Size: 1.36 GB
GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences
What you’ll learn
 Understand the simple recurrent unit (Elman unit)

Understand the GRU (gated recurrent unit)

Understand the LSTM (long shortterm memory unit)
 Write various recurrent networks in Theano
 Understand backpropagation through time
 Understand how to mitigate the vanishing gradient problem
 Solve the XOR and parity problems using a recurrent neural network
 Use recurrent neural networks for language modeling
 Use RNNs for generating text, like poetry
 Visualize word embeddings and look for patterns in word vector representations
Requirements
 Calculus
 Linear algebra
 Python, Numpy, Matplotlib
 Write a neural network in Theano
 Understand backpropagation
 Probability (conditional and joint distributions)
 Write a neural network in Tensorflow
Description
Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences – but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not – and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades. So what’s going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? calculus
 linear algebra
 probability (conditional and joint distributions)
 Python coding: if/else, loops, lists, dicts, sets
 Numpy coding: matrix and vector operations, loading a CSV file
 Deep learning: backpropagation, XOR problem
 Can write a neural network in Theano and Tensorflow
 Watch it at 2x.
 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!
 Realize that most exercises will take you days or weeks to complete.
 Write code yourself, don’t just sit there and look at my code.
 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)
Who this course is for:
 If you want to level up with deep learning, take this course.
 If you are a student or professional who wants to apply deep learning to time series or sequence data, take this course.
 If you want to learn about word embeddings and language modeling, take this course.
 If you want to improve the performance you got with Hidden Markov Models, take this course.
 If you’re interested the techniques that led to new developments in machine translation, take this course.
 If you have no idea about deep learning, don’t take this course, take the prerequisites
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