Deep Learning for NLP: Neural Network Architectures
Natural Language Processing
| Intermediate
- 19 Videos | 2h 30m 10s
- Includes Assessment
- Earns a Badge
Natural language processing (NLP) is constantly evolving with cutting edge advancements in tools and approaches. Neural network architecture (NNA) supports this evolution by providing a method of processing language-based information to solve complex data-driven problems. Explore the basic NNAs relevant to NLP problems. Learn different challenges and use cases for single-layer perceptron, multi-layer perceptron, and RNNs. Analyze data and its distribution using pandas, graphs, and charts. Examine word vector representations using one-hot encodings, Word2vec, and GloVe and classify data using recurrent neural networks. After you have completed this course, you will be able to use a product classification dataset to implement neural networks for NLP problems.
WHAT YOU WILL LEARN
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discover the key concepts covered in this courseillustrate single layer perceptron architecture of a neural networkillustrate MLP Architecture of Neural Networkdescribe RNN Architecture and how it can capture context in languagedescribe the various challenges of RNNillustrate different applications of basic Neural Network-based architecturedescribe the Amazon Product Reviews dataset and list the libraries that are required to be importeddescribe the steps to load the Amazon Product Reviews dataset into Google Colaboratoryexplore the data and its distribution in the Amazon Product Reviews datasetanalyze the product review data using pandas, graphs, and charts
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describe the steps involved in pre-processing the product review datasetillustrate word representations using one-hot encodingsillustrate word vector representations using neural network and Word2veccreate average feature vectors of all the words in the word vectorcreate word embeddings vector using Word2vecconstruct a RNN model with Word2Vec Embeddingsillustrate sentence vector representations using GloVe vectorsperform classification of product review data using RNNsummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 19sUP NEXT
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2.Basic Architecture of a Neural Network4m 10s
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3.Multilayer Perceptron (MLP)2m 48s
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4.Recurrent Neural Network (RNN) Architecture5m 5s
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5.Challenges in RNN3m 19s
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6.Applications of Neural Network-based Architecture1m 5s
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7.Introducing the Product Reviews Data11m 55s
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8.Loading Product Reviews Data into Google Colaboratory6m 55s
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9.Understanding Product Reviews Data15m 45s
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10.Exploring Product Reviews Data12m 40s
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11.Pre-processing Product Reviews Data7m 39s
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12.Applying Feature Engineering - Word Representation9m 54s
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13.Creating Vector Representations Using Word2vec15m 14s
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14.Averaging Feature Vectors13m 19s
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15.Creating Word Embeddings with Word2Vec11m 6s
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16.Constructing a RNN Model with Word2vec Embeddings8m 19s
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17.Using GloVe Vectors11m 10s
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18.Product Reviews Classification Using RNN7m 7s
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19.Course Summary1m 24s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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