Model: datasocietyco/bge-base-en-v1.5-course-recommender-v4python
Author: datasocietyco
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:48, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:1705.00652, base_model:BAAI/bge-base-en-v1.5, base_model:finetune:BAAI/bge-base-en-v1.5, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: BAAI/bge-base-en-v1.5
Card metadata: base_model: BAAI/bge-base-en-v1.5
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:48, loss:MultipleNegativesRankingLoss
widget: source_sentence: Fundamentals of Deep Learning for Multi GPUs. Find out how to use multiple GPUs to train neural networks and effectively parallelize\ntraining of deep neural networks using TensorFlow.. tags: multiple GPUs, neural networks, TensorFlow, parallelize. Languages: Course language: Python. Prerequisites: No prerequisite course required. Target audience: Professionals want to train deep neural networks on multi-GPU technology to shorten\nthe training time required for data-intensive applications.
sentences: Course Name:Hypothesis Testing in Python|Course Description:In this course, learners with foundational knowledge of statistical concepts will dive deeper into hypothesis testing by focusing on three standard tests of statistical significance: t-tests, F-tests, and chi-squared tests. Covering topics such as t-value, t-distribution, chi-square distribution, F-statistic, and F-distribution, this course will familiarize learners with techniques that will enable them to assess normality of data and goodness-of-fit and to compare observed and expected frequencies objectively.|Tags:f-distribution, chi-square distribution, f-statistic, t-distribution, t-value|Course language: Python|Target Audience:Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.|Prerequisite course required: Foundations of Statistics in Python, Course Name:Foundations of Data & AI Literacy for Managers|Course Description:Designed for managers leading teams and projects, this course empowers individuals to build data-driven organizations and integrate AI tools into daily operations. Learners will gain a foundational understanding of data and AI concepts and learn how to leverage them for actionable business insights. Managers will develop the skills to increase collaboration with technical experts and make informed decisions about analysis methods, ensuring their enterprise thrives in today’s data-driven landscape.|Tags:Designed, managers, leading, teams, projects,, course, empowers, individuals, build, data-driven, organizations, integrate, AI, tools, into, daily, operations., Learners, will, gain, foundational, understanding, data, AI, concepts, learn, how, leverage, them, actionable, business, insights., Managers, will, develop, skills, increase, collaboration, technical, experts, make, informed, decisions, about, analysis, methods,, ensuring, their, enterprise, thrives, today’s, data-driven, landscape.|Course language: None|Target Audience:No target audience|No prerequisite course required, Course Name:Fundamentals of Deep Learning for Multi GPUs|Course Description:Find out how to use multiple GPUs to train neural networks and effectively parallelize\ntraining of deep neural networks using TensorFlow.|Tags:multiple GPUs, neural networks, TensorFlow, parallelize|Course language: Python|Target Audience:Professionals want to train deep neural networks on multi-GPU technology to shorten\nthe training time required for data-intensive applications|No prerequisite course required, source_sentence: Data Visualization Design & Storytelling. This course focuses on the fundamentals of data visualization, which helps support data-driven decision-making and to create a data-driven culture.. tags: data driven culture, data analytics, data literacy, data quality, storytelling, data science. Languages: Course language: TBD. Prerequisites: No prerequisite course required. Target audience: Professionals who would like to understand more about how to visualize data, design and concepts of storytelling through data..
sentences: Course Name:Building Transformer-Based NLP Applications (NVIDIA)|Course Description:Learn how to apply and fine-tune a Transformer-based Deep Learning model to Natural Language Processing (NLP) tasks. In this course, you'll construct a Transformer neural network in PyTorch, Build a named-entity recognition (NER) application with BERT, Deploy the NER application with ONNX and TensorRT to a Triton inference server. Upon completion, you’ll be proficient i.n task-agnostic applications of Transformer-based models. Data Society's instructors are certified by NVIDIA’s Deep Learning Institute to teach this course.|Tags:named-entity recognition, text, Natural language processing, classification, NLP, NER|Course language: Python|Target Audience:Professionals with basic knowledge of neural networks and want to expand their knowledge in the world of Natural langauge processing|No prerequisite course required, Course Name:Nonlinear Regression in Python|Course Description:In this course, learners will practice implementing a variety of nonlinear regression techniques in Python to model complex relationships beyond simple linear patterns. They will learn to interpret key transformations, including logarithmic (log-log, log-linear) and polynomial models, and identify interaction effects between predictor variables. Through hands-on exercises, they will also develop practical skills in selecting, fitting, and validating the most appropriate nonlinear model for their data.|Tags:nonlinear, regression|Course language: Python|Target Audience:This is an intermediate level course for data scientists who want to learn to understand and estimate relationships between a set of independent variables and a continuous dependent variable.|Prerequisite course required: Multiple Linear Regression, Course Name:Data Visualization Design & Storytelling|Course Description:This course focuses on the fundamentals of data visualization, which helps support data-driven decision-making and to create a data-driven culture.|Tags:data driven culture, data analytics, data literacy, data quality, storytelling, data science|Course language: TBD|Target Audience:Professionals who would like to understand more about how to visualize data, design and concepts of storytelling through data.|No prerequisite course required, source_sentence: Foundations of Probability Theory in Python. This course guides learners through a comprehensive review of advanced statistics topics on probability, such as permutations and combinations, joint probability, conditional probability, and marginal probability. Learners will also become familiar with Bayes’ theorem, a rule that provides a way to calculate the probability of a cause given its outcome. By the end of this course, learners will also be able to assess the likelihood of events being independent to indicate whether further statistical analysis is likely to yield results.. tags: conditional probability, bayes' theorem. Languages: Course language: Python. Prerequisites: Prerequisite course required: Hypothesis Testing in Python. Target audience: Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools..
sentences: Course Name:Foundations of Probability Theory in Python|Course Description:This course guides learners through a comprehensive review of advanced statistics topics on probability, such as permutations and combinations, joint probability, conditional probability, and marginal probability. Learners will also become familiar with Bayes’ theorem, a rule that provides a way to calculate the probability of a cause given its outcome. By the end of this course, learners will also be able to assess the likelihood of events being independent to indicate whether further statistical analysis is likely to yield results.|Tags:conditional probability, bayes' theorem|Course language: Python|Target Audience:Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.|Prerequisite course required: Hypothesis Testing in Python, Course Name:Foundations of Generative AI|Course Description:Foundations of Generative AI|Tags:Foundations, Generative, AI|Course language: None|Target Audience:No target audience|No prerequisite course required, Course Name:Data Science for Managers|Course Description:This course is designed for managers seeking to bolster their data literacy with a deep dive into data science tools and teams, project life cycles, and methods.|Tags:data driven culture, data analytics, data quality, storytelling, data science|Course language: TBD|Target Audience:This course is targeted for those who would like to understand more about data literacy, make more informed decisions and identify data-driven solutions through data science tools and methods.|No prerequisite course required
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Created: 2024-12-16 11:25:06