Getting Started with NLP Deep Learning Using PyTorch and fastai

By Gianni Rosa Gallina
This course will teach you how to start using fastai library and PyTorch to obtain near-state-of-the-art results with Deep Learning NLP for text classification. It will give you a theoretical background and show how to take models to production.
Play course overview
Course info
Level
Intermediate
Updated
Mar 21, 2019
Duration
2h 13m
Table of contents
Course Overview
Course Overview 2m
Exploring the fastai Library
Introducing the fastai Library 4m What Else Can You Do with the fastai Library? 3m What's a Great Way to Learn ML/DL? 3m
Setting up a Development Environment
Requirements and Install Options 4m Set up a Dev Environment for fastai 1.0 4m Other Dev Environment Options 3m
Building a Text/Topic Classifier with Transfer Learning
Module Overview 4m Preparing the Data 6m Introduction to Language Models 6m Language Model Fine-tuning 5m Finalizing Language Model Tuning and Testing 5m Training a Document Classifier with Transfer Learning 7m Discussing the Results and Summary 4m
Using Deep Learning for NLP
Introduction 2m Transfer Learning and Fine-tuning 2m Introduction to ULMFiT 7m Discriminitive Fine-tuning 2m Hyper-parameters Scheduling 3m AWD-LSTM Language Model 2m Dropout 3m Embedding Layer 2m Text Pre-processing 4m Batch Data Loading 6m Summary 3m
Going from Prototype to Production
Introduction and Dev Environment Setup 3m Preparing a Model for Production 2m Scoring Script for Local Test 4m Set up Azure ML Workspace 2m Scoring Script for AML Services 1m Create the Production Image 2m Deploy the Production Image 3m Test the Web Service 1m Summary 2m
Building a Custom Language Model from Scratch
Introduction 1m Getting the Data 4m Text Pre-processing 3m Pre-train the Language Model 3m Verify the Quality of the Language Model 2m Scripts for Training the Full Model 1m Summary 1m
Recapping and Next Steps
Course Recap 3m Next Steps 4m
Description
Course info
Level
Intermediate
Updated
Mar 21, 2019
Duration
2h 13m
Description

In this course, Getting Started with NLP Deep Learning Using PyTorch and fastai, we'll have a look at the amazing fastai library, built on top of the PyTorch Deep Learning Framework, to learn how to perform Natural Language Processing (NLP) with Deep Neural Networks, and how to achieve some of the most recent state-of-the-art results in text classification. First, we’ll learn how to train a model for text classification very quickly, thanks to the fastai library and transfer learning. Next, we'll explore some of the theory behind Deep Learning NLP techniques, and how to deploy our models to production in Microsoft Azure. Finally, we’ll discover how to train a custom language model from scratch. When you’re finished with this course, you’ll know why fastai and PyTorch are great frameworks, how to train deep learning models for NLP tasks on your own datasets, and how to bring them to production.

About the author
Gianni Rosa Gallina
About the author

Gianni is an R&D Senior Software Engineer in Deltatre's Innovation Lab, based in Italy. A Microsoft MVP since 2011, he has been focused on emerging technologies, AI, and Virtual/Augmented/Mixed Reality since 2013.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone. My name is Gianni Rosa Gallina, and welcome to my course, Getting Started with NLP Deep Learning Using PyTorch and fastai. I am an R&D specialist and senior software engineer in the Innovation Lab of Deltatre, based in Italy. In this course, we are going to have a look at the amazing fastai library built on top of PyTorch deep learning framework to learn how to perform natural language processing with deep learning neural networks, how to achieve some of the most recent state-of-the-art results in this field. Some of the major topics that we will cover include data preparation for deep learning NLP, from raw text to embeddings, learn how to leverage transfer learning in NLP for classification tasks, understanding and building a custom language model, going from prototype to production once we are satisfied with our models. By the end of this course, you'll know why fastai and PyTorch are great frameworks, how it's quick and easy to train deep learning models for natural language processing tasks on your own dataset and bring them to production. Before beginning the course, you should be familiar with general machine learning and deep learning concepts, some basic Python programming and dev tools, PyTorch basics, and Microsoft Azure Machine Learning services. I hope you will join me on this journey to learn some NLP deep learning concepts with the Getting Started with NLP Deep Learning Using PyTorch and fastai course, at Pluralsight.

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