Language modeling is the task of assigning probabilities to sequences of words and is one of the most important tasks in natural language processing. Given the context of one word or a sequence of words in the language that the language model was trained on, the model should provide the next most probable words or sequence of words that follow from the given sequence of words in the sentence.

In this third workshop of the Your Path to Deep Learning series, we will learn how to perform language modeling on the Penn Treebank data set by creating a Recurrent Neural Network using long short-term memory (LSTM) units in a Jupyter notebook.

Implementing a Recurrent Neural Network with LSTM

Natural Language Processing with Deep Learning

Developers interested in building deep learning models using Python

Developers interested in Natural Language Processing

Deep learning & machine learning enthusiasts

Developers discovering deep learning algorithms

Developers building use cases around deep learning

All Developers interested in analytics and data science are welcome to attend the webinar

You must have some background in programming. Python is preferred.

You must understand the basics of Machine Learning. You can watch this webinar as a refresher: https://www.crowdcast.io/e/path-to-ai1

Log in to your existing account or Sign up for a free account IBM Cloud at https://ibm.biz/YourPathToDeepLearning

Karim Deif, Client Developer Advocate @ IBM
(https://www.linkedin.com/in/karimdeif/)
Mridul Bhandari, Developer Advocate @ IBM
(https://www.linkedin.com/in/mridul-bhandari)

Please note: We will be providing a badge & certificate for this series. At the end of the series, you will be asked to enrol into a course "Deep Learning with TensorFlow" on https://cognitiveclass.ai. Once you complete the quizzes in the modules, you will be provided with a badge and a certificate.

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Your Path to Deep Learning: Language Processing using RNN with TensorFlow