Deep learning is one of the branches of machine learning. We can divide machine learning into two groups: classical machine learning models (e.g. KNN, LinearRegression, RandomForest) and neural networks (e.g. fully connected NN, recurrent NN). Neural network is the collective name of different kinds of architectures. Without loss of generality we can define NN as the several layers of neurons which are connected by weights. The number of layers define the name “deep neural network”. Usually we call a neural network the deep NN starting with 3 layers and more. Therefore, deep learning it’s the branch of machine learning which operates with deep neural networks.
Deep learning is a subfield of machine learning that uses neural networks with multiple layers to learn representations of data. The key difference between deep learning and traditional machine learning approaches is that deep learning algorithms can learn to recognize patterns in data without being explicitly programmed to do so.
In traditional machine learning, the feature extraction process is often done manually by domain experts who identify relevant features in the data. These features are then fed into a machine learning algorithm to train a model. This process can be time-consuming and may require significant domain expertise.
In contrast, deep learning models automatically learn features from the data. Deep learning algorithms use multiple layers of artificial neural networks to extract increasingly complex and abstract representations of the data. These representations are learned through a process called backpropagation, where the error signal from the output of the model is propagated backwards through the network to adjust the weights of the connections between neurons.
Deep learning has been successful in a wide range of applications, including computer vision, natural language processing, and speech recognition. Because deep learning models can learn to recognize patterns in data automatically, they have the potential to perform better than traditional machine learning models in complex tasks with large amounts of data. However, deep learning models can also be computationally intensive and require large amounts of data to train effectively.
Dear Wisam Mohammed Abed Alqaraghuli ,
Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own.
https://www.zendesk.com/blog/machine-learning-and-deep-learning/#:~:text=Machine%20learning%20uses%20algorithms%20to,intelligent%20decisions%20on%20its%20own.
Regards,
Shafagat
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