Model Finetuning for NLI

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Related lectures (31)

Page 2 of 4

Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.

Gaussian Mixture Models: Data Classification

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Pretraining Sequence-to-Sequence Models: BART and T5

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

Collaborative Data Science: Tools and Git Workflow

Explores tools like Git and Docker for collaborative data science projects.

Decision Trees: Classification

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Big Data Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Enterprise and Service-Oriented Architecture

Discusses solutions for business/IT alignment and the importance of updating business to IT models.

Deep Learning for NLP

Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.