- "Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning."
Neural networks with multiple layers that can learn and make predictions on complex data.
Neural Networks: A mathematical model made up of interconnected nodes that can be used to approximate complex functions and make predictions.
Backpropagation: An algorithm used to calculate the error gradients in a neural network, allowing it to iteratively learn and improve its predictions.
Convolutional Neural Networks (CNNs): A specialized type of neural network that is particularly well-suited for image and video processing.
Recurrent Neural Networks (RNNs): A type of neural network that has connections between neurons forming a directed cycle, which allows it to process sequences of data such as text or speech.
Generative Adversarial Networks (GANs): A class of deep learning techniques that use two networks to generate new data from existing datasets.
Deep Reinforcement Learning: A technique for training an AI to learn through trial and error, by allowing it to interact with its environment and receive rewards or penalties.
Natural Language Processing (NLP): A field of AI that focuses on analyzing and processing human language.
Transfer Learning: The ability to use a pre-trained model to quickly adapt to a new task or data set.
Unsupervised Learning: A type of machine learning where the model is not given explicit labels or targets to train on, and must instead identify patterns or structure in the data on its own.
Supervised Learning: A type of machine learning where the model is provided with labeled training data that specifies the correct outputs for a given input, allowing it to learn to map inputs to outputs.
Hyperparameter Optimization: The process of tuning the parameters of a machine learning model to achieve the best possible performance on a given task.
Regularization: Techniques used to prevent overfitting by introducing additional constraints or penalties on the model's parameters.
Gradient Descent: An optimization algorithm that is commonly used to train neural networks, by iteratively adjusting the model's parameters in the direction of the steepest descent of the loss function.
Dropout: A regularization technique where a randomly chosen subset of the neurons in a neural network are ignored during training, reducing the risk of overfitting.
Activation Functions: A function applied to the outputs of a neural network's nodes to introduce nonlinearity and enable the model to learn complex patterns.
Learning Rate: A hyperparameter that controls the size of the updates that are made to the model's parameters during training.
Loss Functions: A mathematical function that measures how well the model is performing on a given task, which is used to guide the optimization algorithm during training.
Batch Normalization: A technique used to improve the stability and speed of neural network training, by normalizing the inputs to each layer in the network.
Data Augmentation: Techniques used to create new training examples from existing data, by applying transformations such as rotation, translation, or scaling.
Convolutional Neural Network Architectures: Popular CNN architectures such as AlexNet, VGG, and ResNet, which have proven to be effective in various computer vision tasks.
Convolutional Neural Networks (CNNs): Used for image and video recognition, CNNs are neural networks that automatically detect features from images or videos and classify them into different classes.
Recurrent Neural Networks (RNNs): Used for sequence data such as language processing, RNNs are neural networks that have a feedback loop which allows the output to be fed back as input to the next layer, making use of context information.
Generative Adversarial Networks (GANs): Used for generating new content such as images, GANs are neural networks that are made up of two separate networks, one generator and one discriminator, that work together to create new content.
Transfer Learning: Used to transfer learned knowledge from one domain to another, transfer learning is a technique where the weights of a pre-trained neural network are used to initialize a new network.
Autoencoders: Used for feature extraction and data compression, autoencoders are neural networks that use unsupervised learning to identify patterns in data and then use those patterns to compress the data.
Reinforcement Learning: Used for decision-based problems, reinforcement learning is a type of learning where an algorithm learns to make decisions based on feedback from a set of rewards and penalties.
Deep Belief Networks (DBNs): Used for recognizing patterns in high-dimensional data, DBNs are neural networks that are made up of multiple layers of restricted Boltzmann machines (RBMs) that learn to recognize patterns in data.
Long Short-Term Memory (LSTM) Networks: Used for sequence data such as speech recognition, LSTM networks are a type of RNN that have a mechanism for storing information over time, making them useful for tasks that involve prediction or classification of sequences.
Self-Organizing Maps (SOMs): Used for clustering and visualization of complex data, SOMs are neural networks that can be used to visualize and cluster high-dimensional data, making it easier to interpret and understand.
Deep Q-Networks (DQNs): Used for decision-making in complex environments, DQNs are a type of deep reinforcement learning where an agent learns to make decisions based on the environment it is in and the rewards it receives.
- "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."