Module_0 Flash Cards
What is deep learning?
A subfield of machine learning focusing on inductive learning using large datasets, capable of applications in computer vision, NLP, audio processing, and more.
What are some applications of deep learning?
Image classification, image captioning, answering natural language questions, decision-making tasks like AlphaGo, and more.
When did deep learning see a resurgence, and why?
Around 2012, due to success in competitions involving large datasets like ImageNet, and the availability of GPUs and large labeled datasets.
What are the key enablers of deep learning success?
Large labeled datasets, specialized hardware (GPUs), open research platforms (e.g., arXiv), and open-source code availability.
What distinguishes deep learning from traditional machine learning?
Deep learning uses hierarchical, compositional, and distributed representations, and operates end-to-end without feature engineering.
What are computation graphs in deep learning?
Sequences of computations optimized over time to transform inputs into outputs, similar to programming constructs like loops or if-statements.
What are the foundational concepts of deep learning introduced in the course?
Linear classifiers, gradient descent, neural networks, convolutional neural networks (CNNs), and optimization techniques.
What is the role of GPUs in deep learning?
GPUs enable fast processing of large datasets and complex models during training and inference, making deep learning feasible.
What are convolutional neural networks (CNNs)?
Specialized neural networks for processing grid-like data such as images, with layers designed to capture spatial hierarchies.
What advanced topics in deep learning are covered in the course?
Structured neural representations, NLP tasks, deep reinforcement learning, unsupervised and semi-supervised learning, and generative modeling.
How does deep learning perform end-to-end learning?
It directly optimizes input-to-output transformations without hand-engineered features, learning representations automatically.
What is the significance of labeled data in deep learning?
Labeled data provides the answers needed for supervised learning, enabling deep models to learn from examples effectively.
What are the major areas where deep learning is applied today?
Computer vision, natural language processing (NLP), audio processing, and decision-making systems like AlphaGo.
