Deep Learning: Recurrent Neural Networks in Python

GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences.

What Will I Learn?

• Understand the simple recurrent unit (Elman unit)
• Understand the GRU (gated recurrent unit)
• Understand the LSTM (long short-term 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

Who is the target audience?

• 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.

Includes:

• 6.5 hours on-demand video
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

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