The fundamental concept behind deep learning is to emulate the human brain's neural networks in order to perform tasks that require pattern recognition, feature extraction, and decision making. Deep learning is a subset of machine learning, which itself is a branch of artificial intelligence (AI). Deep learning uses artificial neural networks with many layers (hence the term "deep"), and each layer consists of interconnected nodes called neurons that process and transform input data.

eep learning (also called deep learning AI) is a form of machine learning that builds neural-like networks, similar to those found in a human brain. The neural networks make connections between data, a process that simulates how humans learn.

Neural nets include three or more layers of data to improve their learning and predictions. While AI can learn and make predictions from a single layer of data, additional layers provide more context to the data. This optimizes the process of making more complex and detailed connections, which can lead to greater accuracy.

Abu Rayhan The fundamental difference between classical techniques and deep learning is the way features are learned. The classical models do not build or extract any features on their own. Deep learning works hierarchically to build various abstract representations of features.

Traditional(classic) AI consists of HMMs (Hidden Markov Models), Heuristics (which is close to Approximate algorithms in algorithmic fields), Bayesian Theorem, and Bayesian Networks.

Reinforcement learning is also a branch of traditional AI - I suppose.

But in deep learning, Neural Networks have arised. They try to mimic the functionality of human brains.

The main concept behind them is Backpropagation.

The fundamental concept behind deep learning is the use of artificial neural networks to enable machines to learn and make decisions in a manner inspired by the human brain. Deep learning models consist of multiple layers of interconnected nodes, called neurons, organized into input, hidden, and output layers. Each neuron performs a weighted sum of its inputs and passes the result through an activation function to produce an output.

The key idea is that these neural networks can automatically learn and extract hierarchical features from data, allowing them to model complex patterns and relationships in the input data. This hierarchical feature extraction is what makes deep learning particularly powerful, as it can discover intricate and abstract representations of data that would be challenging to engineer manually.

Deep learning algorithms learn by adjusting the weights and biases of the neurons during a training process. They minimize the difference between their predictions and the actual target values, typically using optimization techniques like gradient descent. This iterative learning process allows deep learning models to improve their performance over time through exposure to a large amount of labeled data.

One of the most significant breakthroughs in deep learning was the development of convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) for sequential data. These specialized architectures have revolutionized fields like computer vision, natural language processing, and speech recognition, enabling machines to achieve human-level performance in various tasks. The scalability, adaptability, and ability to capture intricate patterns make deep learning a fundamental concept in modern artificial intelligence and machine learning.