Intro to deep learning_ matteo alberti
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1. The document discusses the evolution of neural networks from perceptrons to multi-layer perceptrons (MLPs), allowing for more complex decision functions to be modeled through stacking layers of neurons. 2. It describes common activation functions like sigmoid and ReLU, and how backpropagation and gradient descent are used to optimize neural network weights. 3. Deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs) including LSTMs, word embeddings, and autoencoders are applied to tasks in computer vision, natural language processing, reinforcement learning, and finance.
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Basic models of ANN
Linearly separable functions
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Seminar given to instructors at Misr International University. Contains slides on "neural machine translation for translators", but also other information about teaching translation technologies at the Universitat d'Alacant.
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The document discusses natural language processing (NLP) and deep learning. It provides an overview of NLP, including common techniques like tokenization and part-of-speech tagging. Deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have achieved state-of-the-art results on NLP tasks. Overall, NLP is a rapidly evolving field that has the potential to revolutionize human-computer interaction.
Tsinghua invited talk_zhou_xing_v2r0
Invited talk at Tsinghua University on "Applications of Deep Neural Network". As the tech. lead of deep learning task force at NIO USA INC, I was invited to give this colloquium talk on general applications of deep neural network.
Alberto Massidda - Images and words: mechanics of automated captioning with n...
Image captioning is the process of generating textual description of an image. It uses both Natural Language Processing and Computer Vision to generate the captions. Like in the notorious “finger pointing to the moon”, automated image captioning requires the ability to discern what it’s really going on in a scene and generate a fluent description for the act taking place. In this talk we present the underlying mechanics to the object detection and language generation using Convolutional and Recurrent Neural Networks.
What Deep Learning Means for Artificial Intelligence
Discusses how deep learning has advanced artificial intelligence and contrasts the approach with symbol-based systems.
Convolutional Neural Networks for Sentence Classification
This document discusses convolutional neural networks for sentence classification. It begins with introducing automated text classification and definitions of key concepts like deep learning, language models, neural language models, and convolutional neural networks. It then discusses various datasets used for evaluating models. Several CNN models are described and their results on different datasets are presented, outperforming other methods. The document concludes by showing the most similar words learned by static and non-static CNN channels.
nlp dl 1.pdf
This document discusses a deep learning course at Carnegie Mellon University for fall 2016 that covers topics like popularization of backpropagation for training neural networks, unsupervised pre-training of deep networks, and convolutional neural networks winning the ImageNet competition in 2012 leading to increased interest in deep learning research. It also shows the architecture of a convolutional neural network and how it is split across two GPUs during training.
Deep Learning - The Past, Present and Future of Artificial Intelligence
The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.
BASIC CONCEPT OF DEEP LEARNING.pptx
Deep learning is a type of machine learning that uses neural networks with multiple layers to progressively extract higher-level features from raw input. Lower layers may identify simple elements like edges in images while higher layers identify more complex concepts like digits or faces. Deep learning models learn representations of data by using backpropagation to indicate how a machine should change its internal parameters to best fit the training data. Convolutional neural networks are a type of deep learning model that use convolution operations to identify patterns in grid-like data like images or text.
Deep learning lecture - part 1 (basics, CNN)
This presentation is a lecture with the Deep Learning book. (Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. MIT press, 2017) It contains the basics of deep learning and theories about the convolutional neural network.
A tutorial on deep learning at icml 2013
This document provides an overview of deep learning presented by Yann LeCun and Marc'Aurelio Ranzato at an ICML tutorial in 2013. It discusses how deep learning learns hierarchical representations through multiple stages of non-linear feature transformations, inspired by the hierarchical structure of the mammalian visual cortex. It also compares different types of deep learning architectures and training protocols.
EXPERIMENTS ON DIFFERENT RECURRENT NEURAL NETWORKS FOR ENGLISH-HINDI MACHINE ...
Recurrent Neural Networks are a type of Artificial Neural Networks which are adept at dealing
with problems which have a temporal aspect to them. These networks exhibit dynamic
properties due to their recurrent connections. Most of the advances in deep learning employ
some form of Recurrent Neural Networks for their model architecture. RNN's have proven to be
an effective technique in applications like computer vision and natural language processing. In
this paper, we demonstrate the effectiveness of RNNs for the task of English to Hindi Machine
Translation. We perform experiments using different neural network architectures - employing
Gated Recurrent Units, Long Short Term Memory Units and Attention Mechanism and report
the results for each architecture. Our results show a substantial increase in translation quality
over Rule-Based and Statistical Machine Translation approaches.
lecun-01.ppt
This document discusses deep learning and machine learning challenges. It addresses how to build internal representations of the world that capture relevant information and eliminate irrelevant variability. It also discusses how hierarchical representations can be learned from data in a supervised manner using deep learning architectures like convolutional neural networks. These networks are inspired by the hierarchical structure of the visual cortex and can learn invariant visual features through multiple stages of convolution and pooling operations.
My invited talk at the 2018 Annual Meeting of SIAM (Society of Industrial and...
This is a slightly expanded version of the talk I gave at the 2018 ISMP (International Symposium on Mathematical Programming). This SIAM talk has some more introductory material than the ISMP talk.
Temporal Hypermap Theory and Application
The document summarizes research on modeling multiple sequence processing using an unsupervised neural network approach based on the Hypermap Model. Key points:
- The researcher extends previous models to handle complex sequences with repeating subsequences and multiple sequences occurring together without interference.
- Modifications include incorporating short-term memory to dynamically encode time-varying sequence context and inhibitory links to enable competitive queuing during recall.
- Experimental evaluation shows the network can correctly recall sequences using partial context and when sequences overlap.
- Future work aims to model the transition from single-word to two-word child speech and incorporate temporal processing of multimodal inputs like gestures.
SoftComputing.pdf
The document discusses soft computing and its components. Soft computing aims to solve real-world problems that are difficult for traditional hard computing techniques. It uses fuzzy logic, neural networks, evolutionary computation and other inexact methods. Unlike hard computing which requires precise modeling, soft computing is tolerant of imprecision, uncertainty and approximation. It is well-suited for problems where ideal models are not available, such as pattern recognition, forecasting and control systems. Some key applications of soft computing mentioned include handwriting recognition, image processing, data mining and control systems.
Top 10 deep learning algorithms you should know in
In scientific computing, deep learning has gained immense popularity, and its algorithms are commonly used by companies that solve complex problems.
Similar to Intro to deep learning_ matteo alberti (20)
What Deep Learning Means for Artificial Intelligence
What Deep Learning Means for Artificial Intelligence
Understanding Deep Learning & Parameter Tuning with MXnet, H2o Package in R
Understanding Deep Learning & Parameter Tuning with MXnet, H2o Package in R
Alberto Massidda - Images and words: mechanics of automated captioning with n...
Alberto Massidda - Images and words: mechanics of automated captioning with n...
What Deep Learning Means for Artificial Intelligence
What Deep Learning Means for Artificial Intelligence
Convolutional Neural Networks for Sentence Classification
Convolutional Neural Networks for Sentence Classification
Deep Learning - The Past, Present and Future of Artificial Intelligence
Deep Learning - The Past, Present and Future of Artificial Intelligence
EXPERIMENTS ON DIFFERENT RECURRENT NEURAL NETWORKS FOR ENGLISH-HINDI MACHINE ...
EXPERIMENTS ON DIFFERENT RECURRENT NEURAL NETWORKS FOR ENGLISH-HINDI MACHINE ...
My invited talk at the 2018 Annual Meeting of SIAM (Society of Industrial and...
My invited talk at the 2018 Annual Meeting of SIAM (Society of Industrial and...
Top 10 deep learning algorithms you should know in
Top 10 deep learning algorithms you should know in
More from Deep Learning Italia
Machine Learning driven Quantum Optimization for Marketing
The document discusses using machine learning and quantum computing to optimize marketing campaigns. Specifically, it details using transformers to predict user appreciation and activation for different incentive levels. A quantum annealer is then used to solve the NP-hard problem of allocating incentives to users to maximize appreciation within a cost target. Three A/B tests are conducted, with the second hitting targets and the third having uncertain results. Overall the approach shows potential to optimize large marketing budgets.
Modelli linguistici da Eliza a ChatGPT P roblemi , fraintendimenti e prospettive
Modelli linguistici da Eliza a ChatGPT P roblemi , fraintendimenti e prospettiveDeep Learning Italia
Scopri con il dott. Paolo Caressa i modelli linguistici, da
Eliza a ChatGPT. Vieni a trovarci su deeplearningitalia.com per maggiori contenutiTransformers In Vision From Zero to Hero (DLI).pptx
This document provides an overview of transformers in computer vision. It discusses how transformers were originally developed for natural language processing using attention mechanisms instead of recurrent connections. Vision transformers apply this approach to images by treating patches as tokens and using self-attention. Early vision transformers achieved strong results on image classification tasks. Recent developments include Swin transformers which use shifted windows to incorporate positional information, and models that combine convolutional and transformer architectures. Transformers are also being applied to video understanding tasks. The document explores different transformer architectures and applications of vision transformers.
Meetup Luglio - Operations Research.pdf
1. The document discusses operations research, which uses mathematical modeling to help make better decisions.
2. Operations research tools like mathematical programming and decomposition methods can be used to solve large, complex problems and scale to practical applications.
3. Decomposition methods break large problems into smaller subproblems that can be solved independently to find good feasible solutions for the original problem.
Meetup Giugno - c-ResUNET.pdf
This document describes a deep learning approach called c-ResUnet for counting cells in fluorescent microscopy images. It discusses fluorescent microscopy imaging techniques and applications in life sciences. It then introduces the Fluorescent Neuronal Cells dataset and challenges in counting cells, such as class imbalance, overcrowding, and noise. The c-ResUnet model is presented, which uses a convolutional neural network with residual blocks for semantic segmentation. Experiments show that c-ResUnet outperforms other architectures and achieves performance close to human experts on this dataset through the use of weight maps and oversampling artifacts during training. Both qualitative and quantitative evaluations demonstrate the effectiveness of c-ResUnet for automated cell counting.
MEETUP Maggio - Team Automata
Negli ultimi anni la robotica sta finalmente uscendo dalle fabbriche per popolare le città in cui viviamo. Auto a guida autonoma, droni e robot per la consegna di cibo, quadrupedi per la sorveglianza delle strade: questi sono solo alcuni esempi di ciò che si può trovare già oggi in molti quartieri nel mondo. La rivoluzione generata dal deep learning a partire dal 2012 è soltanto uno degli elementi di questa diffusione, che si fonda anche su complesse dinamiche di mercato e decenni di ricerca precedente nell'ambito dei sistemi robotici, dal punto di vista sia software che hardware. A che punto siamo arrivati? Quali sono le sfide che i ricercatori e le aziende devono affrontare oggi in questo settore? Quali sono i meccanismi di mercato che guidano lo sviluppo di questi sistemi? In questo talk risponderemo a queste domande, in modo da fornire una panoramica completa sullo stato dell'arte nella robotica mobile urbana.
MEETUP APRILE - Ganomaly - Anomaly Detection.pdf
L’identificazione di anomalie è una tematica sempre più popolare che viene affrontata su più fronti. In generale, l’anomalia rappresenta un’entità, un evento o una caratteristica che non risulta conforme allo standard di normalità. Le anomalie sono un ostacolo, a volte anche pericoloso come per esempio nella sicurezza informatica, in cui l’intrusione di persone non fidate all’interno di sistemi informatici può diventare critico per un’azienda o un’istituzione; in industrie invece, le anomalie possono danneggiare la qualità dei prodotti, causando pesanti perdite in termini economici. Per questo motivo vengono ideate numerose tecniche che permettono di riconoscere le anomalie e ridurre i pericoli, i danni da esse causate o semplicemente per monitorare la qualità e gestire la manutenzione.
In un contesto di immagini, il riconoscimento di anomalie è un problema di Computer Vision. Esistono metodi di ricostruzione come gli Autoencoder o metodi generativi come le GAN che si occupano di risolvere tale problema. Tra i modelli che si basano sulle GAN, chiamati GAN-based, si distingue il modello Ganomaly: esso permette di rilevare se un’immagine sia anomala.
Sulla base di quest’ultimo, nascono Patch-Ganomaly, con cui si vuole migliorare il comportamento di Ganomaly, andando a localizzare la regione anomala di un’immagine, in termini di pixel, e migliorarne efficacia ed efficienza.
Mediante l’utilizzo di transfer learning basato sulla rete VGG16 è possibile ottenere un modello più preciso, TL-Ganomaly. Esso localizza la regione anomala in maniera precisa, in termini di pixel riconosciuti correttamente anomali.
In fase di post-processing inoltre è possibile dare un ulteriore apporto con il modello Conv-Processing, il quale apprende quale kernel convoluzionale riesca a migliorare la segmentazione delle anomalie in fase di post-processing.
2022_Meetup_Mazza-Marzo.pptx
This document outlines a presentation on exploiting graph theory for systems biology. It introduces concepts of graph theory including networks, connected graphs, representations like adjacency matrices, and biological network abstractions. It discusses analyzing biological networks for non-random organization using measures like node degree, power law distributions, and scale-free properties. Examples of protein interaction and metabolic networks are provided. Sources of interaction data and network analysis tools like Cytoscape and Genemania are also mentioned. The document outlines identifying key molecules and mechanisms through centrality measures and optimal diffusion/disruption of networks.
Machine Learning Security
This document discusses machine learning security risks. It begins by explaining how machine learning works and its increasing applications. However, it notes that criminals are also exploiting machine learning tools. It then describes different types of machine learning attacks, including evasion attacks, adversarial attacks, data poisoning attacks, and backdoor poisoning attacks. Specifically, backdoor poisoning aims to force a model to predict an attacker's chosen class when presented with a specific trigger. The document argues that understanding how backdoor poisoning works is an open problem and presents a framework using learning curves to better understand a model's vulnerability to backdoors.
The science of can and can t e la computazione quantistica
The "Science of Can and Can't" e la computazione quantistica
Dli meetup moccia
The document discusses deep learning applications for medical image analysis, including for diagnosis, surgical planning and guidance, and risk assessment. Specifically, it presents examples of using deep learning for tasks like classification, segmentation, detection, and pose estimation using medical images from modalities like ultrasound, X-ray, and video. Challenges in the field include limited datasets, variability in medical images, and privacy concerns, but deep learning methods are able to learn features directly from data to help with complex medical image analysis problems.
Pi school-dli-presentation de nobili
This document summarizes an AI training program hosted by Pi Campus. The program trains engineers from around the world in artificial intelligence skills and has them apply their new skills on industry projects provided by partner companies. It offers the training for free to top developers and provides grants to cover travel and accommodation for those transferring from abroad. The program focuses on learning by doing through hands-on projects rather than traditional teaching. It partners with companies like Google, Facebook, and Amazon to sponsor developers and solve real-world challenges.
Machine Learning Explanations: LIME framework
LIME is a model-agnostic framework that provides local explanations for black box machine learning models. It works by generating new data points around the prediction being explained and training a simple interpretable model, such as linear regression, on those points. This local model approximates the more complex black box model and is used to provide feature importance values for explaining the prediction. The key steps in LIME are data point generation, weighting points by proximity to the prediction being explained, and training an interpretable local model on the weighted points. LIME aims to provide human-understandable explanations by approximating the black box model with an interpretable local model.
Explanation methods for Artificial Intelligence Models
This document discusses explainable artificial intelligence (XAI) techniques. It begins with an introduction to XAI and defines interpretability, comprehensibility, and explainability. It then discusses the problems of "black box" models and the need for explanations. The document outlines several XAI techniques including LIME, LORE, and SHAP. LIME provides local explanations by learning an interpretable model on a perturbed dataset. LORE uses a genetic algorithm to sample the dataset and extracts rules. SHAP assigns feature importance values based on Shapley values from game theory.
Use Cases Machine Learning for Healthcare
Felipe Campos Kitamura is a medical doctor, radiologist, and AI practitioner whose research interests include medical imaging, computer vision, artificial intelligence, and machine learning. He is currently focused on using machine learning in healthcare applications such as medical imaging analysis and using AI to help summarize surgical events in real-time. Machine learning can be applied in healthcare for tasks like medical diagnosis, predictive analytics for disease screening and monitoring, and assisting with surgical procedures.
NLG, Training, Inference & Evaluation
The document summarizes recent work in natural language generation (NLG), including common training and evaluation practices as well as efforts to address limitations. It discusses how teacher forcing can lead to exposure bias during inference and explores alternatives like reinforcement learning and generative adversarial networks. It also reviews work on multilingual datasets and metrics as well as efforts to develop more accurate evaluation methods for NLG like question-based metrics and SAFEval. The document concludes by discussing promising directions for future work such as leveraging discriminators during training and generating questions to evaluate NLG models.
Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)
This document provides an overview of transformer seq2seq models, including their concepts, trends, and limitations. It discusses how transformer models have replaced RNNs for seq2seq tasks due to being more parallelizable and effective at modeling long-term dependencies. Popular seq2seq models like T5, BART, and Pegasus are introduced. The document reviews common pretraining objectives for seq2seq models and current trends in larger model sizes, task-specific pretraining, and long-range modeling techniques. Limitations discussed include the need for grounded representations and efficient generation for seq2seq models.
Towards quantum machine learning calogero zarbo - meet up
Towards Quantum Machine Learning Hands-on
Machine Learning (ML) gained a lot of momentum in the last ten years, mostly thanks to the advancements in non-linear patterns discovery, and more specifically, in Deep Learning (DL). But those who think that DL is going to address all possible problems might be terribly wrong. DL and ML tasks, in general, are categorized as Non-Polynomial problems, which means that the number of possible solutions for a given problem can grow exponentially, making it intractable using the classical algorithmic approach. Here, Quantum Computing (QC) techniques have the potential to address these issues and help ML methods to solve problems faster and sometimes better than the classical counterpart. The conjunction of these two disciplines resulted in a new exciting research direction to explore: Quantum Machine Learning (QML).
Macaluso antonio meetup dli 2020-12-15
towards Quantum Machine Learning
Machine Learning (ML) gained a lot of momentum in the last ten years, mostly thanks to the advancements in non-linear patterns discovery, and more specifically, in Deep Learning (DL). But those who think that DL is going to address all possible problems might be terribly wrong. DL and ML tasks, in general, are categorized as Non-Polynomial problems, which means that the number of possible solutions for a given problem can grow exponentially, making it intractable using the classical algorithmic approach. Here, Quantum Computing (QC) techniques have the potential to address these issues and help ML methods to solve problems faster and sometimes better than the classical counterpart. The conjunction of these two disciplines resulted in a new exciting research direction to explore: Quantum Machine Learning (QML).
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一比一原版加拿大麦吉尔大学毕业证（mcgill毕业证书）如何办理
原版一模一样【微信：741003700 】【加拿大麦吉尔大学毕业证（mcgill毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
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◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
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◇回国马上就要找工作，办给用人单位看；
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◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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校名:学校英文全称
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毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
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一比一原版爱尔兰都柏林大学毕业证(本硕）ucd学位证书如何办理
原版一模一样【微信：741003700 】【爱尔兰都柏林大学毕业证(本硕）ucd成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Do People Really Know Their Fertility Intentions? Correspondence between Sel...
Fertility intention data from surveys often serve as a crucial component in modeling fertility behaviors. Yet, the persistent gap between stated intentions and actual fertility decisions, coupled with the prevalence of uncertain responses, has cast doubt on the overall utility of intentions and sparked controversies about their nature. In this study, we use survey data from a representative sample of Dutch women. With the help of open-ended questions (OEQs) on fertility and Natural Language Processing (NLP) methods, we are able to conduct an in-depth analysis of fertility narratives. Specifically, we annotate the (expert) perceived fertility intentions of respondents and compare them to their self-reported intentions from the survey. Through this analysis, we aim to reveal the disparities between self-reported intentions and the narratives. Furthermore, by applying neural topic modeling methods, we could uncover which topics and characteristics are more prevalent among respondents who exhibit a significant discrepancy between their stated intentions and their probable future behavior, as reflected in their narratives.
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
原版一模一样【微信：741003700 】【(uom毕业证书)曼彻斯特大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uom毕业证书)曼彻斯特大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uom毕业证书)曼彻斯特大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uom毕业证书)曼彻斯特大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uom毕业证书)曼彻斯特大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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一比一原版马来西亚博特拉大学毕业证（upm毕业证）如何办理
原版一模一样【微信：741003700 】【马来西亚博特拉大学毕业证（upm毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理马来西亚博特拉大学毕业证（upm毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理马来西亚博特拉大学毕业证（upm毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理马来西亚博特拉大学毕业证（upm毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理马来西亚博特拉大学毕业证（upm毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
办理【微信号:176555708】【办理(UofT毕业证书)】【微信号:176555708】《成绩单、外壳、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号:176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号:176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版加拿大渥太华大学毕业证（uottawa毕业证书）如何办理
原版一模一样【微信：741003700 】【渥太华大学毕业证（uottawa毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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一比一原版澳洲西澳大学毕业证（uwa毕业证书）如何办理
原版一模一样【微信：741003700 】【澳洲西澳大学毕业证（uwa毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理澳洲西澳大学毕业证（uwa毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
PyData London 2024: Mistakes were made (Dr. Rebecca Bilbro)
To honor ten years of PyData London, join Dr. Rebecca Bilbro as she takes us back in time to reflect on a little over ten years working as a data scientist. One of the many renegade PhDs who joined the fledgling field of data science of the 2010's, Rebecca will share lessons learned the hard way, often from watching data science projects go sideways and learning to fix broken things. Through the lens of these canon events, she'll identify some of the anti-patterns and red flags she's learned to steer around.
一比一原版卡尔加里大学毕业证（uc毕业证）如何办理
原版一模一样【微信：741003700 】【卡尔加里大学毕业证（uc毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
PyData London 2024: Mistakes were made (Dr. Rebecca Bilbro)
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Sid Sigma educational and problem solving power point- Six Sigma.ppt
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Intro to deep learning_ matteo alberti
- 1. Matteo Alberti, Artificial Intelligence and Deep Learning in Quantum Computing @ Atos Email: matteo.alberti@deeplearningitalia.net
- 4. From Perceptron to Multi Layer Perceptron (MLP) 𝑎𝑖 = 𝑓( 𝑖 𝑤𝑖 𝑥𝑖 + 𝑏𝑖) “Not-Linear Discrimination” Where 𝑓 𝑥 Activation Function without 𝑓 𝑥 ANN regresses into linear form: 𝐿 𝑇 = 𝑊𝑇 𝐿 𝑇−1 = 𝑊𝑇 𝑊𝑇−1 𝐿 𝑇−2 = ⋯ 𝑊𝑇 𝑊𝑇−1. . . 𝑊1 𝐿0 = 𝑊𝐿0 One single neuron isn’t able to approximate enough well some decision functions (typically not linear). We can model that stacking “layers” of neurons.
- 5. Activation Functions There are several activation functions: In Deep Neural Networks it’s useful lighten ours AF Sigmoid: 𝑦 = 𝜎 𝑥 = 1/(1 + ⅇ−𝑥 ) “special case of Logistic Function” “Vanishing-Gradient Problem” Rectified Linear Unit (ReLu): 𝑦 = max(0, 𝑥) “Accelerate convergence of weights ”
- 6. • Back-Propagation, Gradient Descent • Optimizers • Neural Networks in Regression and Classification Practical Example (1)
- 7. Deep Learning: Cutting-edge approach to: • Computer Vision • Natural Language Processing • Reinforcement Learning • Finance Artificial Intelligence Applications
- 8. Computer Vision & Convolutional Neural Networks Lasciamolo al Prossimo Speaker
- 9. Natural Language Processing 1 Text Classification: Classifying the topic or theme of a document (i.e. Sentiment Analysis) 2 Language Modeling: Predict the next word given the previous words. It is fundamental for other tasks 3 Speech Recognition: Mapping an acoustic signal containing a spoken natural language utterance into the corresponding sequence of words intended by the speaker 4 Caption Generation: Given a digital image, such as a photo, generate a textual description of the contents of the image.
- 10. Natural Language Processing 5 Machine Translation: Automatic translation of text or speech from one language to another, is one [of] the most important applications of NLP 6 Document Summarization: It is the task where a short description of a text document is created 7 Question Answering: It is the task where the system tries to answer a user query that is formulated in the form of a question by returning the appropriate noun phrase such as a location, a person, or a date. (i.e. Who killed President Kennedy? Oswald)
- 11. Long Short-Term Memory (LSTM) 𝒄 𝒕 (cell state) represents the internal memory of the cell which stores both short term memory and long- term memories 𝒉 𝒕 (hidden state): the hidden state can decide to only retrive the short or long-term or both types of memory stored in the cell state to make the next prediction. 𝒊 𝒕, 𝒇 𝒕 (input/forget gate) How much information from current input (and previous) flows to the cell state 𝒐 𝒕 (output) Decides how much information from the current cell state flows into the hidden state, so that if needed LSTM can only pick the long-term memories or short-term memories and long-term memories
- 12. Word Embedding & Language Modeling Word embedding is the collective name for a set of language modeling and feature learning techniques for natural language processing (NLP) where words or sentences from the vocabulary are mapped to vectors of real numbers. These vectors are semantically correlated by metrics like cosine distance
- 13. • RNN – LSTM • As I said before, not just one taks • Autoencoders • Deep Reinfocement Learning • And . . . Finance! Practical Example (2)
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