Deep Learning Concepts and Applications

The video explains deep learning, a subset of machine learning and AI, highlighting its ability to process unstructured data through neural networks. It contrasts deep learning with traditional machine learning, emphasizing the independence of feature selection in deep learning. The video details the workings of neural networks, including input, hidden, and output layers, and the role of weights, biases, and activation functions. Applications in customer support, medical care, and self-driving cars are discussed, along with limitations like data requirements, computational power, and training time. The video concludes with future prospects and advancements in deep learning technology.

What is deep learning a subset of?

• Machine Learning

• Computer Vision

• Data Science

• Artificial Intelligence

How does deep learning differ from machine learning in feature selection?

• Features are automatically selected by the neural network in deep learning

• Features are selected based on user input in deep learning

• Deep learning does not use features

• Features are manually selected in deep learning

What is the role of neurons in a neural network?

• They delete data

• They transmit data

• They process information

• They store data

What is the purpose of the activation function in a neural network?

• To determine if a neuron should be activated

• To calculate the weighted sum

• To add bias to the inputs

• To deactivate neurons

In which field is deep learning used to detect cancer cells?

• Agriculture

• Education

• Medical Care

• Finance

Which companies are working on self-driving cars using deep learning?

• Google, Amazon, Facebook

• Apple, Tesla, Nissan

• Microsoft, IBM, Intel

• Samsung, Sony, LG

What is a major limitation of deep learning related to data?

• It requires only structured data

• It does not require any data

• It requires a massive volume of data

• It requires a small amount of data

Why is computational power a limitation for deep learning?

• It requires no computational power

• It requires GPUs with thousands of cores

• It requires CPUs with few cores

• It requires only cloud computing

How does the training time of deep neural networks change with data?

• It is not affected by data

• It increases with more data

• It remains constant

• It decreases with more data

Which of the following is a popular deep learning framework?

• TensorFlow

• Vue.js

• React

• Angular