlow to predict if a person receives a salary above or below 50k. Now, let us get started and understand Deep Learning in detail.
Below topics are explained in this Deep Learning presentation:
1. Applications of Deep Learning
2. What is Deep Learning
3. Why is Deep Learning important
4. What are Neural Networks
5. Activation function
6. Cost function
7. How do Neural Networks work
8. Deep Learning platforms
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
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. 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:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement Deep Learning algorithms, understand Neural Networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional Neural Networks, Recurrent Neural Networks, training deep networks and high-level interfaces
4. Build Deep Learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of Artificial Neural Networks
6. Troubleshoot and improve Deep Learning models
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Episodic Memory Reader: Learning What to Remember for Question Answering from...LGCNSairesearch
2019/09/05
LGCNS AI Tech Talk for NLU (feat.KorQuAD)
- KAIST 한문수, 강민기 님
- Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data
Iterative Multi-document Neural Attention for Multiple Answer PredictionClaudio Greco
Slides for the presentation of the paper "Iterative Multi-document Neural Attention for Multiple Answer Prediction" at the Deep Understanding and Reasoning: A challenge for Next-generation Intelligent Agents (URANIA) workshop, held in the context of the AI*IA 2016 conference.
Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning...Simplilearn
This presentation about Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We'll also implement a neural network manually. Finally, we'll code a neural network in Python using TensorFlow.
Below topics are explained in this Deep Learning with Python presentation:
1. What is Deep Learning
2. Biological versus Artificial Intelligence
3. What is a Neural Network
4. Activation function
5. Cost function
6. How do Neural Networks work
7. How do Neural Networks learn
8. Implementing the Neural Network
9. Gradient descent
10. Deep Learning platforms
11. Introduction to TensoFlow
12. Implementation in TensorFlow
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:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Deep Learning: Introduction & Chapter 5 Machine Learning BasicsJason Tsai
Given lecture for Deep Learning 101 study group with Frank Wu on Dec. 9th, 2016.
Reference: https://www.deeplearningbook.org/
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...Simplilearn
This Deep Learning presentation will help you in understanding what is Deep Learning, why do we need Deep learning, what is neural network, applications of Deep Learning, what is perceptron, implementing logic gates using perceptron, types of neural networks. At the end of the video, you will get introduced to TensorFlow along with a usecase implementation on recognizing hand-written digits. Deep Learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Deep Learning, on the other hand, uses advanced computing power and special type of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. W will also understand neural networks and how they work in this Deep Learning tutorial video. This Deep Learning tutorial is ideal for professionals with beginner to intermediate level of experience. Now, let us dive deep into this topic and understand what Deep Learning actually is.
Below topics are explained in this Deep Learning presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. What is Neural network?
4. What is Perceptron?
5. Implementing logic gates using Perceptron
6. Types of Neural networks
7. Applications of Deep Learning
8. Working of Neural network
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
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.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Alessandro Suglia
Presentation for "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
See paper: http://ceur-ws.org/Vol-1653/paper_11.pdf
Visual-Semantic Embeddings: some thoughts on LanguageRoelof Pieters
Language technology is rapidly evolving. A resurgence in the use of distributed semantic representations and word embeddings, combined with the rise of deep neural networks has led to new approaches and new state of the art results in many natural language processing tasks. One such exciting - and most recent - trend can be seen in multimodal approaches fusing techniques and models of natural language processing (NLP) with that of computer vision.
The talk is aimed at giving an overview of the NLP part of this trend. It will start with giving a short overview of the challenges in creating deep networks for language, as well as what makes for a “good” language models, and the specific requirements of semantic word spaces for multi-modal embeddings.
Deep dive into the world of word vectors. We will cover - Bigram model, Skip-gram, CBOW, GLO. Starting from simplest models, we will journey through key results and ideas in this area.
Methodological study of opinion mining and sentiment analysis techniquesijsc
Decision making both on individual and organizational level is always accompanied by the search of
other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum
discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated
content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining
and sentiment analysis are the formalization for studying and construing opinions and sentiments. The
digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is
an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
On how to change the utility curve of deep learning to make deep learning projects deliver an ROI no matter how accurate the machine learning system is - presented at the Nasscom Analytics Summit 2018.
The transformer is the neural architecture that has received most attention in the early 2020's. It removed the recurrency in RNNs, replacing it with and attention mechanism across the input and output tokens of a sequence (cross-attenntion) and between the tokens composing the input (and output) sequences, named self-attention.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Deep Learning: Introduction & Chapter 5 Machine Learning BasicsJason Tsai
Given lecture for Deep Learning 101 study group with Frank Wu on Dec. 9th, 2016.
Reference: https://www.deeplearningbook.org/
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...Simplilearn
This Deep Learning presentation will help you in understanding what is Deep Learning, why do we need Deep learning, what is neural network, applications of Deep Learning, what is perceptron, implementing logic gates using perceptron, types of neural networks. At the end of the video, you will get introduced to TensorFlow along with a usecase implementation on recognizing hand-written digits. Deep Learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Deep Learning, on the other hand, uses advanced computing power and special type of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. W will also understand neural networks and how they work in this Deep Learning tutorial video. This Deep Learning tutorial is ideal for professionals with beginner to intermediate level of experience. Now, let us dive deep into this topic and understand what Deep Learning actually is.
Below topics are explained in this Deep Learning presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. What is Neural network?
4. What is Perceptron?
5. Implementing logic gates using Perceptron
6. Types of Neural networks
7. Applications of Deep Learning
8. Working of Neural network
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
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.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Alessandro Suglia
Presentation for "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
See paper: http://ceur-ws.org/Vol-1653/paper_11.pdf
Visual-Semantic Embeddings: some thoughts on LanguageRoelof Pieters
Language technology is rapidly evolving. A resurgence in the use of distributed semantic representations and word embeddings, combined with the rise of deep neural networks has led to new approaches and new state of the art results in many natural language processing tasks. One such exciting - and most recent - trend can be seen in multimodal approaches fusing techniques and models of natural language processing (NLP) with that of computer vision.
The talk is aimed at giving an overview of the NLP part of this trend. It will start with giving a short overview of the challenges in creating deep networks for language, as well as what makes for a “good” language models, and the specific requirements of semantic word spaces for multi-modal embeddings.
Deep dive into the world of word vectors. We will cover - Bigram model, Skip-gram, CBOW, GLO. Starting from simplest models, we will journey through key results and ideas in this area.
Methodological study of opinion mining and sentiment analysis techniquesijsc
Decision making both on individual and organizational level is always accompanied by the search of
other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum
discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated
content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining
and sentiment analysis are the formalization for studying and construing opinions and sentiments. The
digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is
an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
On how to change the utility curve of deep learning to make deep learning projects deliver an ROI no matter how accurate the machine learning system is - presented at the Nasscom Analytics Summit 2018.
The transformer is the neural architecture that has received most attention in the early 2020's. It removed the recurrency in RNNs, replacing it with and attention mechanism across the input and output tokens of a sequence (cross-attenntion) and between the tokens composing the input (and output) sequences, named self-attention.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Grammarly Meetup: Memory Networks for Question Answering on Tabular Data - Sv...Grammarly
Tabular data is difficult to analyze and search through. There is a clear need for new tools and interfaces that would allow even non-tech-savvy users to gain insights from open datasets without resorting to specialized data analysis tools or even having to fully understand the dataset structure. We explore the End-To-End Memory Networks architecture (Sukhbaatar et al., 2015) in application to answering natural language questions from tabular data. This architecture was originally designed for the question-answering tasks from short natural language texts (bAbI tasks) (Weston et al., 2015), which include testing elements of inductive and deductive reasoning, co-reference resolution and time manipulation.
Memory Networks for Question Answering on Tabular Data Viktoria Kolomiets
Tabular data is difficult to analyze and search through. There is a clear need for new tools and interfaces that would allow even non-tech-savvy users to gain insights from open datasets without resorting to specialized data analysis tools or even having to fully understand the dataset structure. We explore the End-To-End Memory Networks architecture (Sukhbaatar et al., 2015) in application to answering natural language questions from tabular data. This architecture was originally designed for the question-answering tasks from short natural language texts (bAbI tasks) (Weston et al., 2015), which include testing elements of inductive and deductive reasoning, co-reference resolution and time manipulation.
Slides from an online presentation given to teachers in the Enhancing Education Through Technology group under the auspices of Erie 1 BOCES. April, 2009.
Online Tests: Filling in the Gaps | Mary-Ann Shuker & Dr Suzzanne Owen - Grif...Blackboard APAC
Blackboard online tests are powerful, with multiple settings and multiple question types. So often test are created with only two question types - multiple choice and short answer - with the majority testing recall only. Academics are often confused or simply unaware of all the settings and steps in administering tests. We present a tool developed to: engage academics with the full range of automatically marking question types; explain how to create higher order thinking questions; and expose them to the full workflow of online test capabilities. This tool can be used in a class or as self-directed learning. Finally we share statistics and feedback on its success and a tricky method for enticing busy academics to fully engage in a class for two hours.
Tranformational Model of Translational Research that Leverages Educational Te...EduSkills OECD
The CERI OECD/National Science Foundation International Conference took place in Paris, at the OECD Headquarters on 23-24 January 2012. Here the presentation of Session 6, Technology, Item 1.
발표자: 조경현 (NYU 교수)
Kyunghyun Cho is an assistant professor of computer science and data science at New York University.
He was a postdoctoral fellow at University of Montreal until summer 2015, and received PhD and MSc degrees from Aalto University early 2014.
He tries best to find a balance among machine learning, natural language processing and life, but often fails to do so.
개요:
There are three axes along which advances in machine learning and deep learning happen. They are (1) network architectures, (2) learning algorithms and (3) spatio-temporal abstraction.
In this talk, I will describe a set of research topics I’ve pursued in each of these axes.
- For network architectures, I will describe how recurrent neural networks, which were largely forgotten during 90s and early 2000s, have evolved over time and have finally become a de facto standard in machine translation.
- I continue on to discussing various learning paradigms, how they related to each other, and how they are combined in order to build a strong learning system. Along this line, I briefly discuss my latest research on designing a query-efficient imitation learning algorithm for autonomous driving.
- Lastly, I present my view on what it means to be a higher-level learning system. Under this view each and every end-to-end trainable neural network serves as a module, regardless of how they were trained, and interacts with each other in order to solve a higher-level task.
I will describe my latest research on trainable decoding algorithm as a first step toward building such a framework.
발표영상: https://youtu.be/soZXAH3leeQ (본 발표는 영어로 진행됩니다.)
Grokking Techtalk #45: First Principles ThinkingGrokking VN
Bạn có từng nghe ai đó nói về First Principles Thinking? Nó là gì và engineers chúng ta có thể sử dụng như thế nào cho công việc của mình?
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First Principles Thinking là một trong những phương pháp mà chúng ta có thể vận dụng để phân chia những vấn đề phức tạp thành những vấn đề nhỏ và cơ bản hơn có thể giải quyết được, cuối cùng tổng hợp lại thành một giải pháp có thể giải quyết được vấn đề phức tạp ban đầu.
Nối tiếp về chủ đề Problem Solving, trong Techtalk lần này, Grokking Vietnam cùng Gambaru sẽ mang đến cho các bạn thêm một góc nhìn về tư duy giải quyết vấn đề. Chúng ta sẽ cùng gặp gỡ anh Hùng Đoàn - exFacebook và hiện đang là Software Engineer tại Coda và cùng nhau thảo luận sâu hơn về chủ đề First Principles Thinking này nhé.
Nội dung bài talk:
* Analogy thinking
* Breaking a problem space down to its building blocks
* Techniques to arrive at first principles thinking
* Application in Programming
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Ngôn ngữ: Tiếng Việt
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Speaker:
- Hùng Đoàn - Software Engineer @ Coda.io, Ex-Facebook SWE
Anh Hùng có nhiều năm kinh nghiệm trong các lĩnh vực thuộc software engineering. Anh từng thi quốc gia tin học quốc tế và đoạt huy chương vào 2007
Talk on Ebooks at the NSF BPC/CE21/STEM-C Community MeetingMark Guzdial
Why we should use ebooks (rather than MOOCs) for CS learning opportunities for high school teachers. We use educational psychology principles to design our book. The talk presents data from our first three studies: usability, log file analysis, and learnability
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
160805 End-to-End Memory Networks
1. Perception and Intelligence Laboratory
Seoul
National
University
End-to-End Memory
Networks
Sukhbaatar, S., Weston, J., & Fergus, R. (NIPS 2015)
Junho Cho2016.08.05
8. Full Scheme
2. Embed question
and inner product with
each memory vector.
If memory is related to
question, it will be
more attended
9. Full Scheme
3. Knowing which
memory to attend,
weight sum on
output memory vector.
Add embedded question,
on output and predict
answer as one-hot vector.
17. End-to-end Memory Network (MemN2N)
• Presented in NIPS2015
• New end-to-end model:
• Reads from memory with soft attention
• Performs multiple lookups (hops) on memory
• End-to-end training with back-propagation
• Only need supervision on the final output
• It is based on MemNN but that had:
• Hard attention
• requires explicit supervision of attention during training
• Only feasible for simple tasks
19. Input: Context 문장들과 질문
𝑥1 = Mary journeyed to the den.
𝑥2 = Mary went back to the kitchen.
𝑥3 = John journeyed to the bedroom.
𝑥4 = Mary discarded the milk.
Q: Where was the milk before the den?
𝑥1 𝑥2 𝑥3 𝑥4
slide from TaeHoon Kim
Word: 𝑥𝑖𝑗 ∈ ℝ 𝑉
one hot vector, V = 177
Sentences and Question: BoW Representation
20. 문장 하나 𝑥𝑖, 단어의 조합
A sentence ∶ 𝑥𝑖
“Mary journeyed to the den”
𝑥𝑖 = 𝑥𝑖1, 𝑥𝑖2, … , 𝑥𝑖𝑛
𝑥𝑖1 = mary
𝑥𝑖2 = journeyed
𝑥𝑖3 = to
𝑥𝑖4 = the
𝑥𝑖5 = den
slide from TaeHoon Kim
Word: 𝑥𝑖𝑗 ∈ ℝ 𝑉
one hot vector, V = 177
Sentences and Question: BoW Representation
21. 단어 하나: Bag-of-Words (BoW)로
표현
1
0
0
0
.
.
.
0
mary
Bag-of-Words (BoW)
𝑥𝑖1
=
𝑥𝑖1 = mary
𝑥𝑖 = 𝑥𝑖1, 𝑥𝑖2, … , 𝑥𝑖𝑛
slide from TaeHoon Kim
Word: 𝑥𝑖𝑗 ∈ ℝ 𝑉
one hot vector, V = 177
Sentences and Question: BoW Representation
A sentence ∶ 𝑥𝑖
“Mary journeyed to the den”
𝑥𝑖 = 𝑥𝑖1, 𝑥𝑖2, … , 𝑥𝑖𝑛
Mary
journeyed
to
the
den
22. 문장 하나: BoW의 set
1
0
0
0
.
.
.
0
mary
0
1
0
0
.
.
.
0
journeyed
0
0
1
0
.
.
.
0
to
0
0
0
1
.
.
.
0
the
𝑥𝑖 = 𝑥𝑖1, 𝑥𝑖2, … , 𝑥𝑖𝑛
𝑥𝑖1 = mary
𝑥𝑖2 = journeyed
𝑥𝑖3 = to
𝑥𝑖4 = the
𝑥𝑖5 = den
slide from TaeHoon Kim
Word: 𝑥𝑖𝑗 ∈ ℝ 𝑉
one hot vector, V = 177
Sentences and Question: BoW Representation
Mary
journeyed
to
the
den
0
0
0
0
.
.
.
1
den
23. Input: BoW로 표현된 Context 문장들과
질문
𝑥1 = Mary journeyed to the den.
𝑥2 = Mary went back to the kitchen.
𝑥3 = John journeyed to the bedroom.
𝑥4 = Mary discarded the milk.
𝑥1 𝑥3 𝑥8 𝑥2 𝑥4 𝑥6 𝑥9
Q: Where was the milk before the den?
1
0
0
0
.
.
.
0
0
1
0
0
.
.
.
0
0
0
1
0
.
.
.
0
0
0
0
1
.
.
.
0
slide from TaeHoon Kim
Word: 𝑥𝑖𝑗 ∈ ℝ 𝑉
one hot vector, V = 177
Sentences and Question: BoW Representation
27. Embedding to Memory
𝑚1, 𝑚2, 𝑚3, 𝑚4
𝑥1 = Mary journeyed to the den.
𝑥2 = Mary went back to the kitchen.
𝑥3 = John journeyed to the bedroom.
𝑥4 = Mary discarded the milk.
𝑥1 = Mary journeyed to the den.
𝑥2 = Mary went back to the kitchen.
𝑥3 = John journeyed to the bedroom.
𝑥4 = Mary discarded the milk.
In bAbI task, Simpler form of memory component.
d d d
𝑚1, 𝑚2, 𝑚3, 𝑚4
𝑨
𝑚𝑖 =
𝑗
𝑨𝑥𝑖𝑗
𝑨: embedding matrix
d
28. Memory: 필요한 것만 Input으로 사용
Input memory
실제로 메모리의 본체는 embedding matrix인 𝑨이며,
𝑨로 BoW Representation을 memory vector로 embedding
Training에서 𝑨를 학습.
𝑚𝑖 =
𝑗
𝑨𝑥𝑖𝑗
d d dd
# of sentences : n < 320
# of memory vectors: n
but maximum capacity
restricted to recent 50 sentences.
d…
30. 𝑢 =
𝑗
𝑩𝑞 𝑗
What memory vector to attend, based on question
𝑝𝑖 = 𝑆𝑜𝑓𝑡𝑚𝑎𝑥(𝑢 𝑇
𝑚 𝑡)
𝑥1 = Mary journeyed to the den.
𝑥2 = Mary went back to the kitchen.
𝑥3 = John journeyed to the bedroom.
𝑥4 = Mary discarded the milk.
Q: Where was the milk before the den?
Attention model on external memory
31. 질문 q에 대해서 어떤 memory vector에
얼마나 집중할 것인지
𝑝𝑖 = 𝑆𝑜𝑓𝑡𝑚𝑎𝑥(𝑢 𝑇
𝑚 𝑡)
If 𝑢 and 𝑚𝑖 is related,
𝑚𝑖 is attended in memory,
𝑝𝑖 is higher
Attention model on external memory
32. 𝑐𝑖 =
𝑗
𝑪𝑥𝑖𝑗
Purpose of each embedding matrices
B : Question
A : Input memory vector to attend
C : Output vector based on attention
Output
33. 𝑜 =
𝑗
𝑝𝑖 𝑐𝑖
slide from TaeHoon Kim
Output: 요약된 정보 𝑜 + 질문 정보 𝑢
o : response vector from memory
weighted by probability vector from input
34. Output: 요약된 정보 𝑜 + 질문 정보 𝑢
𝑜 + 𝑢
𝑜, 𝑢 둘 다 고려해 답을 도출
slide from TaeHoon Kim
o : response vector from memory
weighted by probability vector from input
u : internal state.
Input(Question)
embedding.
35. Output: 실제로 정답 단어 𝑎
𝑎=Softmax(W(o+u))
slide from TaeHoon Kim
W: d x V dimensional
40. Recurrent attention model
with external memory
uk+1
=ok
+ uk
Recurrent on memory component.
More hops, better inference on
multiple supporting facts.
41. Recurrent attention model
with external memory
Weight sharing. Constrained to ease training & reduce parameters
1) Adjacent: 𝐶 𝑖 = 𝐴𝑖+1, 𝑊 𝑇 = 𝐶 𝐾, 𝐵 = 𝐴1
2) RNN-like: 𝐴𝑖
= 𝐴 𝑗
, 𝐶 𝑖
= 𝐶 𝑗
uk+1
=ok
+ uk
(act like hidden state in RNN)
43. Recurrent attention model
with external memory
𝑎: School
John is in the school.
Jason is in the office.
John picked up the football.
Jason went to the kitchen.
Q: Where is the football?
A: School
Factoid QA with Two Supporting Facts
- MemNN: Fully Supervised with Support Facts
- MemN2N: Weakly supervised with only answer
- Supporting facts not used
SUPPORTING FACT
SUPPORTING FACT
NOT USED
NOT USED