Deep Learning: Recurrent Neural Networks in Python
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
- Basic math (taking derivatives, matrix arithmetic, probability) is helpful
- Python, Numpy, Matplotlib
*** 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...
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)
Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out
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
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.
I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering.
Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.
I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.
My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.