GRU, LSTM, Time Series Forecasting, Stock Predictions, Natural Language Processing (NLP) using Artificial Intelligence
What you'll learn
·
Apply RNNs to Time Series Forecasting (tackle the ubiquitous
"Stock Prediction" problem)
·
Apply RNNs to Natural Language Processing (NLP) and Text
Classification (Spam Detection)
·
Apply RNNs to Image Classification
·
Understand the simple recurrent unit (Elman unit), GRU, and LSTM
(long short-term memory unit)
·
Write various recurrent networks in Tensorflow 2
·
Understand how to mitigate the vanishing gradient problem
Requirements
·
Basic math (taking derivatives, matrix arithmetic, probability)
is helpful
·
Python, Numpy, Matplotlib
Description
*** NOW IN TENSORFLOW 2 and
PYTHON 3 ***
Learn about one of the most powerful Deep
Learning architectures yet!
The Recurrent Neural Network (RNN) has
been used to obtain state-of-the-art results in sequence modeling.
This includes time series analysis, forecasting and natural
language processing (NLP).
Learn about why RNNs beat old-school machine
learning algorithms like Hidden Markov Models.
This course will teach you:
- The basics of machine learning
and neurons (just a review to get you warmed up!)
- Neural networks for
classification and regression (just a review to get you warmed up!)
- How to model sequence data
- How to model time series data
- How to model text data for NLP
(including preprocessing steps for text)
- How to build an RNN using
Tensorflow 2
- How to use a GRU and LSTM in
Tensorflow 2
- How to do time series
forecasting with Tensorflow 2
- How to predict stock
prices and stock returns with LSTMs in Tensorflow 2 (hint: it's
not what you think!)
- How to use Embeddings in
Tensorflow 2 for NLP
- How to build a Text
Classification RNN for NLP (examples: spam detection, sentiment analysis,
parts-of-speech tagging, named entity recognition)
All of the materials required for this course
can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow.
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!
"If you can't implement it, you don't
understand it"
- Or as the great physicist
Richard Feynman said: "What I cannot create, I do not
understand".
- My courses are the ONLY courses
where you will learn how to implement machine learning algorithms from
scratch
- Other courses will teach you
how to plug in your data into a library, but do you really need help with
3 lines of code?
- After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:
- matrix addition, multiplication
- basic probability (conditional
and joint distributions)
- Python coding: if/else, loops,
lists, dicts, sets
- Numpy coding: matrix and vector
operations, loading a CSV file
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
- Check out the lecture
"Machine Learning and AI Prerequisite Roadmap" (available
in the FAQ of any of my courses, including the free Numpy course)
Who this course is
for:
·
Students, professionals, and anyone else interested in Deep
Learning, Time Series Forecasting, Sequence Data, or NLP
·
Software Engineers and Data Scientists who want to level up
their career
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