Page 121 - Artificial Intellegence_v2.0_Class_11
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Brainy Fact
In 2016, Google’s AlphaGo beat Go master Lee Sedol utilizing deep learning neural networks most closely
approaching human thought.
Let us now understand the difference between machine learning and deep learning:
Deep Learning Vs Machine Learning
Factors Deep Learning Machine Learning
Data Requirement Requires large data Can train on less data
Reliability Provides high accuracy Gives lesser accuracy
Training Time Takes longer to train Takes less time to train
Hardware dependence Requires GPU to train properly Trains on CPU
Hyperparameter Tuning Can be tuned in various different ways Limited tuning capabilities
Here are a few examples of deep learning at work:
• Automated Driving: Deep leaning is used to spot stoplights and traffic signals and also to detect pedestrians,
reducing the incidence of accidents.
• Aerospace and Defence: Identifying objects from satellites and locate safe and unsafe zones for troops is another
area where deep learning is playing a major role.
• Medical Research: Cancer researchers use deep leaning to automatically detect cancer cells.
• Industrial Automation: Deep learning helps detect when people or objects are within an unsafe distance from the
heavy machines thereby ensuring their safety.
Experiential Learning
GAME 02 Wolf, Sheep, and Cabbage
Visit the following website and play the wolf, sheep, and cabbage game:
https://www.proprofs.com/games/wolf-sheep-and-cabbage/
The aim of the game is to move the wolf, sheep, and cabbage to the opposite shore. However, points to keep
in mind, when the man is not around, are:
• the wolf will eat the sheep
• the sheep will eat the cabbage
Introduction to AI 119

