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Data Science & ML track

RNN

Recurrent architectures, vanishing gradients and sequence modeling.

6 concepts3 labs1 challenges
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Concepts

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Work through the concepts in order, or jump to what you need.

1

Sequence Modeling

Concept · Hands-on lab included

2

RNN Architecture

Concept · Hands-on lab included

3

Vanishing & Exploding Gradients

Concept · Hands-on lab included

4

Sequence-to-Sequence Patterns

Concept · Hands-on lab included

5

Training RNNs

Concept · Hands-on lab included

6

RNN Applications

Concept · Hands-on lab included

Practice

Hands-on labs

Apply what you've learned in guided, production-style environments.

Browse all labs

A From-Scratch RNN Stepwise

25 min · intermediate · RNN Architecture

Build a Tiny Seq2Seq in Numpy

30 min · advanced · Sequence-to-Sequence Patterns

Ship a Tiny Sequence Classifier

30 min · advanced · RNN Applications

Continue

Next modules

Keep your learning streak going with related modules.

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Mathematics

Linear algebra, calculus and notation needed for machine learning.

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Statistics

Distributions, estimators, hypothesis testing and statistical inference.

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Python

Scientific Python stack: environments, typing, numpy-style thinking.

DevOpsForge - Data Science & ML · RNN
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