Introduction to Deep Learning at TU München

Flashcards and summaries for Introduction to Deep Learning at the TU München

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Study with flashcards and summaries for the course Introduction to Deep Learning at the TU München

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

Describe Reinforcement Learning

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

How does the k-NN Algorithm work?

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

What does Cross Validation do?

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

How can we prevent our model from overfitting?

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

What is a problem with Gradient Descent?

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

What does the analogy "AI is the new electricity" refer to?

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

When an experienced deep learning engineer works on a new problem, they can usually use insight from previous problems to train a good model on the first try, without needing to iterate multiple times through different models.

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

A demographic dataset with statistics on different cities' population, GPD per capita, economic growth is an example of "unstructured" data because it contains data coming from different sources.

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

Why is a Recurrent Neural Network (RNN) used for machine translation?

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

What does a neuron compute?

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

Which of the following are true?

Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

During forward propagation, in the forward function for a layer l you need to know what is the activation function in a layer (Sigmoid, tanh, ReLU, etc.). During backpropagation, the corresponding backward function also needs to know what is the activation function for layer l, since the gradient depends on it.

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Exemplary flashcards for Introduction to Deep Learning at the TU München on StudySmarter:

Introduction to Deep Learning

Describe Reinforcement Learning

Agent learns by reward

Introduction to Deep Learning

How does the k-NN Algorithm work?

A classifier that looks at the distance of k neighbors:

1. Load the data

2. Initialise the value of k 

3. For gettin the predicted class, iterate from 1 tot toal number of training data points

3.1. Calculate the distance between test data and each row of training data (e.g. Euclidean Distance)

3.2. Add the distance and the index of the example to an ordered collection

4. Sort the calculated distances in ascending order based on distance values

5. Get top k rows from the sorted array

6. Get the most frequent class of theses rows

7. Return the predicted class

8. If regression: return the mean of the K; else: return the mode of the K Labels

Introduction to Deep Learning

What does Cross Validation do?

It tests the effectiveness of a machine learning model: Under-, Over-fitting or Well generalised

Introduction to Deep Learning

How can we prevent our model from overfitting?

Regularization

Introduction to Deep Learning

What is a problem with Gradient Descent?

We can only determine the local minimum and Neural Networks are non-convex -> there are many local minima

But this can be good enough to solve our problem.

Introduction to Deep Learning

What does the analogy "AI is the new electricity" refer to?

Similar to electricity starting about 100 years ago, AI is transforming multiple industries

Introduction to Deep Learning

When an experienced deep learning engineer works on a new problem, they can usually use insight from previous problems to train a good model on the first try, without needing to iterate multiple times through different models.

False

Introduction to Deep Learning

A demographic dataset with statistics on different cities' population, GPD per capita, economic growth is an example of "unstructured" data because it contains data coming from different sources.

False

Introduction to Deep Learning

Why is a Recurrent Neural Network (RNN) used for machine translation?

It can be traiend as a supervised learning problem

Introduction to Deep Learning

What does a neuron compute?

A neuron computes the mean of all features before applying the output to an activation function

Introduction to Deep Learning

Which of the following are true?

X is a matrix in which each column is one training example

Introduction to Deep Learning

During forward propagation, in the forward function for a layer l you need to know what is the activation function in a layer (Sigmoid, tanh, ReLU, etc.). During backpropagation, the corresponding backward function also needs to know what is the activation function for layer l, since the gradient depends on it.

True

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