What is Deep Learning?
With a wide range of uses extending from face recognition systems to the autopilot feature in vehicles, from image enhancement to cyber threat analysis, and from alarm systems to cancer research, deep learning applications are present in almost every area of modern life. By continuing to read, you can find the answer to the question what is deep learning, and learn in detail the working mechanism of deep learning, its differences from machine learning, and its most common forms of application.
Deep Learning is a sub-branch of machine learning that enables artificial neural networks, inspired by the working principle of neurons in the human brain, to learn by processing large data sets with their multi-layered and complex structures. This architecture, which contains numerous hidden layers, is also referred to as "deep structured learning" or hierarchical learning in the literature. Starting its development in the 1940s and rooted in neural networks, this model further developed and grew over the following decades. Especially after the 2000s, with advancements in GPU (Graphics Processing Unit) technology and the onset of the Big Data era, deep learning models became trainable at a speed not previously possible. Thanks to this technological leap, systems matching or exceeding human performance have been developed in areas such as image recognition, natural language processing, and strategy games.
Deep Learning Models
- Artificial Neural Networks (ANN): Artificial neural networks are the structures forming the basis of deep learning. Developed inspired by neurons in the human brain, these models consist of input layers, hidden layers, and output layers. While data progresses between layers, weights and connections are constantly updated. In this way, the model can learn complex relationships and patterns. These networks are generally used in classification and regression problems.
- Convolutional Neural Networks (CNN): Convolutional neural networks provide high success especially on image and video data. These models automatically learn local features such as edges, shapes, and patterns in images. Thanks to filters and pooling layers, both dimensionality reduction is performed and important visual features are preserved. Convolutional neural networks are widely used in areas such as face recognition, object detection, and medical image analysis.
- Recurrent Neural Networks (RNN): Recurrent neural networks are designed to work with sequential and time-dependent data. These models can influence subsequent predictions by keeping information from previous steps in memory. They offer the advantage of preserving context in time-dependent data. These are frequently preferred in speech recognition, text analysis, and time series forecasting.
- Long Short-Term Memory (LSTM): LSTM networks are designed to solve the "vanishing gradient" problem seen in standard RNNs (Recurrent Neural Networks) and to learn long-term dependencies. This structure, which can store important information and eliminate unnecessary ones, provides more balanced learning in long sequences. It produces effective results in natural language processing and speech technologies.
- Generative Adversarial Networks (GAN): GAN models consist of two separate neural networks (Generator and Discriminator) competing against each other. While the generator network tries to produce new and realistic data, the discriminator network tries to understand whether this data is real or artificial. Thanks to this competitive structure, high-quality visuals, synthetic data, and creative content can be produced.
Deep Learning Techniques
- Activation Functions: Activation functions determine how the neural network will transmit the information it has learned. Thanks to these functions, the model can learn not only linear relationships but also more complex structures. Therefore, activation functions greatly assist in deciding which information is important.
- Backpropagation: Backpropagation is the fundamental technique that enables the model to learn from its errors. The model calculates the difference between the prediction and the actual result and updates the connection weights by propagating this error backwards. As this process is repeated, the model produces more accurate results.
- Regularization: These are techniques used to prevent the model from overlearning the training data (Overfitting) and to increase its generalization ability (e.g., Dropout, L1/L2). The aim is to ensure the model is successful not only on training data but also on new and unseen data. In this way, more balanced and reliable results are obtained.
- Stochastic Gradient Descent (SGD): It is the fundamental optimization algorithm that ensures weights are iteratively updated to minimize the model's error rate (loss function). Data is processed in small batches, and the model is slightly improved at each step. This method accelerates the learning process and offers a more flexible structure.
- Adam Optimization: Adam optimization is a technique that makes the learning process more balanced and efficient. It enables the model to make smarter updates by taking into account the learning rate from previous steps. In this way, stable results can be obtained across different data types.
