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:

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.

Select the correct answers:

  1. False

  2. True

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:

Which of the following are true?

Select the correct answers:

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

  2. a₄[²] is the activation output by the 4th neuron of the 2nd layer

  3. a[²]⁽¹²⁾ denotes activation vector of the 12th layer on the 2nd training example

  4. a[²] denotes the activation vextor of the 2nd layer

  5. a₄[²] is the activation output of the 2nd layer for the 4th training example

  6. a[²]⁽¹²⁾ denotes the activation vector of the 2nd layer for the 12th training example

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.

Select the correct answers:

  1. False

  2. True

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

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

Select the correct answers:

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

  2. AI is powering personal devices in our homes and offices, similar to elextricity

  3. AI runs on computers and is thus powered by electricity, but it is letting computers do things not possible before

  4. Through the “smart grid”, AI is delivering a new wave of electricity

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:

List advanced regularization approaches

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

What happens if we initialize all weights with small random numbers?

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

What does a neuron compute?

Select the correct answers:

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

  2. A neuron computes an activation function followed by a linear function (z = Wx +b)

  3. A neuron computes a linear function (z = Wx + b) followed by an activation function

  4. A neuron computes a function g that scales the input x linearly (Wx +b)

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

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

Select the correct answers:

  1. It can be traiend as a supervised learning problem

  2. It is strictly more powerful than a Convolutional Neural Network (CNN)

  3. It is applicable when the input/output is a sequence (e.g., a sequence of words)

  4. RNNs represent the recurrent process of Idea->Code->Experiment->Idea->…

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?

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

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.

  1. False

  2. True

Introduction to Deep Learning

How can we prevent our model from overfitting?

Regularization

Introduction to Deep Learning

Which of the following are true?

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

  2. a₄[²] is the activation output by the 4th neuron of the 2nd layer

  3. a[²]⁽¹²⁾ denotes activation vector of the 12th layer on the 2nd training example

  4. a[²] denotes the activation vextor of the 2nd layer

  5. a₄[²] is the activation output of the 2nd layer for the 4th training example

  6. a[²]⁽¹²⁾ denotes the activation vector of the 2nd layer for the 12th training example

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.

  1. False

  2. True

Introduction to Deep Learning

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

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

  2. AI is powering personal devices in our homes and offices, similar to elextricity

  3. AI runs on computers and is thus powered by electricity, but it is letting computers do things not possible before

  4. Through the “smart grid”, AI is delivering a new wave of electricity

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

List advanced regularization approaches

  • Weight Decay
  • Early Stopping
  • Bagging and Ensemble Methods
  • Dropout
  • Batch Normalization

Introduction to Deep Learning

What happens if we initialize all weights with small random numbers?

Gradients vanish

Introduction to Deep Learning

What does a neuron compute?

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

  2. A neuron computes an activation function followed by a linear function (z = Wx +b)

  3. A neuron computes a linear function (z = Wx + b) followed by an activation function

  4. A neuron computes a function g that scales the input x linearly (Wx +b)

Introduction to Deep Learning

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

  1. It can be traiend as a supervised learning problem

  2. It is strictly more powerful than a Convolutional Neural Network (CNN)

  3. It is applicable when the input/output is a sequence (e.g., a sequence of words)

  4. RNNs represent the recurrent process of Idea->Code->Experiment->Idea->…

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

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