The Marriage between Music and Machine Learning in KKBOX
•Download as PPTX, PDF•
14 likes•961 views
陳怡安(Ann Chen) KKBOX 研發工程師 對KKBOX來說，如果我有一百萬個顧客，我就應該有遠多於一百萬個頻道 - 因為音樂聆聽，即便對同一個使用者而言，在不同的時間、地點以及心情下，都有極為不同的需求。如何在對的時間、對的地點、把對的音樂推薦給對的人，是KKBOX研究團隊面臨到的一大挑戰。KKBOX團隊使用機器學習的技術，試圖描述音樂特性及使用者喜好的特徵，進而將歌曲、專輯及歌手推送給每個使用者，創造優質的個人化聆聽體驗。
Report
Share
Report
Share
Recommended
Budget-aware Semi-Supervised Semantic and Instance Segmentation
Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms of supervision, e.g. image-level labels or bounding boxes. Another option are semi-supervised settings, that commonly leverage a few strong annotations and a huge number of unlabeled/weakly-labeled data. In this paper, we revisit semi-supervised segmentation schemes and narrow down significantly the annotation budget (in terms of total labeling time of the training set) compared to previous approaches. With a very simple pipeline, we demonstrate that at low annotation budgets, semi-supervised methods outperform by a wide margin weakly-supervised ones for both semantic and instance segmentation. Our approach also outperforms previous semi-supervised works at a much reduced labeling cost. We present results for the Pascal VOC benchmark and unify weakly and semi-supervised approaches by considering the total annotation budget, thus allowing a fairer comparison between methods.
http://openaccess.thecvf.com/content_CVPRW_2019/html/Deep_Vision_Workshop/Bellver_Budget-aware_Semi-Supervised_Semantic_and_Instance_Segmentation_CVPRW_2019_paper.html
Categorizing and pos tagging with nltk python
https://www.learntek.org/blog/categorizing-pos-tagging-nltk-python/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
Nlp2
This document presents a new neural network model called Linguistically-Informed Self-Attention (LISA) for semantic role labeling. LISA uses multi-task learning across several NLP tasks and incorporates syntactic information through multi-head self-attention. One attention head is trained to attend to syntactic parents using a biaffine parsing mechanism. Experiments show LISA achieves state-of-the-art performance on in-domain and out-of-domain semantic role labeling benchmarks, and incorporating a gold syntactic parse at test time provides further gains. Analysis indicates the largest source of errors is incorrectly labeled semantic role spans.
WIA 2019 - Using Embeddings to Understand the Variance and Evolution of Data ...
WIA 2019 - Using Embeddings to Understand the Variance and Evolution of Data ...Women in Analytics Conference
This document discusses using word embeddings to understand how data science skill sets have evolved over time. It presents two approaches to modeling word embeddings dynamically: 1) training embeddings together over time (dynamic embeddings), and 2) stitching together static embeddings trained on different time periods (static embeddings). The document demonstrates applying dynamic Bernoulli embeddings to career documents from 2016-2018. Analyses of embedding neighborhoods and drifting words identify shifting demand for certain skills like MBAs, PhDs, Tableau, and Hadoop in both small and large corpora.Yun-Nung (Vivian) Chen - 2015 - Unsupervised Learning and Modeling of Knowled...
Yun-Nung (Vivian) Chen - 2015 - Unsupervised Learning and Modeling of Knowled...Association for Computational Linguistics
Anthropic's Constitutional AI assistant Claude. I do not have the capabilities of understanding speech or performing spoken dialogue. I am an AI assistant focused on having helpful text-based conversations.Semantic Search_ NLP_ ML.pdf
This document discusses semantic search, machine learning, and AI in Google's latest algorithms. It explains what semantic search and machine learning are and how search engines use machine learning. It discusses how machine learning can find patterns in URLs and page content, analyze search and classification phrases, identify synonyms and word connections, and provide customized alerts. The document also covers natural language processing and how search engines understand content, as well as topics like Google BERT and how to write better optimized texts.
Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken...
Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken...Yun-Nung (Vivian) Chen
This document describes a method for unsupervised spoken language understanding using matrix factorization with knowledge graph propagation. It discusses four main parts:
1) Ontology induction uses frame-semantic parsing to extract semantic slots from utterances.
2) Structure learning applies knowledge graph propagation to model relations between slots.
3) Spoken language understanding uses matrix factorization to model implicit semantics.
4) Experiments evaluate the method on a dialogue corpus, showing it improves over baselines at estimating semantic slot probabilities from utterances.Recommended
Budget-aware Semi-Supervised Semantic and Instance Segmentation
Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms of supervision, e.g. image-level labels or bounding boxes. Another option are semi-supervised settings, that commonly leverage a few strong annotations and a huge number of unlabeled/weakly-labeled data. In this paper, we revisit semi-supervised segmentation schemes and narrow down significantly the annotation budget (in terms of total labeling time of the training set) compared to previous approaches. With a very simple pipeline, we demonstrate that at low annotation budgets, semi-supervised methods outperform by a wide margin weakly-supervised ones for both semantic and instance segmentation. Our approach also outperforms previous semi-supervised works at a much reduced labeling cost. We present results for the Pascal VOC benchmark and unify weakly and semi-supervised approaches by considering the total annotation budget, thus allowing a fairer comparison between methods.
http://openaccess.thecvf.com/content_CVPRW_2019/html/Deep_Vision_Workshop/Bellver_Budget-aware_Semi-Supervised_Semantic_and_Instance_Segmentation_CVPRW_2019_paper.html
Categorizing and pos tagging with nltk python
https://www.learntek.org/blog/categorizing-pos-tagging-nltk-python/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
Nlp2
This document presents a new neural network model called Linguistically-Informed Self-Attention (LISA) for semantic role labeling. LISA uses multi-task learning across several NLP tasks and incorporates syntactic information through multi-head self-attention. One attention head is trained to attend to syntactic parents using a biaffine parsing mechanism. Experiments show LISA achieves state-of-the-art performance on in-domain and out-of-domain semantic role labeling benchmarks, and incorporating a gold syntactic parse at test time provides further gains. Analysis indicates the largest source of errors is incorrectly labeled semantic role spans.
WIA 2019 - Using Embeddings to Understand the Variance and Evolution of Data ...
WIA 2019 - Using Embeddings to Understand the Variance and Evolution of Data ...Women in Analytics Conference
This document discusses using word embeddings to understand how data science skill sets have evolved over time. It presents two approaches to modeling word embeddings dynamically: 1) training embeddings together over time (dynamic embeddings), and 2) stitching together static embeddings trained on different time periods (static embeddings). The document demonstrates applying dynamic Bernoulli embeddings to career documents from 2016-2018. Analyses of embedding neighborhoods and drifting words identify shifting demand for certain skills like MBAs, PhDs, Tableau, and Hadoop in both small and large corpora.Yun-Nung (Vivian) Chen - 2015 - Unsupervised Learning and Modeling of Knowled...
Yun-Nung (Vivian) Chen - 2015 - Unsupervised Learning and Modeling of Knowled...Association for Computational Linguistics
Anthropic's Constitutional AI assistant Claude. I do not have the capabilities of understanding speech or performing spoken dialogue. I am an AI assistant focused on having helpful text-based conversations.Semantic Search_ NLP_ ML.pdf
This document discusses semantic search, machine learning, and AI in Google's latest algorithms. It explains what semantic search and machine learning are and how search engines use machine learning. It discusses how machine learning can find patterns in URLs and page content, analyze search and classification phrases, identify synonyms and word connections, and provide customized alerts. The document also covers natural language processing and how search engines understand content, as well as topics like Google BERT and how to write better optimized texts.
Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken...
Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken...Yun-Nung (Vivian) Chen
This document describes a method for unsupervised spoken language understanding using matrix factorization with knowledge graph propagation. It discusses four main parts:
1) Ontology induction uses frame-semantic parsing to extract semantic slots from utterances.
2) Structure learning applies knowledge graph propagation to model relations between slots.
3) Spoken language understanding uses matrix factorization to model implicit semantics.
4) Experiments evaluate the method on a dialogue corpus, showing it improves over baselines at estimating semantic slot probabilities from utterances. Yun-Nung (Vivian) Chen - 2015 - Matrix Factorization with Knowledge Graph Pr...
Yun-Nung (Vivian) Chen - 2015 - Matrix Factorization with Knowledge Graph Pr...Association for Computational Linguistics
This document describes a method for unsupervised spoken language understanding using matrix factorization with knowledge graph propagation. It discusses two main issues: 1) adapting generic frames to domain-specific slots, which is addressed using a knowledge graph propagation model; and 2) learning implicit semantics, which is addressed using matrix factorization. The method is evaluated on a dialogue corpus where it achieves improved semantics estimation compared to baselines by modeling implicit semantics.RAG-FUSION: A NEW TAKE ON RETRIEVALAUGMENTED GENERATION
Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product
information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots,
but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines
RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal
scores and fusing the documents and scores. Through manually evaluating answers on accuracy,
relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and
comprehensive answers due to the generated queries contextualizing the original query from various
perspectives. However, some answers strayed off topic when the generated queries' relevance to the
original query is insufficient. This research marks significant progress in artificial intelligence (AI) and
natural language processing (NLP) applications and demonstrates transformations in a global and multiindustry context
Rag-Fusion: A New Take on Retrieval Augmented Generation
Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comprehensive answers due to the generated queries contextualizing the original query from various perspectives. However, some answers strayed off topic when the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multi-industry context.
Realtime captioning checklist
Here is the checklist for CART Captioners in a simple PDF. On-line or real-time captioning occurs at any captioned " live" event.
IJSRED-V2I2P5
1. The document describes a voice command system built using a Raspberry Pi that recognizes disordered speech from people with severe speech disabilities like dysarthria.
2. The system uses Google's speech to text API to convert speech to text which is then processed to match commands. Matched commands trigger modules that provide responses which are converted to speech using a text-to-speech engine.
3. The system was able to successfully take voice commands and trigger the appropriate modules to respond as intended, demonstrating its ability to help those with speech disabilities communicate and interact using voice commands.
AI-powered Semantic SEO by Koray GUBUR
This document discusses optimizing websites and search engines using semantic techniques. It suggests that Website B, with more content, triples, accuracy and connected topics, would be more successful at satisfying search queries. It introduces the concept of topical authority to lower retrieval costs. Several techniques are proposed for language model optimization including fine-tuning, creating topical maps and semantic networks, and generating content informed by human effort and microsemantics. Cross-lingual embeddings and understanding word relationships are also discussed as ways to improve semantic search.
WordNet Based Online Reverse Dictionary with Improved Accuracy and Parts-of-S...
This document proposes improvements to existing reverse dictionary systems. It discusses how current reverse dictionaries map user input phrases to words using semantic similarity, but sometimes return unrelated words. The proposed improvements are:
1. Refine the mapping process to consider candidate words that appear in at least two of the input terms' semantic sets, rather than requiring inclusion in all sets. This may return more relevant words.
2. When expanding queries using hypernyms, limit the level of generalization to avoid overly broad terms. Broad terms are more likely to appear in unrelated words' definitions.
3. Determine the part-of-speech of terms in the user's input phrase and return candidate words matching that part-of-speech,
"Updates on Semantic Fingerprinting", Francisco Webber, Inventor and Co-Found...
Francisco Webber is the CEO and Founder of Cortical.io, a company that develops Natural Language Processing solutions for Big Text Data. Francisco’s medical background in genetics combined with over two decade’s of experience in Information Technology, inspired him to create a groundbreaking technology, called Semantic Folding, which is based on the latest findings on the way the human neocortex processes information.
DeepPavlov 2019
DeepPavlov is an open-source framework for the development of production-ready chat-bots and complex conversational systems, as well as NLP and dialog systems research.
“Towards Multi-Step Expert Advice for Cognitive Computing” - Dr. Achim Rettin...
Dr. Achim Rettinger from Karlsruhe Institute of Technology presented this today as part of the Cognitive Systems Institute Speaker Series on October 13, 2016
Adria Recasens, DeepMind – Multi-modal self-supervised learning from videos
The document summarizes a talk on multi-modal self-supervised learning from videos. It discusses using multiple modalities like vision, audio and language from videos for self-supervised learning. It presents two models: 1) A Multi-Modal Versatile network that can take any modality as input and respects the specificity of each while enabling comparison. 2) BraVe which learns representations by regressing a broad representation of the whole video from a narrow view to leverage different augmentations and modalities. Both models achieve state-of-the-art results on downstream tasks, showing videos provide rich self-supervision and using additional context improves representation learning.
Rosinski ibm ai overview with several examples of projects in the media and l...
As presented by Jakob Rosinski at the FIAT/IFTA Media Management Seminar in Stockholm, 23-24 May 2019.
RCOMM 2011 - Sentiment Classification
This document summarizes a presentation on sentiment classification using supervised machine learning approaches and RapidMiner. It discusses how sentiment analysis can be used for search, recommendations, market research and ad placement. A case study is described that uses RapidMiner to classify movie reviews from IMDB as positive or negative based on word vectors. Additional features like part-of-speech tags, sentiment lexicons, and document statistics are shown to improve accuracy from 85% to 86%.
RCOMM 2011 - Sentiment Classification with RapidMiner
This document summarizes a presentation on sentiment classification using supervised machine learning approaches and RapidMiner. It discusses how sentiment analysis can be used for search, recommendations, market research and ad placement. A case study is described that uses RapidMiner to classify movie reviews from IMDB as positive or negative based on word vectors. Additional features like part-of-speech tags, sentiment lexicons, and document statistics are shown to improve accuracy from 85% to 86%.
RESEARCH PROPOSAL ON ENHANCING AUTOMATIC IMAGE CAPTIONING SYSTEM LSTM.pdf
In this research study, the researchers aim to investigate and address these challenges by proposing techniques and architectures for enhancing image captioning systems using CNNs and LSTMs. Specifically, the researchers will focus on developing a system that generates accurate and semantically meaningful captions for a wide range of images. By doing so, the researchers aim to contribute to the development of more effective and reliable image captioning systems.
Semantic video classification based on subtitles and domain terminologies
This document proposes an unsupervised approach to semantically classify videos based on analyzing their subtitles. It extracts keywords from subtitles using text ranking, disambiguates word senses with WordNet, identifies relevant WordNet domains, defines correspondences between domains and category labels, and assigns categories to videos by comparing their domains to label domains. An experiment on classifying documentaries based on their subtitles achieved 69.4% accuracy, outperforming a decision tree classifier. The approach is part of developing automatic video annotation techniques for a TV metadata platform.
Investigation of the Effect of MFCC Variation on the Convolutional Neural Net...
Md. Rakibul Hasan investigated the effect of Mel-frequency cepstral coefficient (MFCC) variation on convolutional neural network-based speech classification. He collected isolated vowel and word samples from Bengali speech and extracted MFCC features. A CNN model was trained on the MFCC data and achieved higher accuracy for vowel recognition compared to word recognition. Analysis showed vowels had less MFCC variation than words, contributing to their better classification performance by the CNN model.
Neural Models for Information Retrieval
In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modelling and machine translation. This suggests that neural models may also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text.
In this talk, I will present my recent work on neural IR models. We begin with a discussion on learning good representations of text for retrieval. I will present visual intuitions about how different embeddings spaces capture different relationships between items, and their usefulness to different types of IR tasks. The second part of this talk is focused on the applications of deep neural architectures to the document ranking task.
Straight
This document discusses fixed point representations for high-quality speech and sound modification systems. It presents the STRAIGHT system, which uses fixed point algorithms to extract features such as fundamental frequency and excitation from speech. Fixed points provide values as well as reliability indices. The system allows for grading of parameters and morphing of speech. It provides transparent representation and manipulation of speech without post-processing.
Crowdsourcing-enabled Linked Data management architecture
This document proposes a semantically enabled architecture for crowdsourced Linked Data management. The architecture includes a SPARQL query engine that can process queries using both automatic query processing and crowdsourcing. The query engine includes components for parsing, optimizing, and executing queries. It generates crowdsourcing tasks from queries and integrates the results. The architecture extends the VoID and SPARQL specifications to support crowdsourcing. Example crowdsourcing tasks addressed include classification, ordering, identity resolution, and providing missing information. Challenges include designing optimal human intelligence tasks and user interfaces from SPARQL queries.
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
More Related Content
Similar to The Marriage between Music and Machine Learning in KKBOX
Yun-Nung (Vivian) Chen - 2015 - Matrix Factorization with Knowledge Graph Pr...
Yun-Nung (Vivian) Chen - 2015 - Matrix Factorization with Knowledge Graph Pr...Association for Computational Linguistics
This document describes a method for unsupervised spoken language understanding using matrix factorization with knowledge graph propagation. It discusses two main issues: 1) adapting generic frames to domain-specific slots, which is addressed using a knowledge graph propagation model; and 2) learning implicit semantics, which is addressed using matrix factorization. The method is evaluated on a dialogue corpus where it achieves improved semantics estimation compared to baselines by modeling implicit semantics.RAG-FUSION: A NEW TAKE ON RETRIEVALAUGMENTED GENERATION
Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product
information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots,
but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines
RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal
scores and fusing the documents and scores. Through manually evaluating answers on accuracy,
relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and
comprehensive answers due to the generated queries contextualizing the original query from various
perspectives. However, some answers strayed off topic when the generated queries' relevance to the
original query is insufficient. This research marks significant progress in artificial intelligence (AI) and
natural language processing (NLP) applications and demonstrates transformations in a global and multiindustry context
Rag-Fusion: A New Take on Retrieval Augmented Generation
Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comprehensive answers due to the generated queries contextualizing the original query from various perspectives. However, some answers strayed off topic when the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multi-industry context.
Realtime captioning checklist
Here is the checklist for CART Captioners in a simple PDF. On-line or real-time captioning occurs at any captioned " live" event.
IJSRED-V2I2P5
1. The document describes a voice command system built using a Raspberry Pi that recognizes disordered speech from people with severe speech disabilities like dysarthria.
2. The system uses Google's speech to text API to convert speech to text which is then processed to match commands. Matched commands trigger modules that provide responses which are converted to speech using a text-to-speech engine.
3. The system was able to successfully take voice commands and trigger the appropriate modules to respond as intended, demonstrating its ability to help those with speech disabilities communicate and interact using voice commands.
AI-powered Semantic SEO by Koray GUBUR
This document discusses optimizing websites and search engines using semantic techniques. It suggests that Website B, with more content, triples, accuracy and connected topics, would be more successful at satisfying search queries. It introduces the concept of topical authority to lower retrieval costs. Several techniques are proposed for language model optimization including fine-tuning, creating topical maps and semantic networks, and generating content informed by human effort and microsemantics. Cross-lingual embeddings and understanding word relationships are also discussed as ways to improve semantic search.
WordNet Based Online Reverse Dictionary with Improved Accuracy and Parts-of-S...
This document proposes improvements to existing reverse dictionary systems. It discusses how current reverse dictionaries map user input phrases to words using semantic similarity, but sometimes return unrelated words. The proposed improvements are:
1. Refine the mapping process to consider candidate words that appear in at least two of the input terms' semantic sets, rather than requiring inclusion in all sets. This may return more relevant words.
2. When expanding queries using hypernyms, limit the level of generalization to avoid overly broad terms. Broad terms are more likely to appear in unrelated words' definitions.
3. Determine the part-of-speech of terms in the user's input phrase and return candidate words matching that part-of-speech,
"Updates on Semantic Fingerprinting", Francisco Webber, Inventor and Co-Found...
Francisco Webber is the CEO and Founder of Cortical.io, a company that develops Natural Language Processing solutions for Big Text Data. Francisco’s medical background in genetics combined with over two decade’s of experience in Information Technology, inspired him to create a groundbreaking technology, called Semantic Folding, which is based on the latest findings on the way the human neocortex processes information.
DeepPavlov 2019
DeepPavlov is an open-source framework for the development of production-ready chat-bots and complex conversational systems, as well as NLP and dialog systems research.
“Towards Multi-Step Expert Advice for Cognitive Computing” - Dr. Achim Rettin...
Dr. Achim Rettinger from Karlsruhe Institute of Technology presented this today as part of the Cognitive Systems Institute Speaker Series on October 13, 2016
Adria Recasens, DeepMind – Multi-modal self-supervised learning from videos
The document summarizes a talk on multi-modal self-supervised learning from videos. It discusses using multiple modalities like vision, audio and language from videos for self-supervised learning. It presents two models: 1) A Multi-Modal Versatile network that can take any modality as input and respects the specificity of each while enabling comparison. 2) BraVe which learns representations by regressing a broad representation of the whole video from a narrow view to leverage different augmentations and modalities. Both models achieve state-of-the-art results on downstream tasks, showing videos provide rich self-supervision and using additional context improves representation learning.
Rosinski ibm ai overview with several examples of projects in the media and l...
As presented by Jakob Rosinski at the FIAT/IFTA Media Management Seminar in Stockholm, 23-24 May 2019.
RCOMM 2011 - Sentiment Classification
This document summarizes a presentation on sentiment classification using supervised machine learning approaches and RapidMiner. It discusses how sentiment analysis can be used for search, recommendations, market research and ad placement. A case study is described that uses RapidMiner to classify movie reviews from IMDB as positive or negative based on word vectors. Additional features like part-of-speech tags, sentiment lexicons, and document statistics are shown to improve accuracy from 85% to 86%.
RCOMM 2011 - Sentiment Classification with RapidMiner
This document summarizes a presentation on sentiment classification using supervised machine learning approaches and RapidMiner. It discusses how sentiment analysis can be used for search, recommendations, market research and ad placement. A case study is described that uses RapidMiner to classify movie reviews from IMDB as positive or negative based on word vectors. Additional features like part-of-speech tags, sentiment lexicons, and document statistics are shown to improve accuracy from 85% to 86%.
RESEARCH PROPOSAL ON ENHANCING AUTOMATIC IMAGE CAPTIONING SYSTEM LSTM.pdf
In this research study, the researchers aim to investigate and address these challenges by proposing techniques and architectures for enhancing image captioning systems using CNNs and LSTMs. Specifically, the researchers will focus on developing a system that generates accurate and semantically meaningful captions for a wide range of images. By doing so, the researchers aim to contribute to the development of more effective and reliable image captioning systems.
Semantic video classification based on subtitles and domain terminologies
This document proposes an unsupervised approach to semantically classify videos based on analyzing their subtitles. It extracts keywords from subtitles using text ranking, disambiguates word senses with WordNet, identifies relevant WordNet domains, defines correspondences between domains and category labels, and assigns categories to videos by comparing their domains to label domains. An experiment on classifying documentaries based on their subtitles achieved 69.4% accuracy, outperforming a decision tree classifier. The approach is part of developing automatic video annotation techniques for a TV metadata platform.
Investigation of the Effect of MFCC Variation on the Convolutional Neural Net...
Md. Rakibul Hasan investigated the effect of Mel-frequency cepstral coefficient (MFCC) variation on convolutional neural network-based speech classification. He collected isolated vowel and word samples from Bengali speech and extracted MFCC features. A CNN model was trained on the MFCC data and achieved higher accuracy for vowel recognition compared to word recognition. Analysis showed vowels had less MFCC variation than words, contributing to their better classification performance by the CNN model.
Neural Models for Information Retrieval
In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modelling and machine translation. This suggests that neural models may also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text.
In this talk, I will present my recent work on neural IR models. We begin with a discussion on learning good representations of text for retrieval. I will present visual intuitions about how different embeddings spaces capture different relationships between items, and their usefulness to different types of IR tasks. The second part of this talk is focused on the applications of deep neural architectures to the document ranking task.
Straight
This document discusses fixed point representations for high-quality speech and sound modification systems. It presents the STRAIGHT system, which uses fixed point algorithms to extract features such as fundamental frequency and excitation from speech. Fixed points provide values as well as reliability indices. The system allows for grading of parameters and morphing of speech. It provides transparent representation and manipulation of speech without post-processing.
Crowdsourcing-enabled Linked Data management architecture
This document proposes a semantically enabled architecture for crowdsourced Linked Data management. The architecture includes a SPARQL query engine that can process queries using both automatic query processing and crowdsourcing. The query engine includes components for parsing, optimizing, and executing queries. It generates crowdsourcing tasks from queries and integrates the results. The architecture extends the VoID and SPARQL specifications to support crowdsourcing. Example crowdsourcing tasks addressed include classification, ordering, identity resolution, and providing missing information. Challenges include designing optimal human intelligence tasks and user interfaces from SPARQL queries.
Similar to The Marriage between Music and Machine Learning in KKBOX (20)
Yun-Nung (Vivian) Chen - 2015 - Matrix Factorization with Knowledge Graph Pr...
Yun-Nung (Vivian) Chen - 2015 - Matrix Factorization with Knowledge Graph Pr...
RAG-FUSION: A NEW TAKE ON RETRIEVALAUGMENTED GENERATION
RAG-FUSION: A NEW TAKE ON RETRIEVALAUGMENTED GENERATION
Rag-Fusion: A New Take on Retrieval Augmented Generation
Rag-Fusion: A New Take on Retrieval Augmented Generation
WordNet Based Online Reverse Dictionary with Improved Accuracy and Parts-of-S...
WordNet Based Online Reverse Dictionary with Improved Accuracy and Parts-of-S...
"Updates on Semantic Fingerprinting", Francisco Webber, Inventor and Co-Found...
"Updates on Semantic Fingerprinting", Francisco Webber, Inventor and Co-Found...
“Towards Multi-Step Expert Advice for Cognitive Computing” - Dr. Achim Rettin...
“Towards Multi-Step Expert Advice for Cognitive Computing” - Dr. Achim Rettin...
Adria Recasens, DeepMind – Multi-modal self-supervised learning from videos
Adria Recasens, DeepMind – Multi-modal self-supervised learning from videos
Rosinski ibm ai overview with several examples of projects in the media and l...
Rosinski ibm ai overview with several examples of projects in the media and l...
RCOMM 2011 - Sentiment Classification with RapidMiner
RCOMM 2011 - Sentiment Classification with RapidMiner
RESEARCH PROPOSAL ON ENHANCING AUTOMATIC IMAGE CAPTIONING SYSTEM LSTM.pdf
RESEARCH PROPOSAL ON ENHANCING AUTOMATIC IMAGE CAPTIONING SYSTEM LSTM.pdf
Semantic video classification based on subtitles and domain terminologies
Semantic video classification based on subtitles and domain terminologies
Investigation of the Effect of MFCC Variation on the Convolutional Neural Net...
Investigation of the Effect of MFCC Variation on the Convolutional Neural Net...
Crowdsourcing-enabled Linked Data management architecture
Crowdsourcing-enabled Linked Data management architecture
Recently uploaded
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理
原版一模一样【微信：741003700 】【(lbs毕业证书)伦敦商学院毕业证成绩单】【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(lbs毕业证书)伦敦商学院毕业证【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(lbs毕业证书)伦敦商学院毕业证【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(lbs毕业证书)伦敦商学院毕业证【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(lbs毕业证书)伦敦商学院毕业证【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。
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
原版办【微信号:BYZS866】【兰加拉学院毕业证(Langara毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、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BYZS866】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BYZS866】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(曼大毕业证书)曼尼托巴大学毕业证如何办理
原版一模一样【微信：741003700 】【(曼大毕业证书)曼尼托巴大学毕业证成绩单】【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(曼大毕业证书)曼尼托巴大学毕业证【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(曼大毕业证书)曼尼托巴大学毕业证【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(曼大毕业证书)曼尼托巴大学毕业证【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(曼大毕业证书)曼尼托巴大学毕业证【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。
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
Jio cinema Retention & Engagement Strategy.pdf
Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（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(harvard毕业证书）哈佛大学毕业证如何办理
一模一样【微信：A575476】【(harvard毕业证书）哈佛大学毕业证成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）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。
【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：A575476】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：A575476】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
→ 【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：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部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！
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
Open Source Contributions to Postgres: The Basics POSETTE 2024
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
How To Control IO Usage using Resource Manager
How To Control IO Usage using Resource Manager
In Multitenant and Exadata architecture
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。
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
学校原件一模一样【微信：741003700 】《(unimelb毕业证书)墨尔本大学毕业证》【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
Cell The Unit of Life for NEET Multiple Choice Questions.docx
celltheunitoflife
ribosomes
thegolgi
nuclearmembrane
cilia
phragmoplast
Recently uploaded (20)
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
Cell The Unit of Life for NEET Multiple Choice Questions.docx
Cell The Unit of Life for NEET Multiple Choice Questions.docx
The Marriage between Music and Machine Learning in KKBOX
- 1. The Marriage of Music and Machine Learning in KKBOX Research Center of KKBOX
- 10. Word2Vec - “The results, to our own surprise, show that the buzz is fully justified, as the context-predicting models obtain a thorough and resounding victory against their count-based counterparts.” - Marco et al. “You should know the word by the company it keeps” (Firth J.R.)
- 15. DeepWalk (Bryan Perozzi, Rami Al-Rfou& Steven Skiena, 2014 ) Random Walk Word2Vec
- 16. Short truncated random walks are sentences in an artificial language Random walk distance is known to be good features for many problems for example: a. Recommendation b. Search optimization despite accuracy, gives the sense of serendipity
- 17. 青花瓷 珊瑚海 我不配 給我一首歌的時間 黃金甲 珊瑚海 雙截棍 天地一鬥
- 21. Cold start ? Learn the relationships between laten factors and audio signals
- 24. Future Directions Hybrid of collaborative filtering and content based techniques Trending/Popular within user segments Optimizing for various metrics drive business goals