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.
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.
Word Embeddings, Application of Sequence modelling, Recurrent neural network , drawback of recurrent neural networks, gated recurrent unit, long short term memory unit, Attention Mechanism
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 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.
Word Embeddings, Application of Sequence modelling, Recurrent neural network , drawback of recurrent neural networks, gated recurrent unit, long short term memory unit, Attention Mechanism
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 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/
Introduction For seq2seq(sequence to sequence) and RNNHye-min Ahn
This is my slides for introducing sequence to sequence model and Recurrent Neural Network(RNN) to my laboratory colleagues.
Hyemin Ahn, @CPSLAB, Seoul National University (SNU)
Transformer modality is an established architecture in natural language processing that utilizes a framework of self-attention with a deep learning approach.
This presentation was delivered under the mentorship of Mr. Mukunthan Tharmakulasingam (University of Surrey, UK), as a part of the ScholarX program from Sustainable Education Foundation.
Brief introduction on attention mechanism and its application in neural machine translation, especially in transformer, where attention was used to remove RNNs completely from NMT.
Deep Learning: Recurrent Neural Network (Chapter 10) Larry Guo
This Material is an in_depth study report of Recurrent Neural Network (RNN)
Material mainly from Deep Learning Book Bible, http://www.deeplearningbook.org/
Topics: Briefing, Theory Proof, Variation, Gated RNNN Intuition. Real World Application
Application (CNN+RNN on SVHN)
Also a video (In Chinese)
https://www.youtube.com/watch?v=p6xzPqRd46w
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
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 is a presentation I gave as a short overview of LSTMs. The slides are accompanied by two examples which apply LSTMs to Time Series data. Examples were implemented using Keras. See links in slide pack.
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.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. 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. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels 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. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
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.
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.
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
Building a Neural Machine Translation System From ScratchNatasha Latysheva
Human languages are complex, diverse and riddled with exceptions – translating between different languages is therefore a highly challenging technical problem. Deep learning approaches have proved powerful in modelling the intricacies of language, and have surpassed all statistics-based methods for automated translation. This session begins with an introduction to the problem of machine translation and discusses the two dominant neural architectures for solving it – recurrent neural networks and transformers. A practical overview of the workflow involved in training, optimising and adapting a competitive neural machine translation system is provided. Attendees will gain an understanding of the internal workings and capabilities of state-of-the-art systems for automatic translation, as well as an appreciation of the key challenges and open problems in the field.
Introduction For seq2seq(sequence to sequence) and RNNHye-min Ahn
This is my slides for introducing sequence to sequence model and Recurrent Neural Network(RNN) to my laboratory colleagues.
Hyemin Ahn, @CPSLAB, Seoul National University (SNU)
Transformer modality is an established architecture in natural language processing that utilizes a framework of self-attention with a deep learning approach.
This presentation was delivered under the mentorship of Mr. Mukunthan Tharmakulasingam (University of Surrey, UK), as a part of the ScholarX program from Sustainable Education Foundation.
Brief introduction on attention mechanism and its application in neural machine translation, especially in transformer, where attention was used to remove RNNs completely from NMT.
Deep Learning: Recurrent Neural Network (Chapter 10) Larry Guo
This Material is an in_depth study report of Recurrent Neural Network (RNN)
Material mainly from Deep Learning Book Bible, http://www.deeplearningbook.org/
Topics: Briefing, Theory Proof, Variation, Gated RNNN Intuition. Real World Application
Application (CNN+RNN on SVHN)
Also a video (In Chinese)
https://www.youtube.com/watch?v=p6xzPqRd46w
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
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 is a presentation I gave as a short overview of LSTMs. The slides are accompanied by two examples which apply LSTMs to Time Series data. Examples were implemented using Keras. See links in slide pack.
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.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. 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. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels 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. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
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.
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.
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
Building a Neural Machine Translation System From ScratchNatasha Latysheva
Human languages are complex, diverse and riddled with exceptions – translating between different languages is therefore a highly challenging technical problem. Deep learning approaches have proved powerful in modelling the intricacies of language, and have surpassed all statistics-based methods for automated translation. This session begins with an introduction to the problem of machine translation and discusses the two dominant neural architectures for solving it – recurrent neural networks and transformers. A practical overview of the workflow involved in training, optimising and adapting a competitive neural machine translation system is provided. Attendees will gain an understanding of the internal workings and capabilities of state-of-the-art systems for automatic translation, as well as an appreciation of the key challenges and open problems in the field.
Deep Learning: Evolution of ML from Statistical to Brain-like Computing- Data...Impetus Technologies
Presentation on 'Deep Learning: Evolution of ML from Statistical to Brain-like Computing'
Speaker- Dr. Vijay Srinivas Agneeswaran,Director, Big Data Labs, Impetus
The main objective of the presentation is to give an overview of our cutting edge work on realizing distributed deep learning networks over GraphLab. The objectives can be summarized as below:
- First-hand experience and insights into implementation of distributed deep learning networks.
- Thorough view of GraphLab (including descriptions of code) and the extensions required to implement these networks.
- Details of how the extensions were realized/implemented in GraphLab source – they have been submitted to the community for evaluation.
- Arrhythmia detection use case as an application of the large scale distributed deep learning network.
Upload photos Copy this Meetup
Things we will discuss are
1.Introduction of Machine learning and deep learning.
2.Applications of ML and DL.
3.Various learning algorithms of ML and DL.
4.Quick introduction of open source solutions for all programming languages.
5.Finally A broad picture of what you can do with Deep learning to this tech world.
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
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
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We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. Overview
I. Introduction to sequence modelling
II. Quick neural network review
• Feed-forward networks
III. Recurrent neural networks
• From feed-forward networks to recurrence
• RNNs with gating mechanisms
IV. Practical: Building a language model for Game of Thrones
V. Components of state-of-the-art RNN models
• Encoder-decoder models
• Bidirectionality
• Attention
VI. Transformers and self-attention
3. Speaker Intro
• Welocalize
• We provide language services
• Fairly large, by revenue 8th largest globally,
4th largest US. 1500+ employees.
• Lots of localisation (translation)
• International marketing, site optimisation
• NLP engineering team
• 14 people remote across US, Ireland, UK,
Germany, China
• Various NLP things: machine translation,
text-to-speech, NER, sentiment, topics,
classification, etc.
12. Why do we need fancy methods to
model sequences?
• Say we are training a translation
model, English->French
• ”The cat is black” to “Le chat is
noir”
• Could in theory use a feed-
forward network to translate
word-by-word
13. Why do we need fancy methods?
• A feed-forward network treats
time steps as completely
independent
• Even in this simple 1-to-1
correspondence example, things
are broken
• How you translate “black” depends
on noun gender (“noir” vs. “noire”)
• How you translate “The” also
depends on gender (“Le” vs. “La”)
• More generally, getting the
translation right requires context
14. Why do we need fancy methods?
• We need a way for the network
to remember information from
previous time steps
15. Recurrent neural networks
• Extremely popular way of modelling
sequential data
• Process data one time step at a
time, while updating a running
internal hidden state
20. Standard FF network to RNN
• At each time step, RNN
passes on its activations
from previous time step
• In theory all the way back
to the first time step
22. Standard FF network to RNN
• So you can say this is a
form of memory
• Cell hidden state
transferred
• Basis for RNNs
remembering context
23. Memory problems
• Basic RNNs not great at
long-term dependencies
but plenty of ways to
improve this
• Information gating
mechanisms
• Condensing input using
encoders
24. Gating mechanisms
• Gates regulate the flow of
information
• Very helpful - basic RNN cells not really
used anymore. Responsible for recent
RNN popularity.
• Add explicit mechanisms to remember
information and forget information
• Why use gates?
• Helps you learn long-term
dependencies
• Not all time points are equally relevant
– not everything has to be remembered
• Speeds up training/convergence
25. Gated recurrent
units (GRUs)
• GRUs were developed later
than LSTMs but are simpler
• Motivation is to get the main
benefits of LSTMs but with less
computation
• Reset gate: Mechanism to
decide when to remember vs.
forget/reset previous
information (hidden state)
• Update gate: Mechanism to
decide when to update
hidden state
26. GRU mechanics
• Reset gate controls how
much past info we use
• Rt = 0 means we are resetting
our RNN, not using any
previous information
• Rt = 1 means we use all of
previous information (back to
our normal vanilla RNN)
27. GRU mechanics
• Update gate controls whether
we bother updating our
hidden state using new
information
• Zt = 1 means you’re not
updating, you’re just using
previous hidden state
• Zt = 0 means you’re updating as
much as possible
28. LSTM mechanics
• LSTMs add a memory unit to
further control the flow of
information through the cell
• Also whereas GRUs have 2
gates, an LSTM cell has 3
gates:
• An input gate – should I ignore
or consider the input?
• A forget gate – should I keep
or throw away the information
in memory?
• An output gate – how should I
use input, hidden state and
memory to output my next
hidden state?
29. GRUs vs. LSTMs
• GRUs are simpler + train
faster
• LSTMs more popular – can
give slightly better
performance, but GRU
performance often on par
• LSTMs would in theory
outperform GRUs in tasks
requiring very long-range
modelling
36. Encoder-Decoder architectures
• Tends to work a lot
better than using a
single sequence-to-
sequence RNNs to
produce an output
for each input step
• You often need to
see the whole
sequence before
knowing what to
output
37.
38. Bidirectionality in RNN encoder-decoders
• For the encoder,
bidirectional RNNs
(BRNNs) often used
• BRNNs read the
input sequences
forwards and
backwards
41. The problem with RNN encoder-decoders
• Serious information
bottleneck
• Condense input
sequence down to a
small vector?!
• Memorise long
sequence + regurgitate
• Not how humans work
• Long computation
paths
42. Attention concept
• Has been very influential in
deep learning
• Originally developed for
MT (Bahdanau, 2014)
• As you’re producing your
output sequence, maybe
not every part of your input
is as equally relevant
• Image captioning example
Lu et al. 2017. Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning.
43. Attention intuition
• Attention allows the
network to refer back
to the input
sequence, instead of
forcing it to encode
all information into
one fixed-length
vector
44. • Encoder: Used BRNN
to compute rich set of
features about source
words and their
surrounding words
• Decoder is asked to
choose which hidden
states to use and
ignore
• Weighted sum of
hidden states used to
predict the next word
Attention intuition
45. • Decoder RNN uses
attention parameters
to decide how much
to pay attention to
different parts of the
input
• Allows the model to
amplify the signal
from relevant parts of
the input sequence
• This improves
modelling
Attention intuition
46. Main benefits
• Encoder passes a lot
more data to
the decoder
• Not just last hidden
state
• Passes all hidden states
at every time step
• Computation path
problem: relevant
information is now
closer by
47. Summary so far
• Sequence modelling
• Recurrent neural
networks
• Some key components
of SOTA RNN-based
models:
• Gating mechanisms
(GRUs and LSTMs)
• Encoder-decoders
• Bidirectional encoding
• Attention
49. Transformers are taking over NLP
• Translation, language
models, question
answering, summarisation,
etc.
• Some of the best word
embeddings are based on
Transformers
• BERT, ELmO, OpenAI GPT-2
models
50. A single Transformer encoder block
• No recurrence, no convolutions
• “Attention is all you need” paper
• The core concept is the self-
attention mechanism
• Much more parallelisable than
RNN-based models, which
means faster training
51. Self-attention is a
sequence-to-sequence
operation
• At the highest level – self-
attention takes t input
vectors and outputs t
output vectors
• Take input embedding for
“the” and update it by
incorporating in
information from its
context
53. • Each output vector
is a weighted sum
of the input vectors
• But all of these
weights are
different
54. These are not learned weights in the
traditional neural network sense
• The weights are
calculated by taking
dot products
• Can use different
functions over input
58. Attention weight matrix
• The dot product can be
anything (negative infinity to
positive infinity)
• We normalise by length
• We softmax this so that the
weights are positive values
summing to 1
• Attention weight matrix
summarises relationship
between words
• Because dot products capture
similarity between vectors
59. Multi-headed attention
• Attention weight matrix
captures relationship
between words
• But there’s many
different ways words can
be related
• And which ones you want
to capture depends on
your task
• Different attention heads
learn different relations
between word pairs
Img source
60. Difference to RNNs
• Whereas RNNs updates context
token-by-token by updating
internal hidden state, self-
attention captures context by
updating all word representations
simultaneously
• Lower computational complexity,
scales better with more data
• More parallelisable = faster
training
61. Connecting all
these concepts
• “Useful” input representations are
learned
• “Useful” weights for transforming
input vectors are learned
• These quantities should produce
“useful” dot products
• That lead to “useful” updated input
vectors
• That lead to “useful” input to the
feed-forward network layer
• … etc. … that eventually lead to
lower overall loss on the training set
62. Summary
I. Introduction to sequence modelling
II. Quick neural network review
• How a single neuron functions
• Feed-forward networks
III. Recurrent neural networks
• From feed-forward networks to recurrence
• RNNs with gating mechanisms
IV. Practical: Building a language model for Game of Thrones
V. Components of state-of-the-art RNN models
• Encoder-decoder models
• Bidirectionality
• Attention
VI. Transformers and self-attention
63. Further Reading
• More accessible: Andrew Ng
Sequence Course on Coursera
• https://www.coursera.org/learn/nlp-
sequence-models
• More technical: Deep Learning book
by Goodfellow et al.
• https://www.deeplearningbook.org/cont
ents/rnn.html
• Also: Alex Smola Berkeley Lectures
• https://www.youtube.com/user/smolix/vi
deos
64. Just for fun
• Talk to transformer
• https://talktotransformer.com/
• Using OpenAI’s “too
dangerous to release” GPT-
2 language model
67. Sequences in natural language
• Sequence modelling very popular in
NLP because language is sequential by
nature
• Text
• Sequences of words
• Sequences of characters
• We process text sequentially, though in
principle could see all words at once
• Speech
• Sequence of amplitudes over time
• Frequency spectrogram over time
• Extracted frequency features over time
Img source
68. Sequences in biology
• Genomics, DNA and
RNA sequences
• Proteomics, protein
sequences,
structural biology
• Trying to represent
sequences in some
way, or predict some
function or
association of the
sequence
Img source
69. Sequences in finance
• Lots of time series data
• Numerical sequences (stocks,
indices)
• Lots of forecasting work –
predicting the future (trading
strategies)
• Deep learning for these
sequences perhaps not as
popular as you might think
• Quite well-developed methods
based on classical statistics,
interpretability important
Img source
Img source
70. Single neuron computation
• What computation is
happening inside 1
neuron?
• If you understand how 1
neuron computes output
given input, it’s a small
step to understand how an
entire network computes
output given input
71. Single neuron computation
• What computation is
happening inside 1
neuron?
• If you understand how 1
neuron computes output
given input, it’s a small
step to understand how an
entire network computes
output given input
72. Perceptrons
• Modelling a binary outcome using
binary input features
• Should I have a cup of tea?
• 0 = no
• 1 = yes
• Three features with 1 weight each:
• Do they have Earl Grey?
• earl_grey, 𝑤" = 3
• Have I just had a cup of tea?
• already_had, 𝑤# =-1
• Can I get it to go?
• to_go, 𝑤$ =2
73. Perceptrons
• Modelling a binary outcome using
binary input features
• Should I have a cup of tea?
• 0 = no
• 1 = yes
• Three features with 1 weight each:
• Do they have Earl Grey?
• earl_grey, 𝑤" = 3
• Have I just had a cup of tea?
• already_had, 𝑤# =-1
• Can I get it to go?
• to_go, 𝑤$ =2
74. Perceptrons
• Here weights are
cherry-picked, but
perceptrons learn these
weights automatically
from training data by
shifting parameters to
minimise error
75. Perceptrons
• Formalising the perceptron
calculation
• Instead of a threshold, more
common to see a bias term
• Instead of writing out the
sums using sigma notation,
more common to see dot
products.
• Vectorisation for efficiency
• Here, I manually chose these
values – but given a dataset of
past inputs/outputs, you could
learn the optimal parameter
values
76. Perceptrons
• Formalising the
perceptron calculation
• Instead of a threshold,
more common to see a
bias term
• Instead of writing out
the sums using sigma
notation, more common
to see dot products.
• Vectorisation for
efficiency
77. Sigmoid neurons
• Want to handle continuous
values
• Where input can be
something other than just 0 or
1
• Where output can be
something other than just 0 or
1
• We put the weighted sum of
inputs through an activation
function
• Sigmoid or logistic function
78. Sigmoid neurons
• The sigmoid function is
basically a smoothed out
perceptron!
• Output no longer a
sudden jump
• It’s the smoothness of the
function that we care
about
Img source
79. Activation functions
• Which activation function
to use?
• Heuristics based on
experiments, not proof-
based
Img source
81. Training on big window sizes
• How much of window size? On very long sequence, unrolled
RNN becomes a very deep network
• Same problems with vanishing/exploding gradients as normal
networks
• And takes a longer time to train
• The normal tricks can help – good initialization of parameters, non-
saturating activation functions, gradient clipping, batch norm
• Training over a limited number of steps – truncated
backpropagation through time
82. LSTM mechanics
• Input, forget, output gates are
little neural networks within the
cell
• Memory being updated via
forget gate and candidate
memory
• Hidden state being updated by
output gate, which weighs up all
information
83.
84. Query, Key, and Value transformations
• Notice that we are using
each input vector on 3
separate occasions
• E.g. vector x2
1. To take dot products
with each other input
vector when calculating
y2
2. In dot products with
other output vectors (y1,
y3, y4) are calculated
3. And in the weighted
sum to produce output
vector y2
85. Query, Key, and Value transformations
• To model these 3
different functions for
each input vector, and
give the model extra
expressivity and
flexibility, we are going
to modify the input
vectors
• Apply simple linear
transformations