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Deep Learning

This class was created by Brainscape user Eduard Voronkin. Visit their profile to learn more about the creator.

By: Eduard Voronkin
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Decks in this class (13)

C2W2 Optimization algorithms
Batch 1,
Mini batch 2,
Exponentially weighted average 3
13  cards
C2W1 Practical Aspects of Deep Learning
Data should be divided into 1,
Bias variance 2,
How to reduce bias 3
9  cards
C2W3 Hyperparameters Tuning
Tuning order 1,
How to tune learning rate 2,
Pandas vs caviar approach to trai...
7  cards
C3W1 ML Strategy
Key ideas how to improve performa...,
Precision and recall 2,
F1 score 3
7  cards
C3W2
Train dev set 1,
What to do when overfitting to th...,
What is data mismatch 3
14  cards
C4W1
Types of convolutions 1,
Alternative to the hand coded con...,
Advantage of letting model to lea...
10  cards
C4W2
Some important convnets 1,
What is resnet 2,
What is residual block 3
12  cards
C4W3
Difference between classification...,
Explain yolo algorithm 2,
What is non max suppression 3
10  cards
C4W4
Face verification vs recognition 1,
What is one shot learning problem...,
Cost function for style transferr...
3  cards
C5W1 Recurrent Neural Networks
What are the tasks for rnn 1,
How rnn works 2,
Types of rnn 3
6  cards
C5W2 Word Embeddings
Name two ways of word representat...,
How to visualise n dimensional sp...,
What are the advantages of word e...
9  cards
C5W3 (Sequence Models And Attention Mechanism)
What is beam search 1,
Input parameters for beam algorit...,
Why do you need to normalise beam...
6  cards
C5W4(Transformer Network)
Bottleneck of plain rnn gru lstm 1,
What is self attention 2,
What is multihead attention 3
3  cards

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Deep Learning

  • Class purpose General learning

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