Deep learning

A branch of artificial intelligence (AI), which attempts to simulate the behavior of the human brain allowing it to “learn” from large amounts of data

Deep learning is a subset of machine learning, which is itself a subset of artificial intelligence (AI). It mimics the functioning of the human brain through artificial neural networks, enabling computers to learn and make decisions without explicit programming for every possible scenario.

### Key Components of Deep Learning: 1. Neural Networks: The backbone of deep learning, modeled after the human brain. These consist of layers of nodes (neurons) connected by edges, each layer processing information and passing it to the next.

1. Input Layer: Receives the raw data.
2. Hidden Layers: Perform feature extraction and transformation using weights and biases.
3. Output Layer: Produces the final result or prediction.

2. Training: Neural networks learn by training on large datasets using algorithms such as backpropagation and gradient descent, adjusting weights to minimize the error between predicted and actual outcomes.

3. Activation Functions: Functions applied to each neuron's output to introduce non-linearity (e.g., ReLU, sigmoid, tanh), allowing neural networks to model complex patterns.

4. Types of Deep Learning Models:

1. Convolutional Neural Networks (CNNs): Used for image and video recognition.
2. Recurrent Neural Networks (RNNs): Used for sequence-based tasks like time series prediction and natural language processing.
3. Generative Adversarial Networks (GANs): Used for generating new data samples similar to the training data (e.g., image synthesis).

### Applications: - Image and Speech Recognition: Used in self-driving cars, facial recognition, voice assistants (e.g., Siri, Google Assistant). - Natural Language Processing: For tasks like machine translation, chatbots, sentiment analysis. - Healthcare: Diagnosis of medical conditions (e.g., tumor detection in radiology). - Finance: Fraud detection, risk modeling.

Deep learning has revolutionized fields requiring large datasets and complex decision-making processes, outperforming traditional machine learning methods in areas such as vision and language processing. However, it requires significant computational resources and extensive data for effective training.

Deep learning (DL) is an advanced machine learning approach that involves the construction of artificial neural networks with structures and functions similar to those of the human brain using a large number of hidden layers. The DL technique outperforms traditional ML techniques and learns from unstructured and perceptual image data. A convolutional neural network (CNN) is a representative DL model that is highly advantageous for imaging recognition and classification.

Deep learning algorithm.

Although deep-learning approaches have shown promising results, one of the main limitations is a lack of large annotated training datasets, combined with the high dimensionality of the medical image data. Recent methods proposed to combine deep learning with pre-processed features ^{1) 2)}

With the advent of digital technology, machine learning and deep learning, in particular, is increasingly making it possible to utilize big data to more precisely risk stratify and prognosticate how an individual patient will behave based on a given disease or intervention. Machine learning has already been used in other realms such as retail and search engines. However, healthcare has lagged in the uptake of newer techniques to leverage the rich information contained in electronic health records.

Learning can be supervised, semi-supervised or unsupervised.

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A study demonstrates that generating personalized and robust deep learning-based analytics for outcome prediction is feasible even with limited amounts of center-specific data. With prospective validation, the ability to preoperatively and reliably inform patients about the likelihood of symptom improvement could prove useful in patient counseling and shared decision-making ³⁾.