Deep Learning 41 Flashcards Advanced 100% Free

Deep Learning::Feed Forward Neural Networks

Created by Chat Robotics Community  ·  Updated 2025-07-19

Curriculum Overview

Topics & Key Concepts

Deep Learning Ffnn Optimization

Sample Flashcard Questions & Answers

Showing 8 of 41 cards
Question #1 Active Recall

What is a Feed Forward Neural Network (FFNN)?

Answer & Explanation:
A Feed Forward Neural Network is a type of artificial neural network where connections between the nodes do not form a cycle, meaning the information moves in one direction—from input nodes, through hidden nodes (if any), and to output nodes.
Question #2 Active Recall

What are the key components of an FFNN?

Answer & Explanation:
The key components of a Feed Forward Neural Network include input layers, hidden layers, output layers, neurons, weights, and activation functions.
Question #3 Active Recall

What is the role of activation functions in FFNNs?

Answer & Explanation:
Activation functions introduce non-linearity to the model, allowing the network to learn complex patterns. Common activation functions include Sigmoid, Tanh, and ReLU (Rectified Linear Unit).
Question #4 Active Recall

What is the activation function equation for Sigmoid?

Answer & Explanation:
The Sigmoid activation function is defined as: \[ f(x) = \frac{1}{1 + e^{-x}} \]
Question #5 Active Recall

What is the activation function equation for Tanh?

Answer & Explanation:
The Tanh activation function is defined as: \[ f(x) = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}} \]
Question #6 Active Recall

What is the activation function equation for ReLU?

Answer & Explanation:
The ReLU activation function is defined as: \[ f(x) = \max(0, x) \]
Question #7 Active Recall

How do weights function in an FFNN?

Answer & Explanation:
Weights are parameters that are adjusted during training to minimize the error in the output. Each connection between neurons has an associated weight.
Question #8 Active Recall

What is the purpose of the bias term in FFNNs?

Answer & Explanation:
The bias term allows the activation function to be shifted to the left or right, which can be critical for the model's performance.

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