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!
- 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 "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)
- Basic math (taking derivatives, matrix arithmetic, probability) is helpful
- Python, Numpy, Matplotlib
- 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
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 masters degree in computer engineering with a specialization in machine learning and pattern recognition.
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.