Fine-tuning BERT for Question Answering
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This deck covers the problem of fine-tuning a pre-trained BERT model for the task of Question Answering. Check out the GluonNLP model zoo here for models and tutorials: http://gluon-nlp.mxnet.io/model_zoo/bert/index.html Slides: Thomas Delteil
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Startup Day Buenos Aires
The document discusses best practices for creating effective pitch decks to present startup ideas to investors. It provides examples of pitch decks from successful companies like Airbnb, LinkedIn, and Square that emphasize clearly explaining the problem, solution, and business model. It also stresses the importance of demonstrating growth and traction as well as having a strong founding team. Common pitfalls to avoid include not grabbing attention quickly, lacking a clear thesis, including too much unrelated content, and failing to address typical concerns about the business. The overall message is that an effective pitch focuses on problem, solution, business model, growth, and team while avoiding vague, disorganized presentations.
AWS Startup Day Santiago - Pitch essentials
The document discusses best practices for creating effective pitch decks to present startup ideas to investors. It provides examples of pitch decks from successful companies like Airbnb and LinkedIn that emphasize clearly explaining the problem, solution, and business model. It also stresses the importance of demonstrating growth and traction as well as having a strong founding team. Common pitfalls to avoid include not having a clear thesis, including too much unrelated content, and failing to address typical concerns about the business. The key is to iterate the pitch deck while focusing on conveying the core of the startup idea rather than overly focusing on design.
딥러닝@EDM페스티발 누가누가 잘 노나? :: 김태웅 - AWS Community Day 2019
The document discusses Amazon Web Services' machine learning and deep learning services. It describes services like Amazon SageMaker, Amazon Rekognition, and AWS DeepLens. It provides information on building and deploying machine learning models using these services and platforms.
Introduction to the Well-Architected Framework and Tool - SVC212 - Santa Clar...
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Transform with Cloud to drive your Future | AWS Summit Tel Aviv 2019
Innovation and agility are not for startups only, getting a competitive edge requires combining cloud-based tools and business challenges in innovative ways to drive operating efficiency, open new revenue streams, and evolve customer engagement models. In this session we will imagine the future. We will explore how to transform with Cloud to drive your future.
De un monolito a microservicios
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Rapid Prototyping with AWS - AWS Summit Sydney
In this session we will explore the role of rapid prototyping to accelerate business outcomes; diving deep on how to unlock the power of the prototyping model. Delivering outcomes in a day, a week, or a month is often challenging, so we'll explore how to define deliverables, arrange resources, run sprints, do Kanban, and produce outcomes. Hear about the AWS Rapid Innovation Prototyping methodology and other techniques to test and validate value early.
AWS Startup Day Bogotá - The Pitch
This document contains slides from a presentation given by Francisco Ruiz on creating successful pitches and avoiding common mistakes. The presentation discusses key elements to include in a pitch deck such as clearly presenting the problem, solution, business model, growth and traction, and team. It also identifies some common pitches sins to avoid like burying the main point, having an unclear thesis, including too much information, pretending there is no competition, and unrealistic assumptions about the market. The presentation emphasizes iteratively refining the pitch deck but notes the presenter's role is not as a "PowerPoint master."
AWS Startup Day Guadalajara - Pitch essentials
This document contains slides from a presentation given by Francisco Ruiz on creating successful pitches and avoiding common mistakes. The presentation discusses key elements to include in a pitch deck such as the problem, solution, business model, growth and traction, and team. It also identifies some "deadly sins" to avoid like burying the main point, lacking focus, including irrelevant information, pretending the company has no competition, and unrealistic projections. The presentation emphasizes iteratively refining the pitch deck but notes the presenter's role is not as a "PowerPoint master."
Similar to Fine-tuning BERT for Question Answering (20)
Building Event-driven Architectures with Amazon EventBridge
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Automatic Labelling and Model Tuning with Amazon SageMaker - AWS Summit Sydney
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Revving up with Reinforcement Learning by Ricardo Sueiras
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More from Apache MXNet
Recent Advances in Natural Language Processing
The document provides an overview of recent advances in natural language processing (NLP), including traditional methods like bag-of-words models and word2vec, as well as more recent contextualized word embedding techniques like ELMo and BERT. It discusses applications of NLP like text classification, language modeling, machine translation and question answering, and how different models like recurrent neural networks, convolutional neural networks, and transformer models are used.
Introduction to GluonNLP
GluonNLP is a deep learning toolkit for Natural Language Processing. These slides covers the motivation behind the creation of the toolkit and what is available in it. Go try it at https://gluon-nlp.mxnet.io!
Introduction to object tracking with Deep Learning
The document discusses object tracking using deep learning. It defines object tracking as locating an object across consecutive video frames. It notes applications in security, road safety, and entertainment. Object tracking differs from object detection in that the object class is unknown during training and tracking considers objects across time rather than individual frames. Challenges include objects leaving the screen or changing pose. The document discusses metrics for evaluating trackers, including accuracy and robustness, and surveys popular modern trackers.
Introduction to GluonCV
Introduction to the deep learning Computer Vision toolkit GluonCV and the motivations behind its creation. Go try it!
https://gluon-cv.mxnet.io
Introduction to Computer Vision
This presentation introduces the topic of computer vision, especially through the lense of Deep Learning.
Go build! https://gluon-cv.mxnet.io
Slides: Thomas Delteil
Image Segmentation: Approaches and Challenges
This slides go over the problem of deep semantic segmentation. It covers the different approaches taken, from hourglass autoencoder to pyramid networks.
Slides by Thomas Delteil
Introduction to Deep face detection and recognition
Deep Learning approaches tu face detection and recognition, state-of-art results and discussions of current limitations.
Generative Adversarial Networks (GANs) using Apache MXNet
The document provides an overview of generative adversarial networks (GANs) using Apache MXNet. It introduces GANs and deep learning concepts. It then demonstrates how to implement GANs using MXNet with examples like DCGAN. Finally, it discusses other GAN models and provides resources for using MXNet on AWS.
Deep Learning With Apache MXNet On Video by Ben Taylor @ ziff.ai
This talk will go over using Apache MXNet on video streams such as security footage from Ring, or live XBOX video data to perform inference and indexing. This can be used to classify video events, detect anomalies in normal behavior, and search. This talk will focus on using FFMPEG for feeding Apache MXNet models for fast inference throughput and performance. This talk will also discuss the difference between frame level inference, and frame buffer inference (comprehending a temporal video event).
Links to videos on the slides:
IntelAct: Winner, Visual Doom AI Competition, Full Deathmatch: https://www.youtube.com/watch?v=947bSUtuSQ0
GPU assisted call of duty processing, prep for AI auto-play: https://www.youtube.com/watch?v=gTXOYzSC_ZE
Presented at https://www.meetup.com/deep-learning-with-mxnet/events/258901722/
Using Java to deploy Deep Learning models with MXNet
The document discusses deep learning and the Apache MXNet framework. It provides an introduction to deep learning concepts like neural networks and machine learning. It then describes MXNet as an open source deep learning framework that supports multiple languages including Java. It outlines how to get started with MXNet's Java API and discusses some technical challenges around Java memory management when using deep learning models.
AI powered emotion recognition: From Inception to Production - Global AI Conf...
Introduction to Deep Learning,
Handwritten digit recognition - MLP Example
Facial Emotion Recognition - CNN, Transfer learning
MXNet Model Server
MXNet Paris Workshop - Intro To MXNet
MXNet is a flexible and efficient deep learning framework that is programmable in multiple languages and scalable across multiple GPUs and machines. It originated from the DMLC community and is now an Apache incubating project. MXNet provides low-level NDArray and Symbol APIs as well as high-level Gluon APIs and has additional toolkits like GluonCV for computer vision tasks. MXNet supports distributed training across multiple machines using parameter servers and can serve trained models for low-latency inference using the MXNet Model Server.
Apache MXNet ODSC West 2018
This document provides an overview of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It discusses how RNNs can be used for sequence modeling tasks like sentiment analysis, machine translation, and speech recognition by incorporating context or memory from previous steps. LSTMs are presented as an improvement over basic RNNs that can learn long-term dependencies in sequences using forget gates, input gates, and output gates to control the flow of information through the network.
DeepLearning001&ApacheMXNetWithSparkForInference-ACNA2018
What is Deep Learning
Rise of Deep Learning
Phases of Deep Learning - Training and Inference
AI & Limitations of Deep Learning
Apache MXNet History, Apache MXNet concepts
How to use Apache MXNet and Spark together for Distributed Inference.
Apache MXNet EcoSystem - ACNA2018
The document discusses Apache MXNet, an open-source deep learning framework. It provides an overview of MXNet's history and key features, including support for multiple programming languages, an ecosystem of tools like GluonCV and GluonNLP, and model serving capabilities. It also describes MXNet's use of ONNX for model interchange, integration with Keras, and performance optimization using technologies like CUDA, MKL, and TVM. The document highlights MXNet's large community and adoption by customers.
ONNX and Edge Deployments
In this talk ONNX (Open Neural Network eXchange) is introduced, and the ONNX Model Zoo is used as the base for fine-tuning with AWS SageMaker and Apache MXNet's Gluon API. With a fine-tuned model trained on Caltech101, AWS GreenGrass is discussed for edge deployments and the TVM Stack is suggested as a method for optimising the inference of models on edge devices.
Presented by: Thom Lane at Linaro Connect Vancouver 2018 on 19th September 2018.
Distributed Inference with MXNet and Spark
Deep Learning has become ubiquitous with abundance of data, commoditization of compute and storage. Pre-trained models are readily available for many use-cases. Distributed Inference has many applications such as pre-computing results offline, backfilling historic data with predictions from state-of-the-art models, etc.,. Inference on large scale datasets comes with many challenges prevalent in distributed data processing. This presentation will show how to efficiently run deep learning prediction on large data sets, leveraging Apache Spark and Apache MXNet (incubating).
Multivariate Time Series
This presentation describes two major papers in multi-variate time-series using deep neural networks. The first paper, DeepAR was developed at Amazon to deal with forecasting of millions of items where the same model can be applied to millions of products. DeepAR is implemented as a built-in algorithm of Amazon SageMaker. Code example is provided.
The second paper, Long- and Short-Term Temporal Patterns with Deep Neural Networks is developed at CMU and introduces a novel way to detect both short term and long term seasonality in data through introduction of skip-rnn.
A Gluon implementation of the paper is provided in the presentation.
AI On the Edge: Model Compression
Inference on edge has an ever increasing performance for companies and thus it is crucial to be able to make models smaller. Compressing models can be loss-less or can result in loss of accuracy. This presentation provides a survey of compression techniques for deep learning models. It then describes different architectures of AWS IoT/Green Grass to combine on-device inference and GPU inference in a hub model. Additionally the presentation introduces MXNet, which has small footprint and efficient both for inference and training in distributed settings.
Building Content Recommendation Systems using MXNet Gluon
Netflix competition triggered a flurry of research for recommendation engines. This presentation provides a survey of techniques and models for creating a recommender system. The presentation covers Matrix Factorisation, Factorisation Machines, Distributed Factorisation Machines, and DSSM networks as well provide code examples for developing a Matrix Factorisation in Gluon. At the end the presentation provides tips and tricks for large-scale, realtime recommender engines.
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Introduction to object tracking with Deep Learning
Introduction to object tracking with Deep Learning
Introduction to Deep face detection and recognition
Introduction to Deep face detection and recognition
Generative Adversarial Networks (GANs) using Apache MXNet
Generative Adversarial Networks (GANs) using Apache MXNet
Deep Learning With Apache MXNet On Video by Ben Taylor @ ziff.ai
Deep Learning With Apache MXNet On Video by Ben Taylor @ ziff.ai
Using Java to deploy Deep Learning models with MXNet
Using Java to deploy Deep Learning models with MXNet
AI powered emotion recognition: From Inception to Production - Global AI Conf...
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DeepLearning001&ApacheMXNetWithSparkForInference-ACNA2018
DeepLearning001&ApacheMXNetWithSparkForInference-ACNA2018
Building Content Recommendation Systems using MXNet Gluon
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Recently uploaded
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...Abdul Wali Khan University Mardan,kP,Pakistan
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skillsImmersive Learning That Works: Research Grounding and Paths Forward
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Micronuclei test.M.sc.zoology.fisheries.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Compexometric titration/Chelatorphy titration/chelating titration
Classification
Metal ion ion indicators
Masking and demasking reagents
Estimation of Magnisium sulphate
Calcium gluconate
Complexometric Titration/ chelatometry titration/chelating titration, introduction, Types-
1.Direct Titration
2.Back Titration
3.Replacement Titration
4.Indirect Titration
Masking agent, Demasking agents
formation of complex
comparition between masking and demasking agents,
Indicators/Metal ion indicators/ Metallochromic indicators/pM indicators,
Visual Technique,PM indicators (metallochromic), Indicators of pH, Redox Indicators
Instrumental Techniques-Photometry
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Miscellaneous methods.
Complex titration with EDTA.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
waterlessdyeingtechnolgyusing carbon dioxide chemicalspdf
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
The debris of the ‘last major merger’ is dynamically young
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/8.Isolation of pure cultures and preservation of cultures.pdf
Isolation of pure culture, its various method.
Equivariant neural networks and representation theory
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Authoring a personal GPT for your research and practice: How we created the Q...
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
The binding of cosmological structures by massless topological defects
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Thornton ESPP slides UK WW Network 4_6_24.pdf
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
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aziz sancar nobel prize winner: from mardin to nobel
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Compexometric titration/Chelatorphy titration/chelating titration
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The debris of the ‘last major merger’ is dynamically young
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8.Isolation of pure cultures and preservation of cultures.pdf
8.Isolation of pure cultures and preservation of cultures.pdf
Equivariant neural networks and representation theory
Equivariant neural networks and representation theory
Authoring a personal GPT for your research and practice: How we created the Q...
Authoring a personal GPT for your research and practice: How we created the Q...
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
The binding of cosmological structures by massless topological defects
The binding of cosmological structures by massless topological defects
Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless Reproducibility
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Fine-tuning BERT for Question Answering
- 1. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Automatic Question Answering through BERT fine-tuning
- 2. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Question Answering task Q: _______________ ? A: _______ Or A: impossible
- 3. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark SQuAD: The Stanford Question Answering Dataset SQuAD v1.1: Single good answer SQuAD v2: One ore more good answers and questions that are impossible to answer
- 4. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark SQuAD: The Stanford Question Answering Dataset Partial answer query answer
- 5. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark SQuAD: The Stanford Question Answering Dataset https://rajpurkar.github.io/SQuAD-explorer/
- 6. Bi-Directional Attention Flow for machine comprehension (BiDAF), Seo et al. 2018
- 7. (refresher) BERT (Devlin et al. 18): Pre-training INPUT WordPieces Embeddings Sentence Embeddings Position Embeddings Learned during the (pre)training process MASK EMASK In pre-training 15% of the input tokens are masked for the masked LM task There is also a sentence similarity task
- 8. http://jalammar.github.io/illustrated-bert/ (refresher) BERT (Devlin et al. 18): Pre-training
- 9. http://jalammar.github.io/illustrated-bert/ (refresher) BERT (Devlin et al. 18): Pre-training
- 10. Training for Question Answering task: (Modified from: original paper) Fine-tuning Add small weight matrix W: 𝒉 𝐿 𝑖 𝑾 𝑆𝑃𝐴𝑁_𝑆𝑇𝐴𝑅𝑇_𝐸𝑁𝐷 BERT (Devlin et al. 18): Fine-Tuning for QA impossible = start[CLS]+end[CLS] Modified network in Gluon (Bert + Dense Layer) max(𝒔𝒕𝒂𝒓𝒕) + max(𝒆𝒏𝒅) − (𝑠𝑡𝑎𝑟𝑡 𝐶𝐿𝑆 + 𝑒𝑛𝑑 𝐶𝐿𝑆 ) < 𝑖𝑚𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒_𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 Question is impossible to answer if:
- 11. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Lab
- 12. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Go build! • http://gluon-nlp.mxnet.io/ Get help: • https://discuss.mxnet.io/
Editor's Notes
- First call deck for a high level introduction to Apache MXNet.
- Task 1: Mask language model (MLM) Task 2: Next sentence prediction Note that the first token is always forced to be [CLS] — a placeholder that will be used later for prediction in downstream tasks.