Lecture

Anchor Text Indexing and Page Ranking

Related lectures (30)

Link-based Ranking: Fundamentals and Algorithms

Covers the fundamentals and algorithms of link-based ranking, including anchor text indexing, PageRank, HITS, and practical implementations.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Practical Computation of PageRank

Covers the practical computation of PageRank using iterative methods and the challenges of the eigen-vector method.

Link-Based Ranking: PageRank

Explores link-based ranking with a focus on PageRank algorithm and its practical application in web search engines.

Calcul and algorithms: methodology

Explores the PageRank algorithm used by Google to rank web pages.

Creating Links with <A>

Covers the creation of links using the tag in HTML.

Binary Sentiment Classifier Training

Covers the training of a binary sentiment classifier using an RNN.

Document Classification: Transformers and MLPs

Explores transformers and MLPs for document classification, emphasizing their benefits over traditional methods.

Foundations of Deep Learning: Transformer Architecture Overview

Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.

Neural Networks for NLP

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Link-Based Ranking: Introduction

Explores the significance of anchor text in link-based ranking and its impact on search results.

Convolutional Neural Networks

Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Deep Learning for NLP

Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.

Document Classification: Overview

Explores document classification methods, including k-Nearest-Neighbors, Naïve Bayes Classifier, transformer models, and multi-head attention.

Deep Generative Models: Part 2

Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.

Deep Learning Fundamentals

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Deep Learning: Multilayer Perceptron and Training

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Language Models: From Theory to Computation

Explores the mathematics of language models, covering architecture design, pre-training, and fine-tuning, emphasizing the importance of pre-training and fine-tuning for various tasks.

Natural Language Processing: Understanding Transformers and Tokenization

Provides an overview of Natural Language Processing, focusing on transformers, tokenization, and self-attention mechanisms for effective language analysis and synthesis.