- "Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning."
A subset of machine learning that uses neural networks with several hidden layers to learn complex representations of data.
Artificial Intelligence: The study and development of computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Machine Learning: The process of training machines to recognize patterns in input data, using algorithms that learn from experience and automatically improve their performance over time.
Neural Networks: A type of machine learning algorithm that is inspired by the structure and function of the human brain, consisting of layers of interconnected nodes that process and transform input data.
Deep Learning: A type of neural network that is able to learn complex representations of data using multiple layers of processing.
Convolutional Neural Networks (CNNs): A type of deep learning algorithm that is designed to analyze and classify visual input data, such as images or videos.
Image Processing: The use of mathematical algorithms and techniques to analyze and interpret digital images, often used in computer vision applications.
Computer Vision: The field of study focused on enabling machines to perceive and understand visual information from the world around them.
Object Detection: The process of identifying and locating objects of interest within digital images or videos, often used in applications such as self-driving cars or surveillance systems.
Object Recognition: The ability of a machine to recognize and categorize visual objects based on their features, such as shape, color, and texture.
Image Segmentation: The process of dividing an image into multiple segments or regions based on its contents, often used in applications such as medical image analysis or satellite imaging.
Optimization Algorithms: The mathematical methods used to train neural networks and optimize their performance, such as stochastic gradient descent or Adam.
Transfer Learning: The process of using pre-trained models or pre-trained layers of a neural network to accelerate the training process or improve performance on specific tasks.
Data Augmentation: The process of generating new training data by modifying or transforming existing data, often used to increase the size of training sets or improve the robustness of models.
Regularization Techniques: Methods used to prevent overfitting, such as weight decay or dropout.
Natural Language Processing: The field of study focused on enabling machines to understand and generate human language, often used in applications such as chatbots or automated translation systems.
Generative Adversarial Networks (GANs): A type of neural network that is able to generate new data samples that are similar to existing data, often used in applications such as image synthesis or text generation.
Recurrent Neural Networks (RNNs): A type of neural network that is able to process sequential data and model dependencies between inputs, often used in applications such as speech recognition or language translation.
Reinforcement Learning: A type of machine learning where an agent learns to take actions in an environment to maximize a reward signal, often used in applications such as game playing or robotics.
Convolutional Neural Networks (CNN): A type of deep neural network designed specifically for image recognition tasks.
Recurrent Neural Networks (RNN): A type of deep neural network designed for handling sequence data, such as speech, language, and time series data.
Autoencoders: A type of neural network that is trained to reconstruct input data, and is useful for tasks such as denoising and compression.
Generative Adversarial Networks (GANs): A type of deep neural network architecture that consists of two models – a generator that creates new examples of data, and a discriminator that tries to distinguish them from real data.
Variational Autoencoders (VAEs): A combination of autoencoders and Bayesian inference that is used for generating new data and finding hidden features in a dataset.
Siamese Networks: A type of neural network that is designed to compare two different inputs and determine whether they are similar or not.
Long short-term memory (LSTM) networks: A type of RNN that is capable of learning long-term dependencies in data, and is used for tasks such as speech recognition and text translation.
Capsule Networks: A recently proposed deep learning architecture that is designed to better capture hierarchical relationships between different objects in an image.
Deep Belief Networks: A type of hierarchical generative model that is used for unsupervised learning, such as dimensionality reduction and clustering.
Residual Networks (ResNets): A type of CNN architecture that can handle deeper networks by solving the problem of gradient vanishing.
- "The adjective 'deep' in deep learning refers to the use of multiple layers in the network."
- "Methods used can be either supervised, semi-supervised or unsupervised."
- "Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks, and transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection, and board game programs."
- "They have produced results comparable to and in some cases surpassing human expert performance."
- "Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems."
- "ANNs have various differences from biological brains." - "Specifically, artificial neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog."