Deep Learning for Natural Language Processing (NLP)
Published by Pearson
Powerful, Efficient Processing of Natural Language with Deep Neural Networks
Relatively obscure a few short years ago, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, super-human game-playing, and natural language processing (NLP).
This Live Training builds on the fundamentals of Deep Learning to develop a specialization in handling natural language data and building powerful, efficient, broadly-applicable predictive models that have sequences of words as inputs.
To facilitate an intuitive understanding of NLP and neural-network layers particularly well-suited to processing natural language data (e.g., word vectors, RNNs, GRUs, LSTMs), essential theory will be introduced visually and pragmatically. Theory will immediately be brought to life with interactive demos and hands-on exercises within Jupyter notebooks that feature Python, TensorFlow 2, and and Keras layers, the high-level TensorFlow API.
This is part of Jon Krohn’s Complete Artificial Intelligence Series, a collection of interactive trainings that together comprehensively cover the foundations of modern AI approaches. The recommended progression through the Series is to take one of these two introductory sessions:
Following either of the introductory sessions (or if you’re familiar with the content covered in Chapters 1 and 5-9 of Jon Krohn’s Deep Learning Illustrated book), you’re well-prepared to specialize in any of the other Live Trainings in the Complete Artificial Intelligence Series, which can be undertaken in any order you fancy:
- Deep Learning for Machine Vision and Image Generation
- Deep Learning for Natural Language Processing
- [Deep Reinforcement Learning](https://learning.oreilly.com/search/?query="deep reinforcement learning")
- Deep Dive into Deep Learning with TensorFlow 2
(Note that at any given time, only a subset of these classes will be scheduled and open for registration. To be pushed notifications of upcoming classes in the series, sign up for the instructor’s email newsletter at jonkrohn.com.)
What you’ll learn and how you can apply it
- Preprocess natural language data and create word vectors for use in machine learning applications
- Leverage Keras and its TensorFlow backend to make predictions with Deep Learning models trained on natural language
- Improve Deep Learning model performance by tuning hyperparameters
This live event is for you because...
- You already have a working understanding of the fundamentals of Deep Learning
- You want to apply state-of-the-art Deep Learning models to natural language data
- You want to be able to transform natural language into quantitative representations that can be used as inputs into a broad range of machine learning models
Prerequisites
- Experience with an object-oriented programming language, e.g., Python (all code demos during the training will be in Python)
- A working understanding of the fundamentals of Deep Learning would make it a lot easier to follow along with the training
Materials, downloads, or Supplemental Content needed in advance::
- During class, we’ll work on Jupyter notebooks interactively in the cloud via Google Colab. This requires nearly zero setup and instructions will be provided in class. If you’d like to take a sneak peak at the notebooks we’ll be using, check out https://github.com/jonkrohn/DLTFpT
Resources:
- If you’d like to brush up on analyzing data in Python, the topics covered in Pandas Data Analysis with Python Fundamentals LiveLessons will be sufficient for this training
- If you’d like to brush on the fundamentals of deep learning, you can read Chapter 1 and Chapters 5-9 of Jon Krohn’s Deep Learning Illustrated book.
- If you’d like to ensure you have a working understanding of the fundamentals of Deep Learning, the first three lessons of Jon Krohn’s Deep Learning with TensorFlow LiveLessons are the perfect prequel to this training
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1: The Power and Elegance of Deep Learning for NLP (45 min)
- Training Overview
- Introduction to Deep Learning for Natural Language Processing
- Easy, Intermediate, and Complex NLP Applications
- Deep Learning vs Traditional Machine Learning
- Review of Prerequisite Deep Learning Theory, including Artificial Neurons, Activation Functions, Cost Functions, Gradient Descent, Backpropagation, Weight Initialization, Dense Layers, Convolutional Layers, Max-Pooling, and Dropout
- Word Vectors: Representing Language as Embeddings
- Word Vector Arithmetic
- An Interactive Visualization of Vector-Space Embeddings
- Vector-Based Representations vs One-Hot Encodings
- Break + Q&A (5 minutes)
Segment 2: Modeling Natural Language Data (90 min)
- Best Practices for Preprocessing Natural Language Data for Machine Learning Applications
- Using word2vec to Create Word Vectors
- Document Classification with a Dense Neural Network
- Document Classification with a Convolutional Neural Network
- Break + Q&A (5 minutes)
Segment 3: Recurrent and Advanced Neural Networks (45 min)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory Units (LSTMs)
- Gated Recurrent Units (GRUs)
- Bi-Directional LSTMs
- Stacked LSTMs
- Parallel Network Architectures
- Transformer Architectures: BERT, ELMo, GPT-2, and Friends
Break + Q&A (5 minutes)
Your Instructor
Jon Krohn
Jon Krohn is Co-Founder of the AI software firm Y Carrot and a Fellow at Lightning AI. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.
Skills covered
- Deep Learning
- Reinforcement Learning
- Natural Language Processing