This presentation goes into the details of word embeddings, applications, learning word embeddings through shallow neural network , Continuous Bag of Words Model.
An introduction to the Transformers architecture and BERTSuman Debnath
油
The transformer is one of the most popular state-of-the-art deep (SOTA) learning architectures that is mostly used for natural language processing (NLP) tasks. Ever since the advent of the transformer, it has replaced RNN and LSTM for various tasks. The transformer also created a major breakthrough in the field of NLP and also paved the way for new revolutionary architectures such as BERT.
Natural language processing and transformer modelsDing Li
油
The document discusses several approaches for text classification using machine learning algorithms:
1. Count the frequency of individual words in tweets and sum for each tweet to create feature vectors for classification models like regression. However, this loses some word context information.
2. Use Bayes' rule and calculate word probabilities conditioned on class to perform naive Bayes classification. Laplacian smoothing is used to handle zero probabilities.
3. Incorporate word n-grams and context by calculating word probabilities within n-gram contexts rather than independently. This captures more linguistic information than the first two approaches.
Introduction to Natural Language ProcessingPranav Gupta
油
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
The document discusses word embedding techniques used to represent words as vectors. It describes Word2Vec as a popular word embedding model that uses either the Continuous Bag of Words (CBOW) or Skip-gram architecture. CBOW predicts a target word based on surrounding context words, while Skip-gram predicts surrounding words given a target word. These models represent words as dense vectors that encode semantic and syntactic properties, allowing operations like word analogy questions.
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
Word embedding, Vector space model, language modelling, Neural language model, Word2Vec, GloVe, Fasttext, ELMo, BERT, distilBER, roBERTa, sBERT, Transformer, Attention
This document discusses natural language processing (NLP) toolkits and preprocessing techniques. It introduces popular Python NLP libraries like NLTK, TextBlob, spaCy and gensim. It also covers various text preprocessing methods including tokenization, removing punctuation/characters, stemming, lemmatization, part-of-speech tagging, named entity recognition and more. Code examples demonstrate how to implement these techniques in Python to clean and normalize text data for analysis.
Presentation of "Challenges in transfer learning in NLP" from Madrid Natural Language Processing Meetup Event, May, 2019.
https://www.meetup.com/es-ES/Madrid-Natural-Language-Processing-meetup/
Practical related work in repository: https://github.com/laraolmos/madrid-nlp-meetup
A Simple Introduction to Word EmbeddingsBhaskar Mitra
油
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
This document presents an overview of named entity recognition (NER) and the conditional random field (CRF) algorithm for NER. It defines NER as the identification and classification of named entities like people, organizations, locations, etc. in unstructured text. The document discusses the types of named entities, common NER techniques including rule-based and supervised methods, and explains the CRF algorithm and its mathematical model. It also covers the advantages of CRF for NER and examples of its applications in areas like information extraction.
BERT: Bidirectional Encoder Representations from TransformersLiangqun Lu
油
BERT was developed by Google AI Language and came out Oct. 2018. It has achieved the best performance in many NLP tasks. So if you are interested in NLP, studying BERT is a good way to go.
This document provides an overview of Word2Vec, a neural network model for learning word embeddings developed by researchers led by Tomas Mikolov at Google in 2013. It describes the goal of reconstructing word contexts, different word embedding techniques like one-hot vectors, and the two main Word2Vec models - Continuous Bag of Words (CBOW) and Skip-Gram. These models map words to vectors in a neural network and are trained to predict words from contexts or predict contexts from words. The document also discusses Word2Vec parameters, implementations, and other applications that build upon its approach to word embeddings.
This document provides an overview of Word2Vec, a model for generating word embeddings. It explains that Word2Vec uses a neural network to learn vector representations of words from large amounts of text such that words with similar meanings are located close to each other in the vector space. The document outlines how Word2Vec is trained using either the Continuous Bag-of-Words or Skip-gram architectures on sequences of words from text corpora. It also discusses how the trained Word2Vec model can be used for tasks like word similarity, analogy completion, and document classification. Finally, it provides a Python example of loading a pre-trained Word2Vec model and using it to find word vectors, similarities, analogies and outlier words.
This document discusses methods for evaluating language models, including intrinsic and extrinsic evaluation. Intrinsic evaluation involves measuring a model's performance on a test set using metrics like perplexity, which is based on how well the model predicts the test set. Extrinsic evaluation embeds the model in an application and measures the application's performance. The document also covers techniques for dealing with unknown words like replacing low-frequency words with <UNK> and estimating its probability from training data.
Introduction to Named Entity RecognitionTomer Lieber
油
Named Entity Recognition (NER) is a common task in Natural Language Processing that aims to find and classify named entities in text, such as person names, organizations, and locations, into predefined categories. NER can be used for applications like machine translation, information retrieval, and question answering. Traditional approaches to NER involve feature extraction and training statistical or machine learning models on features, while current state-of-the-art methods use deep learning models like LSTMs combined with word embeddings. NER performance is typically evaluated using the F1 score, which balances precision and recall of named entity detection.
SoDA v2 - Named Entity Recognition from streaming textSujit Pal
油
The document describes dictionary-based named entity extraction from streaming text. It discusses named entity recognition approaches like regular expression-based, dictionary-based, and model-based. It then describes the SoDA v.2 architecture for scalable dictionary-based named entity extraction, including the Aho-Corasick algorithm, SolrTextTagger, and services provided. Finally, it outlines future work on improving the system.
Continuous representations of words and documents, which is recently referred to as Word Embeddings, have recently demonstrated large advancements in many of the Natural language processing tasks.
In this presentation we will provide an introduction to the most common methods of learning these representations. As well as previous methods in building these representations before the recent advances in deep learning, such as dimensionality reduction on the word co-occurrence matrix.
Moreover, we will present the continuous bag of word model (CBOW), one of the most successful models for word embeddings and one of the core models in word2vec, and in brief a glance of many other models of building representations for other tasks such as knowledge base embeddings.
Finally, we will motivate the potential of using such embeddings for many tasks that could be of importance for the group, such as semantic similarity, document clustering and retrieval.
The document provides an introduction to word embeddings and two related techniques: Word2Vec and Word Movers Distance. Word2Vec is an algorithm that produces word embeddings by training a neural network on a large corpus of text, with the goal of producing dense vector representations of words that encode semantic relationships. Word Movers Distance is a method for calculating the semantic distance between documents based on the embedded word vectors, allowing comparison of documents with different words but similar meanings. The document explains these techniques and provides examples of their applications and properties.
Deep generative models can generate synthetic images, speech, text and other data types. There are three popular types: autoregressive models which generate data step-by-step; variational autoencoders which learn the distribution of latent variables to generate data; and generative adversarial networks which train a generator and discriminator in an adversarial game to generate high quality samples. Generative models have applications in image generation, translation between domains, and simulation.
Introduction to natural language processing, history and originShubhankar Mohan
油
This document provides an introduction to natural language processing, including its history, goals, challenges, and applications. It discusses how NLP aims to help machines process human language like translation, summarization, and question answering. While language is complex, NLP uses techniques from linguistics, machine learning, and computer science to develop tools that analyze, understand, and generate human language.
This document discusses neural network models for natural language processing tasks like machine translation. It describes how recurrent neural networks (RNNs) were used initially but had limitations in capturing long-term dependencies and parallelization. The encoder-decoder framework addressed some issues but still lost context. Attention mechanisms allowed focusing on relevant parts of the input and using all encoded states. Transformers replaced RNNs entirely with self-attention and encoder-decoder attention, allowing parallelization while generating a richer representation capturing word relationships. This revolutionized NLP tasks like machine translation.
Text similarity measures are used to quantify the similarity between text strings and documents. Common text similarity measures include Levenshtein distance for word similarity and cosine similarity for document similarity. To apply cosine similarity, documents first need to be represented in a document-term matrix using techniques like count vectorization or TF-IDF. TF-IDF is often preferred as it assigns higher importance to rare terms compared to common terms.
Fine-tuning BERT for Question AnsweringApache MXNet
油
This deck covers the problem of fine-tuning a pre-trained BERT model for the task of Question Answering. Check out the GluonNLP model zoo here for models and tutorials: http://gluon-nlp.mxnet.io/model_zoo/bert/index.html
際際滷s: Thomas Delteil
Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.
[Emnlp] what is glo ve part i - towards data scienceNikhil Jaiswal
油
This document introduces GloVe (Global Vectors), a method for creating word embeddings that combines global matrix factorization and local context window models. It discusses how global matrix factorization uses singular value decomposition to reduce a term-frequency matrix to learn word vectors from global corpus statistics. It also explains how local context window models like skip-gram and CBOW learn word embeddings by predicting words from a fixed-size window of surrounding context words during training. GloVe aims to learn from both global co-occurrence patterns and local context to generate word vectors.
Presentation of "Challenges in transfer learning in NLP" from Madrid Natural Language Processing Meetup Event, May, 2019.
https://www.meetup.com/es-ES/Madrid-Natural-Language-Processing-meetup/
Practical related work in repository: https://github.com/laraolmos/madrid-nlp-meetup
A Simple Introduction to Word EmbeddingsBhaskar Mitra
油
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
This document presents an overview of named entity recognition (NER) and the conditional random field (CRF) algorithm for NER. It defines NER as the identification and classification of named entities like people, organizations, locations, etc. in unstructured text. The document discusses the types of named entities, common NER techniques including rule-based and supervised methods, and explains the CRF algorithm and its mathematical model. It also covers the advantages of CRF for NER and examples of its applications in areas like information extraction.
BERT: Bidirectional Encoder Representations from TransformersLiangqun Lu
油
BERT was developed by Google AI Language and came out Oct. 2018. It has achieved the best performance in many NLP tasks. So if you are interested in NLP, studying BERT is a good way to go.
This document provides an overview of Word2Vec, a neural network model for learning word embeddings developed by researchers led by Tomas Mikolov at Google in 2013. It describes the goal of reconstructing word contexts, different word embedding techniques like one-hot vectors, and the two main Word2Vec models - Continuous Bag of Words (CBOW) and Skip-Gram. These models map words to vectors in a neural network and are trained to predict words from contexts or predict contexts from words. The document also discusses Word2Vec parameters, implementations, and other applications that build upon its approach to word embeddings.
This document provides an overview of Word2Vec, a model for generating word embeddings. It explains that Word2Vec uses a neural network to learn vector representations of words from large amounts of text such that words with similar meanings are located close to each other in the vector space. The document outlines how Word2Vec is trained using either the Continuous Bag-of-Words or Skip-gram architectures on sequences of words from text corpora. It also discusses how the trained Word2Vec model can be used for tasks like word similarity, analogy completion, and document classification. Finally, it provides a Python example of loading a pre-trained Word2Vec model and using it to find word vectors, similarities, analogies and outlier words.
This document discusses methods for evaluating language models, including intrinsic and extrinsic evaluation. Intrinsic evaluation involves measuring a model's performance on a test set using metrics like perplexity, which is based on how well the model predicts the test set. Extrinsic evaluation embeds the model in an application and measures the application's performance. The document also covers techniques for dealing with unknown words like replacing low-frequency words with <UNK> and estimating its probability from training data.
Introduction to Named Entity RecognitionTomer Lieber
油
Named Entity Recognition (NER) is a common task in Natural Language Processing that aims to find and classify named entities in text, such as person names, organizations, and locations, into predefined categories. NER can be used for applications like machine translation, information retrieval, and question answering. Traditional approaches to NER involve feature extraction and training statistical or machine learning models on features, while current state-of-the-art methods use deep learning models like LSTMs combined with word embeddings. NER performance is typically evaluated using the F1 score, which balances precision and recall of named entity detection.
SoDA v2 - Named Entity Recognition from streaming textSujit Pal
油
The document describes dictionary-based named entity extraction from streaming text. It discusses named entity recognition approaches like regular expression-based, dictionary-based, and model-based. It then describes the SoDA v.2 architecture for scalable dictionary-based named entity extraction, including the Aho-Corasick algorithm, SolrTextTagger, and services provided. Finally, it outlines future work on improving the system.
Continuous representations of words and documents, which is recently referred to as Word Embeddings, have recently demonstrated large advancements in many of the Natural language processing tasks.
In this presentation we will provide an introduction to the most common methods of learning these representations. As well as previous methods in building these representations before the recent advances in deep learning, such as dimensionality reduction on the word co-occurrence matrix.
Moreover, we will present the continuous bag of word model (CBOW), one of the most successful models for word embeddings and one of the core models in word2vec, and in brief a glance of many other models of building representations for other tasks such as knowledge base embeddings.
Finally, we will motivate the potential of using such embeddings for many tasks that could be of importance for the group, such as semantic similarity, document clustering and retrieval.
The document provides an introduction to word embeddings and two related techniques: Word2Vec and Word Movers Distance. Word2Vec is an algorithm that produces word embeddings by training a neural network on a large corpus of text, with the goal of producing dense vector representations of words that encode semantic relationships. Word Movers Distance is a method for calculating the semantic distance between documents based on the embedded word vectors, allowing comparison of documents with different words but similar meanings. The document explains these techniques and provides examples of their applications and properties.
Deep generative models can generate synthetic images, speech, text and other data types. There are three popular types: autoregressive models which generate data step-by-step; variational autoencoders which learn the distribution of latent variables to generate data; and generative adversarial networks which train a generator and discriminator in an adversarial game to generate high quality samples. Generative models have applications in image generation, translation between domains, and simulation.
Introduction to natural language processing, history and originShubhankar Mohan
油
This document provides an introduction to natural language processing, including its history, goals, challenges, and applications. It discusses how NLP aims to help machines process human language like translation, summarization, and question answering. While language is complex, NLP uses techniques from linguistics, machine learning, and computer science to develop tools that analyze, understand, and generate human language.
This document discusses neural network models for natural language processing tasks like machine translation. It describes how recurrent neural networks (RNNs) were used initially but had limitations in capturing long-term dependencies and parallelization. The encoder-decoder framework addressed some issues but still lost context. Attention mechanisms allowed focusing on relevant parts of the input and using all encoded states. Transformers replaced RNNs entirely with self-attention and encoder-decoder attention, allowing parallelization while generating a richer representation capturing word relationships. This revolutionized NLP tasks like machine translation.
Text similarity measures are used to quantify the similarity between text strings and documents. Common text similarity measures include Levenshtein distance for word similarity and cosine similarity for document similarity. To apply cosine similarity, documents first need to be represented in a document-term matrix using techniques like count vectorization or TF-IDF. TF-IDF is often preferred as it assigns higher importance to rare terms compared to common terms.
Fine-tuning BERT for Question AnsweringApache MXNet
油
This deck covers the problem of fine-tuning a pre-trained BERT model for the task of Question Answering. Check out the GluonNLP model zoo here for models and tutorials: http://gluon-nlp.mxnet.io/model_zoo/bert/index.html
際際滷s: Thomas Delteil
Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.
[Emnlp] what is glo ve part i - towards data scienceNikhil Jaiswal
油
This document introduces GloVe (Global Vectors), a method for creating word embeddings that combines global matrix factorization and local context window models. It discusses how global matrix factorization uses singular value decomposition to reduce a term-frequency matrix to learn word vectors from global corpus statistics. It also explains how local context window models like skip-gram and CBOW learn word embeddings by predicting words from a fixed-size window of surrounding context words during training. GloVe aims to learn from both global co-occurrence patterns and local context to generate word vectors.
Word embedding is a technique in natural language processing where words are represented as dense vectors in a continuous vector space. These representations are designed to capture semantic and syntactic relationships between words based on their distributional properties in large amounts of text. Two popular word embedding models are Word2Vec and GloVe. Word2Vec uses a shallow neural network to learn word vectors that place words with similar meanings close to each other in the vector space. GloVe is an unsupervised learning algorithm that trains word vectors based on global word-word co-occurrence statistics from a corpus.
Deep learning Malaysia presentation 12/4/2017Brian Ho
油
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness, happiness and focus.
The document discusses word embeddings, which learn vector representations of words from large corpora of text. It describes two popular methods for learning word embeddings: continuous bag-of-words (CBOW) and skip-gram. CBOW predicts a word based on surrounding context words, while skip-gram predicts surrounding words from the target word. The document also discusses techniques like subsampling frequent words and negative sampling that improve the training of word embeddings on large datasets. Finally, it outlines several applications of word embeddings, such as multi-task learning across languages and embedding images with text.
The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017StampedeCon
油
Words are no longer sufficient in delivering the search results users are looking for, particularly in relation to image search. Text and languages pose many challenges in describing visual details and providing the necessary context for optimal results. Machine Learning technology opens a new world of search innovation that has yet to be applied by businesses.
In this session, Mike Ranzinger of Shutterstock will share a technical presentation detailing his research on composition aware search. He will also demonstrate how the research led to the launch of AI technology allowing users to more precisely find the image they need within Shutterstocks collection of more than 150 million images. While the company released a number of AI search enabled tools in 2016, this new technology allows users to search for items in an image and specify where they should be located within the image. The research identifies the networks that localize and describe regions of an image as well as the relationships between things. The goal of this research was to improve the future of search using visual data, contextual search functions, and AI. A combination of multiple machine learning technologies led to this breakthrough.
This is presentation about what skip-gram and CBOW is in seminar of Natural Language Processing Labs.
- how to make vector of words using skip-gram & CBOW.
Word embeddings have received a lot of attention since some Tomas Mikolov published word2vec in 2013 and showed that the embeddings that the neural network learned by reading a large corpus of text preserved semantic relations between words. As a result, this type of embedding started being studied in more detail and applied to more serious NLP and IR tasks such as summarization, query expansion, etc More recently, researchers and practitioners alike have come to appreciate the power of this type of approach and have started a cottage industry of modifying Mikolovs original approach to many different areas.
In this talk we will cover the implementation and mathematical details underlying tools like word2vec and some of the applications word embeddings have found in various areas. Starting from an intuitive overview of the main concepts and algorithms underlying the neural network architecture used in word2vec we will proceed to discussing the implementation details of the word2vec reference implementation in tensorflow. Finally, we will provide a birds eye view of the emerging field of 2vec" (dna2vec, node2vec, etc...) methods that use variations of the word2vec neural network architecture.
This (long) version of the Tutorial was presented at #O'Reilly AI 2017 in San Francisco. See https://bmtgoncalves.github.io/word2vec-and-friends/ for further details.
Intro to SVM with its maths and examples. Types of SVM and its parameters. Concept of vector algebra. Concepts of text analytics and Natural Language Processing along with its applications.
DELAB - sequence generation seminar
Title
Open vocabulary problem
Table of contents
1. Open vocabulary problem
1-1. Open vocabulary problem
1-2. Ignore rare words
1-3. Approximative Softmax
1-4. Back-off Models
1-5. Character-level model
2. Solution1: Byte Pair Encoding(BPE)
3. Solution2: WordPieceModel(WPM)
The document summarizes the implementation of a skip-gram model for word embeddings. It describes the preprocessing steps including lemmatization and subsampling. It then explains how the skip-gram algorithm was implemented to train word and context embeddings by maximizing the log-likelihood of predicted contexts. Gradient descent was used to update the embedding matrices based on backpropagation of the loss. Evaluation showed the embeddings captured some semantic relationships for frequent words but not rare words. Additional experiments explored the effect of initialization and combining word and context embeddings.
This document provides an introduction to word embeddings in deep learning. It defines word embeddings as vectors of real numbers that represent words, where similar words have similar vector representations. Word embeddings are needed because they allow words to be treated as numeric inputs for machine learning algorithms. The document outlines different types of word embeddings, including frequency-based methods like count vectors and co-occurrence matrices, and prediction-based methods like CBOW and skip-gram models from Word2Vec. It also discusses tools for generating word embeddings like Word2Vec, GloVe, and fastText. Finally, it provides a tutorial on implementing Word2Vec in Python using Gensim.
Deep learning uses multilayered neural networks to process information in a robust, generalizable, and scalable way. It has various applications including image recognition, sentiment analysis, machine translation, and more. Deep learning concepts include computational graphs, artificial neural networks, and optimization techniques like gradient descent. Prominent deep learning architectures include convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks.
The presenation slide which introduce sparse autoencoders. This presentation covers the property of SAE and use case to LLM interpretivity. This slide also demonstrate the connection with Vector-quantized autoencoders and SAE.
Polynomial Tensor Sketch for Element-wise Matrix Function (ICML 2020)ALINLAB
油
1) The document proposes a polynomial tensor sketch method to approximate element-wise matrix functions in linear time. It combines tensor sketching, which can approximate matrix monomials fast, with polynomial approximation of the target function.
2) Coreset-based regression is used to efficiently compute optimal polynomial coefficients by selecting a small subset of rows.
3) Experiments show the method outperforms alternatives like random Fourier features for applications like kernel approximation, kernel SVM, and Sinkhorn algorithm, providing speedups of up to 49x.
GloVe is an unsupervised learning algorithm for obtaining vector representations of words. It combines the advantages of global matrix factorization and local context window models by training only on the nonzero elements of a word-word co-occurrence matrix. The GloVe model represents word meanings as vectors such that the ratio of the probabilities of any two words appearing together is approximated by the ratio of the dot product of their vector representations. Experiments show GloVe outperforms other models on word analogy, similarity and named entity recognition tasks.
Support Vector Machines USING MACHINE LEARNING HOW IT WORKSrajalakshmi5921
油
This document discusses support vector machines (SVM), a supervised machine learning algorithm used for classification and regression. It explains that SVM finds the optimal boundary, known as a hyperplane, that separates classes with the maximum margin. When data is not linearly separable, kernel functions can transform the data into a higher-dimensional space to make it separable. The document discusses SVM for both linearly separable and non-separable data, kernel functions, hyperparameters, and approaches for multiclass classification like one-vs-one and one-vs-all.
This presentation is a part of the COP2272C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce students to the C++ language and the fundamentals of object orientated programming..
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
This copyright notice specifies that DeepLearning.AI slides are distributed under a Creative Commons license, can be used non-commercially for education
Week 4 advanced labeling, augmentation and data preprocessingAjay Taneja
油
This document provides an overview of advanced machine learning techniques for data labeling, augmentation, and preprocessing. It discusses semi-supervised learning, active learning, weak supervision, and various data augmentation strategies. For data labeling, it describes how semi-supervised learning leverages both labeled and unlabeled data, while active learning intelligently samples data and weak supervision uses noisy labels from experts. For data augmentation, it explains how existing data can be modified through techniques like flipping, cropping, and padding to generate more training examples. The document also introduces the concepts of time series data and how time ordering is important for modeling sequential data.
This is the Machine Learning Engineering in Production Course notes. This is the Week 3 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
Machine Learning Data Life Cycle in Production (Week 2 feature engineering...Ajay Taneja
油
This is the Machine Learning Engineering in Production Course notes. This is the Week 2 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
Course 2 Machine Learning Data LifeCycle in Production - Week 1Ajay Taneja
油
This is the Machine Learning Engineering in Production Course notes. This is the Week 1 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
APM event hosted by the South Wales and West of England Network (SWWE Network)
Speaker: Aalok Sonawala
The SWWE Regional Network were very pleased to welcome Aalok Sonawala, Head of PMO, National Programmes, Rider Levett Bucknall on 26 February, to BAWA for our first face to face event of 2025. Aalok is a member of APMs Thames Valley Regional Network and also speaks to members of APMs PMO Interest Network, which aims to facilitate collaboration and learning, offer unbiased advice and guidance.
Tonight, Aalok planned to discuss the importance of a PMO within project-based organisations, the different types of PMO and their key elements, PMO governance and centres of excellence.
PMOs within an organisation can be centralised, hub and spoke with a central PMO with satellite PMOs globally, or embedded within projects. The appropriate structure will be determined by the specific business needs of the organisation. The PMO sits above PM delivery and the supply chain delivery teams.
For further information about the event please click here.
Research & Research Methods: Basic Concepts and Types.pptxDr. Sarita Anand
油
This ppt has been made for the students pursuing PG in social science and humanities like M.Ed., M.A. (Education), Ph.D. Scholars. It will be also beneficial for the teachers and other faculty members interested in research and teaching research concepts.
This course provides students with a comprehensive understanding of strategic management principles, frameworks, and applications in business. It explores strategic planning, environmental analysis, corporate governance, business ethics, and sustainability. The course integrates Sustainable Development Goals (SDGs) to enhance global and ethical perspectives in decision-making.
How to create security group category in Odoo 17Celine George
油
This slide will represent the creation of security group category in odoo 17. Security groups are essential for managing user access and permissions across different modules. Creating a security group category helps to organize related user groups and streamline permission settings within a specific module or functionality.
Unit 1 Computer Hardware for Educational Computing.pptxRomaSmart1
油
Computers have revolutionized various sectors, including education, by enhancing learning experiences and making information more accessible. This presentation, "Computer Hardware for Educational Computing," introduces the fundamental aspects of computers, including their definition, characteristics, classification, and significance in the educational domain. Understanding these concepts helps educators and students leverage technology for more effective learning.
Managing expiration dates of products in odooCeline George
油
Odoo allows users to set expiration dates at both the product and batch levels, providing flexibility and accuracy. By using Odoo's expiration date management, companies can minimize waste, optimize stock rotation, and maintain high standards of product quality. The system allows users to set expiration dates at both the product and batch levels, providing flexibility and accuracy.
Digital Tools with AI for e-Content Development.pptxDr. Sarita Anand
油
This ppt is useful for not only for B.Ed., M.Ed., M.A. (Education) or any other PG level students or Ph.D. scholars but also for the school, college and university teachers who are interested to prepare an e-content with AI for their students and others.