MLG 022 Deep NLP 1

This podcast episode explores the impact of deep learning, particularly recurrent neural networks (RNNs), on natural language processing (NLP). It discusses the concepts of feature engineering and feature learning in deep learning, as well as the distinction between sequence and non-sequence models in NLP. The episode delves into the inner workings of RNNs, highlighting their ability to process sequential data and perform language modeling. It also introduces the similarities between RNNs and hidden Markov models (HMMs). The episode further introduces the concept of sequence-to-sequence RNNs and compares them to a crime scene investigation to explain their role in NLP tasks. The importance of representing words as vectors, particularly word embeddings, is discussed, along with the Word2Vec model for creating word embeddings. The episode concludes with suggested resources for further learning about RNNs and deep NLP.

Outlines

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Machine Learning Guide

Deep Learning Revolutionizes Natural Language Processing

Feature Engineering and Learning in Deep Learning

Neural Networks in Natural Language Processing: Sequence and Non-Sequence Models

Recurrent Neural Networks vs Feedforward Neural Networks: Understanding the Differences

Recurrent Neural Networks: A Deep Dive into Recursion and Language Modeling

RNN: A Deep Learning Equivalent of Hidden Markov Model

Sequence-to-Sequence RNNs: Encoding and Decoding for NLP Tasks

From Crime Scene to Meaning: The Role of RNNs in Encoding and Decoding

Word Representation and Word Embedding

Word Embeddings: Using Vector Space to Represent Words with Meaning

Word Embeddings: A Guide to Vector Space Models

Word2Vec: Learning Word Embeddings through Context Prediction

Word2Vec and GloVe: Embedding Words in Vector Space

RNNs and Deep NLP Learning Resources

MLG 022 Deep NLP 1

Machine Learning Guide

00:00Deep Learning Revolutionizes Natural Language Processing

Deep Learning Revolutionizes Natural Language Processing

03:27Feature Engineering and Learning in Deep Learning

Feature Engineering and Learning in Deep Learning

07:23Neural Networks in Natural Language Processing: Sequence and Non-Sequence Models

Neural Networks in Natural Language Processing: Sequence and Non-Sequence Models

10:42Recurrent Neural Networks vs Feedforward Neural Networks: Understanding the Differences

Recurrent Neural Networks vs Feedforward Neural Networks: Understanding the Differences

12:57Recurrent Neural Networks: A Deep Dive into Recursion and Language Modeling

Recurrent Neural Networks: A Deep Dive into Recursion and Language Modeling

16:20RNN: A Deep Learning Equivalent of Hidden Markov Model

RNN: A Deep Learning Equivalent of Hidden Markov Model

20:39Sequence-to-Sequence RNNs: Encoding and Decoding for NLP Tasks

Sequence-to-Sequence RNNs: Encoding and Decoding for NLP Tasks

24:46From Crime Scene to Meaning: The Role of RNNs in Encoding and Decoding

From Crime Scene to Meaning: The Role of RNNs in Encoding and Decoding

27:17Word Representation and Word Embedding

Word Representation and Word Embedding

31:04Word Embeddings: Using Vector Space to Represent Words with Meaning

Word Embeddings: Using Vector Space to Represent Words with Meaning

35:34Word Embeddings: A Guide to Vector Space Models

Word Embeddings: A Guide to Vector Space Models

37:19Word2Vec: Learning Word Embeddings through Context Prediction

Word2Vec: Learning Word Embeddings through Context Prediction

42:11Word2Vec and GloVe: Embedding Words in Vector Space

Word2Vec and GloVe: Embedding Words in Vector Space

45:45RNNs and Deep NLP Learning Resources

RNNs and Deep NLP Learning Resources