shivanis09
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Machine learning and deep learning are both subfields of artificial intelligence (AI) that involve training algorithms to learn patterns and make predictions from data. However, there are several key differences between machine learning and deep learning:
- Representation of Data:
- Machine Learning: In traditional machine learning, algorithms typically rely on handcrafted feature engineering, where domain experts extract relevant features from raw data to represent it in a structured format. These features are then used as input to the machine learning model.
- Deep Learning: In deep learning, algorithms automatically learn hierarchical representations of data directly from raw inputs, such as images, text, or audio. Deep neural networks consist of multiple layers of interconnected neurons that progressively extract higher-level features from the input data.
- Model Complexity:
- Machine Learning: Machine learning models often consist of simpler algorithms, such as linear regression, decision trees, or support vector machines, which can be trained using relatively small datasets and computational resources.
- Deep Learning: Deep learning models, particularly deep neural networks, are highly complex and contain many layers (hence the term "deep"). These models require large amounts of data and computational power for training, as well as specialized hardware such as graphics processing units (GPUs) or tensor processing units (TPUs).
- Training Process:
- Machine Learning: In machine learning, training algorithms typically involve optimizing a predefined objective function (e.g., minimizing mean squared error for regression, maximizing likelihood for classification) using techniques such as gradient descent or its variants.
- Deep Learning: Deep learning models are trained using techniques such as backpropagation, where errors are propagated backwards through the network to update the weights of the connections between neurons. This process requires iterative forward and backward passes through the network and can involve millions or even billions of parameters.