Natural language processing (NLP) involves the application of machine learning and other statistical techniques to derive insights from human language. With large volumes of data exchanged as text (in the form of documents, tweets, email, chat, and so on), NLP techniques are indispensable to modern intelligent applications. The applications range from enterprise to pedestrian.
Delip Rao explores natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.
Day 1
Day 2
Sequence modeling
Recurrent neural networks (RNNs) to model sequences
Structured prediction methods
Attention
From sequence models to sequence-to-sequence models
Advanced topics
DL modeling for common NLP tasks
Choose your own adventure
DL for NLP: Best practices
Wrap-up and Q&A
Delip Rao is the founder of Joostware, a San Francisco-based company specializing in consulting and building IP in natural language processing and deep learning. Delip is a well-cited researcher in natural language processing and machine learning and has worked at Google Research, Twitter, and Amazon (Echo) on various NLP problems. He is interested in building cost-effective, state-of-the-art AI solutions that scale well. Delip has an upcoming book on NLP and deep learning from O’Reilly.
Get the Platinum pass or the Training pass to add this course to your package.
Comments on this page are now closed.
For exhibition and sponsorship opportunities, email aisponsorships@oreilly.com
For information on trade opportunities with O'Reilly conferences, email partners@oreilly.com
View a complete list of AI contacts
©2017, O'Reilly Media, Inc. • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • confreg@oreilly.com
Comments
I’ve set up the environment, cloned the repo, and installed all dependencies. Is there a link I’ve missed where we need to download the ~2GB of data ahead of time, or will we be downloading it during the class?
I have done all the installation of the required software for the course. Where should I unzip the tutorial file pytorch-nlp-tutorial-sf2017.zip?
Hi Delip, I have the same question as Jack. Is there a plan to add more spots?
Hi Delip, I saw the class is already full, is there a plan to accommodate more spaces?
@Gautam, please make sure you have at least 2G free. Obviously, more the better.
@Matthew, PyTorch does not have native windows support reliably yet. If you are using Windows, you have two options: 1) use a Linux virtual machine (VirtualBox or other options you get from googling “windows linux virtual machine”), 2) Use Docker. I believe the O’Reilly folks are writing a docker file in the works. When that happens, we will update the environment setup with additional details. So watch for that.
Delip – thank you for the link regarding environment setup. I hate to be annoying, but would it be possible to provide a similar reference for Windows users? This link only provides assistance for OSX/Linux users. Thank you!
Hi,
How much data will we be using? The reason I am asking because my laptop has limited space so want to make sure I keep enough space for data required in the training (I am not talking about model training).
This page will be updated soon with environment setup and a revised outline, but for now, please follow the instructions in here for the environment setup.
Is this fully booked? Wondering if there is 1 more space . Please confirm.
Hi, are there any prerequisites for this training like preinstalling software needed or any other preparatory steps?