It’s hard to believe it's been over a year since I released my first course on Deep Learning
(natural language processing).
A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you.
So what is this course all about, and how have things changed since then?
In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks
), CNNs (convolutional neural networks
), and word embedding
algorithms such as word2vec and GloVe.
This course takes you to a higher systems level of thinking.
Since you know how these things work, it’s time to build systems using these components.
At the end of this course, you'll be able to build applications for problems like:
- text classification (examples are sentiment analysis and spam detection)
- neural machine translation
- question answering
In the bonus section, we'll be looking at speech recognition
using Deep Learning.
We'll take a brief look chatbots
and as you’ll learn in this course, this problem is actually no different from machine translation and question answering.
To solve these problems, we’re going to look at some advanced Deep NLP techniques, such as:
- bidirectional RNNs
- seq2seq (sequence-to-sequence)
- memory networks
All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Python libraries such as Keras
, and Matpotlib
to make things super easy and focus on the high-level concepts. I am always available to answer your questions and help you along your data science journey.
This course focuses on "how to build and understand"
, not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation
. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
See you in class!
- Decent Python coding skills
- Understand RNNs, CNNs, and word embeddings
- Know how to build, train, and evaluate a neural network in Keras
Tips for success:
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don't, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- The best exercises will take you days or weeks to complete.
- Write code yourself, don't just sit there and look at my code. This is not a philosophy course!