HOT

Learn PyTorch for Natural Language Processing

Development

Rating3.3
Enroll Now

Learn PyTorch for Natural Language Processing, available at $49.99, has an average rating of 3.25, with 70 lectures, 1 quizzes, based on 35 reviews, and has 234 subscribers.

You will learn about Work with Deep Learning models and architectures including layers, activations, loss functions, gradients, chain rule, forward and backward passes, and optimizers. Apply Deep Learning architectures to solve Machine Learning problems for Structured Datasets, Computer Vision, and Natural Language Processing. Utilize the concept of Transfer Learning by using pre-trained Deep Learning models to your own problems. Implementing the word embedding model and using it with the Gensim toolkit. Processing insightful information from raw data using NLP techniques with PyTorch. Comparing and analyzing results using Attention networks to improve your project’s performance. This course is ideal for individuals who are This course is for software development professionals, machine learning enthusiasts and Data Science professionals who would like to practically implement PyTorch and exploit its unique features in their Deep Learning projects. It is particularly useful for This course is for software development professionals, machine learning enthusiasts and Data Science professionals who would like to practically implement PyTorch and exploit its unique features in their Deep Learning projects.

Enroll now: Learn PyTorch for Natural Language Processing

Summary

Title: Learn PyTorch for Natural Language Processing

Price: $49.99

Average Rating: 3.25

Number of Lectures: 70

Number of Quizzes: 1

Number of Published Lectures: 70

Number of Published Quizzes: 1

Number of Curriculum Items: 71

Number of Published Curriculum Objects: 71

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Work with Deep Learning models and architectures including layers, activations, loss functions, gradients, chain rule, forward and backward passes, and optimizers.
  • Apply Deep Learning architectures to solve Machine Learning problems for Structured Datasets, Computer Vision, and Natural Language Processing.
  • Utilize the concept of Transfer Learning by using pre-trained Deep Learning models to your own problems.
  • Implementing the word embedding model and using it with the Gensim toolkit.
  • Processing insightful information from raw data using NLP techniques with PyTorch.
  • Comparing and analyzing results using Attention networks to improve your project’s performance.

Who Should Attend

  • This course is for software development professionals, machine learning enthusiasts and Data Science professionals who would like to practically implement PyTorch and exploit its unique features in their Deep Learning projects.

Target Audiences

  • This course is for software development professionals, machine learning enthusiasts and Data Science professionals who would like to practically implement PyTorch and exploit its unique features in their Deep Learning projects.

PyTorch: written in Python, is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. PyTorch is a Deep Learning framework that is a boon for researchers and data scientists.

This course takes a practical approach and is filled with real-world examples to help you create your own application using PyTorch! Learn the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. Get yourself acquainted with the advanced concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks. Moving further you will build real-world NLP applications such as Sentiment Analyzer & advanced Neural Translation Machine.

Contents and Overview

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, PyTorch Deep Learning in 7 Days is for those who are in a hurry to get started with PyTorch. You will be introduced to the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. This course is an attempt to break the myth that Deep Learning is complicated and show you that with the right choice of tools combined with a simple and intuitive explanation of core concepts, Deep Learning is as accessible as any other application development technologies out there. It’s a journey from diving deep into the fundamentals to getting acquainted with the advanced concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks. By the end of the course, you will be able to build Deep Learning applications with PyTorch.

The second course, Hands-On Natural Language Processing 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.

About the Authors:

  • Will Ballard is the chief technology officer at GLG, responsible for engineering and IT. He was also responsible for the design and operation of large data centres that helped run site services for customers including Gannett, Hearst Magazines, NFL, NPR, The Washington Post, and Whole Foods. He has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works (now Bank of America).

  • 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 as a consultant for clients from Retail, Environment, Finance and Health care.

Course Curriculum

Chapter 1: PyTorch Deep Learning in 7 Days

Lecture 1: The Course overview

Lecture 2: Quick Intro to PyTorch

Lecture 3: Installation and Jupyter Notebook Setup

Lecture 4: Tensors and Basic Tensor Operations

Lecture 5: Advanced Tensor Operations

Lecture 6: Loading and Saving Data

Lecture 7: Assignment

Lecture 8: Introduction to Neural Networks

Lecture 9: Creating a Neural Network with PyTorch Sequential

Lecture 10: Activations, Loss Functions, and Gradients

Lecture 11: Forward and Backward Passes

Lecture 12: Building a Network with nn.Module

Lecture 13: Assignment

Lecture 14: Loading Structured Data for Classification

Lecture 15: Preprocessing Data

Lecture 16: Classification, Accuracy, and the Confusion Matrix

Lecture 17: Loading Structured Data for Regression

Lecture 18: Neural Networks for Regression

Lecture 19: Assignment

Lecture 20: Convolutional Networks for Image Analysis

Lecture 21: Convolutional Concepts: Filters, Strides, Padding, and Pooling

Lecture 22: Implementing a Convolutional Network

Lecture 23: Visualizing Convolutional Network Layers

Lecture 24: Implementing an End-To-End Deep Convolutional Network

Lecture 25: Assignment

Lecture 26: Transfer Learning and Prebuilt Models

Lecture 27: Deep Learning with VGG

Lecture 28: Transfer Learning with VGG

Lecture 29: Transfer Learning with ResNet

Lecture 30: Assignment

Lecture 31: Recurrent Networks, RNN, and LSTM, GRU

Lecture 32: Text Modeling with Bag-of-Words

Lecture 33: Sentiment Analysis with Bag-of-Words

Lecture 34: Sentiment Analysis with Word Embeddings

Lecture 35: Assignment

Lecture 36: Introduction to GANs and DCGANs

Lecture 37: Implementing DCGAN Model with PyTorch

Lecture 38: Training and Evaluating DCGAN on an Image Dataset

Lecture 39: Improving Performance

Lecture 40: Assignment

Chapter 2: Hands-On Natural Language Processing 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

Lecture 6: NLTK and spaCy Installations

Lecture 7: Tokenization with NLTK

Lecture 8: Stop Words

Lecture 9: Lemmatization

Lecture 10: Pipelines

Lecture 11: Working with Word Embeddings

Lecture 12: Setting Up and Installing gensim

Lecture 13: Exploring Word Embeddings with gensim

Lecture 14: Understanding the Embeddings Created

Lecture 15: Pretrained Embeddings Using Word2vec

Lecture 16: Working with Recurrent Neural Network

Lecture 17: Implementing RNN

Lecture 18: Results with RNN

Lecture 19: Working with LSTM

Lecture 20: Implementing LSTM

Lecture 21: Results with LSTM

Lecture 22: Intro to seq2seq

Lecture 23: Installations

Lecture 24: Implementing seq2seq – Encoder

Lecture 25: Implementing seq2seq – Decoder

Lecture 26: Results with seq2seq

Lecture 27: Introduction to Attention Networks

Lecture 28: Implementing seq2seq – Encoder

Lecture 29: Results with Attention Network

Lecture 30: The Way Forward

Instructors

Rating Distribution

  • 1 stars: 4 votes
  • 2 stars: 7 votes
  • 3 stars: 12 votes
  • 4 stars: 6 votes
  • 5 stars: 6 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!

You may also like

Podcast: The top 8 ways to monetize by Podcasting

The Art of Storytelling: How to Connect with Audiences

Unbounce for Beginners: Build a Landing Page with Unbounce