Machine learning (ML) and deep learning (DL) – both are process of creating an AI-based model using the certain amount of training data but they are different from each other.
ML is used with an algorithm that can be supervised or unsupervised to implement the training data and build a model that can work automatically when used in real-life. While on the other hand deep learning is part for ML and having the more capability to understand the data or yo can say comprehend the data deeply to create artificial neural network.
How ML is Different from Deep Learning?
While working with deep learning you need high capacity machines to perform large amount of matrix multiplication operations. Compare to machine learning, deep learning requirements includes GPUs, while ML can be performed at low-end machines.
Compare to ML, deep learning take more time to train the models for maximum accuracy. And deep learning has more complex problem solving capability compare to machine learning. ML is basically used for object detection and object recognition.
While on the interpretation point of view, most of the machine learning algorithms are easy to interpret while deep learning algorithms are difficult or impossible to understand. The execution time in ML is much smaller (From few minutes to hours) while deep learning can take up to weeks to train and develop the AI model for complex predictions.
While working with deep learning you need huge amount of data or you can say big data while machine learning can be trained with smaller amount of data compare to deep learning. DL algorithms will not work well or can not give the accurate results with smaller amount of datasets it requires the huge quantity of supervised data sets for model to work properly.
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