Overview of Deep Learning

Branch of machine learning.

Deep Learning is a subfield of machine learning that is a game changer for the field of artificial intelligence, enabling the development of technologies and tools that were previously thought to be far-fetched or impossible.

Introduction to Deep Learning

Deep Learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. It is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers.

Difference between Machine Learning and Deep Learning

While both fall under the broad category of artificial intelligence, deep learning is what powers the most human-like artificial intelligence. Here are the key differences:

• Data Dependencies: Deep learning algorithms require more data than traditional machine learning algorithms. The performance of deep learning models improves with the amount of data, while the performance of traditional machine learning models plateaus after a certain data threshold.
• Hardware Dependencies: Deep learning models require more computational power, typically GPUs, to process the large amounts of data and complex operations.
• Feature Engineering: In traditional machine learning, most of the applied features need to be identified by an expert and then hand-coded as per the domain and data type. Deep learning algorithms, on the other hand, try to learn high-level features from data in an incremental manner.

Understanding Convolutional Neural Networks (CNN)

Convolutional Neural Networks (CNN) are a class of deep learning models that are primarily used to analyze visual data. CNNs are designed to automatically and adaptively learn spatial hierarchies of features from tasks with respect to the image data. They have been very successful in identifying faces, objects, and traffic signs apart from powering vision in robots and self-driving cars.

Understanding Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNN) are a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or the spoken word. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs, making them extremely effective for tasks that require memory of past inputs like language translation and speech recognition.

Applications of Deep Learning

Deep Learning has a wide array of applications including but not limited to:

• Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents.
• Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops.
• Medical Research: Cancer researchers are using deep learning to automatically detect cancer cells. Teams at UCLA built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify cancer cells.
• Public Sector: Deep learning is used in automated hearing and speech translation. For example, automatic speech recognition is used in law enforcement to transcribe and predict crime incidents.

Deep Learning is a rapidly evolving field, with new techniques and applications being developed and published constantly. It's an exciting time to delve into this field and see how it can be applied to various domains.