Model Finetuning for NLI

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Related lectures (31)

Page 3 of 4

Introduces Renku, a platform for collaborative data science, emphasizing reproducibility, shareability, reusability, and security.

Headline Analysis: Language Impact & Success

Explores the influence of language on headline success through real-world data analysis and statistical testing.

Deep Learning: Designing Neural Network Models

Covers the design and optimization of neural network models in deep learning.

Pretraining: Transformers & Models

Explores pretraining models like BERT, T5, and GPT, discussing their training objectives and applications in natural language processing.

Contextual Representations: ELMO and BERT Overview

Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various tasks.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Data Science for Engineers: Part 2

Explores data manipulation, exploration, and visualization in data science projects using Python.

Introduction to Financial Markets and Time Series

Introduces financial markets, time series, machine learning applications in finance, and natural language processing.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Natural Language Processing: A Primer

Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.