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Hands-On Natural Language Processing with Pytorch

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Hands-On Natural Language Processing with Pytorch, available at $44.99, has an average rating of 3.65, with 30 lectures, based on 35 reviews, and has 184 subscribers.

You will learn about Processing insightful information from raw data using NLP techniques with PyTorch Working with PyTorch to take advantage of its maximum speed and flexibility Traditional and modern NLP methods & tools like NLTK, Spacy, Word2Vec & Gensim Implementing word embedding model and using it with the Gensim toolkit Sequence-to-sequence models (used in translation) that read one sequence & produces another Usage of LSTMs using PyTorch for Sentiment Analysis and how its different from RNNs Comparing and analysing results using Attention networks to improve your project’s performance This course is ideal for individuals who are If you’re a developer, researcher or aspiring AI data scientist ready to dive deeper into this rapidly growing area of artificial intelligence then this course is for you! It is particularly useful for If you’re a developer, researcher or aspiring AI data scientist ready to dive deeper into this rapidly growing area of artificial intelligence then this course is for you!.

Enroll now: Hands-On Natural Language Processing with Pytorch

Summary

Title: Hands-On Natural Language Processing with Pytorch

Price: $44.99

Average Rating: 3.65

Number of Lectures: 30

Number of Published Lectures: 30

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

  • Processing insightful information from raw data using NLP techniques with PyTorch
  • Working with PyTorch to take advantage of its maximum speed and flexibility
  • Traditional and modern NLP methods & tools like NLTK, Spacy, Word2Vec & Gensim
  • Implementing word embedding model and using it with the Gensim toolkit
  • Sequence-to-sequence models (used in translation) that read one sequence & produces another
  • Usage of LSTMs using PyTorch for Sentiment Analysis and how its different from RNNs
  • Comparing and analysing results using Attention networks to improve your project’s performance

Who Should Attend

  • If you’re a developer, researcher or aspiring AI data scientist ready to dive deeper into this rapidly growing area of artificial intelligence then this course is for you!

Target Audiences

  • If you’re a developer, researcher or aspiring AI data scientist ready to dive deeper into this rapidly growing area of artificial intelligence then this course is for you!

The main goal of this course is to train you to perform complex NLP tasks (and build intelligent language applications) using Deep Learning with PyTorch.

You will build two complete real-world NLP applications throughout the course. The first application is a Sentiment Analyzer that analyzes data to determine whether a review is positive or negative towards a particular movie. You will then create an advanced Neural Translation Machine that is a speech translation engine, using Sequence to Sequence models with the speed and flexibility of PyTorch to translate given text into different languages.

By the end of the course, you will have the skills to build your own real-world NLP models using PyTorch’s Deep Learning capabilities.

This course uses Python 3.6, Pytorch 1.0, NLTK 3.3.0, and Spacy 2.0 , while not the latest version available, it provides relevant and informative content for legacy users of PyTorch.

About the Author:

Jibin  Mathew is a Tech-Entrepreneur, Artificial Intelligence enthusiast and  an active researcher. He has spent several years as a Software Solutions  Architect, with a focus on Artificial Intelligence for the past 5  years. He has architected and built various solutions in Artificial  Intelligence which includes solutions in Computer Vision, Natural  Language Processing/Understanding and Data sciences, pushing the limits  of computational performance and model accuracies. He is well versed  with concepts in Machine learning and Deep learning and serves a  consultant for clients from Retail, Environment, Finance and Health  care.   

Course Curriculum

Chapter 1: Up and Running with PyTorch

Lecture 1: The Course Overview

Lecture 2: Using Deep Learning in Natural Language Processing

Lecture 3: Functions and Features of PyTorch

Lecture 4: Installing and Setting Up PyTorch

Lecture 5: Understanding Sentiment Analysis and NMT

Chapter 2: Data Cleaning and Preprocessing for Sentiment Analysis

Lecture 1: NLTK and spaCy Installations

Lecture 2: Tokenization with NLTK

Lecture 3: Stop Words

Lecture 4: Lemmatization

Lecture 5: Pipelines

Chapter 3: Implement Word Embeddings with gensim

Lecture 1: Working with Word Embeddings

Lecture 2: Setting Up and Installing gensim

Lecture 3: Exploring Word Embeddings with gensim

Lecture 4: Understanding the Embeddings Created

Lecture 5: Pretrained Embeddings Using Word2vec

Chapter 4: Train RNNs and LSTMs Units for Sentiment Analysis

Lecture 1: Working with Recurrent Neural Network

Lecture 2: Implementing RNN

Lecture 3: Results with RNN

Lecture 4: Working with LSTM

Lecture 5: Implementing LSTM

Lecture 6: Results with LSTM

Chapter 5: Build a Neural Machine Translator

Lecture 1: Intro to seq2seq

Lecture 2: Installations

Lecture 3: Implementing seq2seq – Encoder

Lecture 4: Implementing seq2seq – Decoder

Lecture 5: Results with seq2seq

Chapter 6: Improve the Neural Machine Translation with Attention Networks

Lecture 1: Introduction to Attention Networks

Lecture 2: Implementing seq2seq – Encoder

Lecture 3: Results with Attention Network

Lecture 4: The Way Forward

Instructors

Rating Distribution

  • 1 stars: 5 votes
  • 2 stars: 3 votes
  • 3 stars: 7 votes
  • 4 stars: 9 votes
  • 5 stars: 11 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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