AWS Certified Data Analytics Specialty 2021 – Hands-On - Deep Learning 101

The video tutorial provides an overview of deep learning, explaining its biological inspiration from the human brain and how artificial neurons mimic this process. It discusses the role of GPUs in parallelizing neural network computations and introduces popular frameworks like TensorFlow and MXNet. The tutorial also covers different types of neural networks, such as feed-forward, convolutional, and recurrent networks, and their applications. Finally, it explains how to deploy deep learning applications on AWS using services like Elastic MapReduce and EC2.

What is the primary inspiration for the field of artificial intelligence?

• The behavior of animals

• The structure of the human brain

• The design of modern computers

• The evolution of plants

How are neurons in the brain similar to processing units in a GPU?

• Both are responsible for storing data

• Both are used for input/output operations

• Both are used for long-term memory storage

• Both process information in parallel

What is the main function of an artificial neuron in a neural network?

• To manage network security

• To store data

• To handle user interface tasks

• To sum weighted inputs and apply an activation function

Which type of neural network is best suited for image classification tasks?

• Convolutional Neural Network

• Recurrent Neural Network

• Generative Adversarial Network

• Feedforward Neural Network

What is a key feature of recurrent neural networks (RNNs)?

• They process data in parallel

• They handle sequences in time

• They are primarily used for clustering

• They are used for image processing

Which AWS service supports Apache MXNet out-of-the-box for deep learning?

• Amazon S3

• Amazon RDS

• AWS Lambda

• Elastic MapReduce

What type of AWS instances is recommended for deep learning tasks?

• Instances with high CPU power

• Instances with large storage capacity

• Instances with high network bandwidth

• Instances with multiple GPUs