Contenu du cours

Introduction to Neural Networks

1. Concept of Neural Network

What is a Neural Network?Neural Networks or Traditional Models Applications of Deep Learning in the Real World Neural Network Structure What is a Neuron?Activation Functions Forward and Backward Propagation

2. Neural Network from Scratch

Single Neuron Implementation Challenge: Creating a Neuron Perceptron Layers Challenge: Creating a Perceptron Forward Propagation Loss Function Backward Propagation Backpropagation Implementation Model Training Challenge: Training the Perceptron Model Evaluation Challenge: Evaluating the Perceptron Neural Network with scikit-learn

3. Conclusion

Other types of Neural Networks Hyperparameter Tuning Challenge: Automatic Hyperparameter Tuning Quiz: Neural Networks Essentials Summary

Quiz: Neural Networks Essentials

1. A neuron in a neural network typically:

2. A perceptron is:

3. The primary role of Backward Propagation is:

4. Scaling training data:

5. The process of training a neural network primarily involves:

6. TensorFlow and PyTorch are:

7. When would you choose traditional machine learning models over neural networks?

8. Convolutional Neural Networks (CNNs) are primarily used for:

9. Recurrent Neural Networks (RNNs) are best suited for:

10. Which of the following is NOT an advantage of using neural networks?

11. What role does an activation function play in a neuron?

12. Which phase involves calculating the loss and then adjusting the weights backwards through the layers?

13. In the context of neural networks, epochs refer to:

14. An overfitted neural network:

A neuron in a neural network typically:

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Transmits electrical signals.

Receives inputs and produces an output based on them.

Can learn without any input.

Only exists in biological systems.

A perceptron is:

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A neural network with a single layer.

Another name for neuron.

A type of activation function.

A cloud storage solution.

The primary role of Backward Propagation is:

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Predicting the next sequence in data.

Adjusting weights in the neural network based on errors.

Filtering out noise from the input.

Storing the results of forward propagation.

Scaling training data:

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Makes the data unreadable.

Can help improve the speed and accuracy of training neural networks.

Is only necessary for traditional machine learning models.

Introduces more noise into the data.

The process of training a neural network primarily involves:

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Randomly setting all weights and biases.

Avoiding the use of activation functions.

Iteratively adjusting weights to minimize error.

Only using forward propagation.

TensorFlow and PyTorch are:

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Libraries/frameworks for deep learning.

Types of activation functions.

Methods of backward propagation.

Specific types of neural networks.

When would you choose traditional machine learning models over neural networks?

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When dealing with image recognition.

When you have small datasets and simpler patterns.

In tasks requiring sequence predictions only.

Convolutional Neural Networks (CNNs) are primarily used for:

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Text data without sequence.

Tabular data.

One-dimensional data only.

Image data.

Recurrent Neural Networks (RNNs) are best suited for:

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Image classification.

Tabular data analysis.

Sequential data like time series or text.

Static data without any order.

Which of the following is NOT an advantage of using neural networks?

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Can recognize complex patterns.

Can handle large datasets.

Always provides interpretable results.

What role does an activation function play in a neuron?

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Helps in storing data.

Assists in backward propagation.

Introduces flexibility to the model.

Reduces the number of parameters.

Which phase involves calculating the loss and then adjusting the weights backwards through the layers?

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Initialization

Regularization

Backward Propagation

Forward Propagation

In the context of neural networks, epochs refer to:

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The maximum depth of a network.

The number of neurons.

The number of times the entire dataset is passed through the network.

The learning rate of a network.

An overfitted neural network:

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Performs very well on the training data but poorly on new, unseen data.

Performs poorly on both training and test data.

Performs well on unseen data but poorly on the training data.

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Section 3. Chapitre 4