Definition: A subfield of machine learning using artificial neural networks to model and solve complex problems.
Better definition: When your computer daydreams about neural networks and becomes super smart.
Where does this fit in the AI Landscape?
Deep learning has been a game-changer for AI, allowing machines to excel in areas like computer vision, speech recognition, and natural language understanding. It has enabled technologies like self-driving cars, facial recognition systems, and advanced translation tools, dramatically transforming the AI landscape.
What are the real world impacts of this?
Deep Learning, a further subset of ML, powers cutting-edge technologies like autonomous vehicles and real-time speech translation. These technologies can make our lives safer and more convenient. For developers, understanding deep learning opens the door to these advanced applications, enabling them to work on the forefront of technological innovation.
What could go wrong in the real world with this?
A deep learning model is trained to write poems, but instead of producing beautiful verses, it becomes obsessed with writing limericks about cats in space.
A subtype of ML, used in the model training phase for learning complex patterns. Deep Learning models can be trained to predict next possible code snippets or understand and generate human language.