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WorksheetsFinTech 14-1 Deep Learning
Total questions: 10
Worksheet time: 12mins
Which were objectives of Deep Learning day 1?
Identify the Python libraries available to build neural networks.
Identify all the different types of neural networks.
Implement neural networks with the TensorFlow Keras API.
Explain the intuition of how weights of neurons are determined.
Which are applications of neural nets?
Image recognition
Self-driving cars
Automated translation
Fraud detection
Which is true of the perceptron?
It is based on a brain neuron.
It was the first neural network model.
It was used for image recognition.
Every input data signal is weighted according to the relevance of each one.
Which is true of an activation function?
The output is a probability.
A common one is called a sigmoid function which produces a sigmoid or s-curve.
It is executed at the beginning of a neural net.
It introduces a non-linear property to the network.
Which visualizations did we review?
Tensorflow Playground
Magic 8-ball
Minecraft Neural Nets
Teachable Machine
Which are components of neural nets?
Inputs
Dendrites
Hidden layers
Activation function
What can we do to tune a neural net?
Add more neurons.
Add more layers.
Use principle component analysis.
Test different activation functions.
Using more epochs.
Which deep learning libraries did we use?
Tensorflow
PyTorch
Keras
Spark
How was Deep Learning day 1 for you?
Any suggestions for improvement?
