Artificial Intelligence is a buzzword which is on a voyage to replace most of the human work efforts in the subsequent upcoming years. It is a giant circle of a pool of various applications to make a machine intelligent.
Machine learning is a one of the subsets of AI. Representation learning is a subset of Machine learning. And Deep learning is the crux of Representation learning. Hence, going into deep learning before getting an overview of Machine learning can be non-fruitful. Although, it depends on your end goal for which you started the subject. Why do you even want to learn machine learning and its subclasses?
To answer your question, yes! You can start directly with deep learning as well, but it will need some basics of machine learning. Deep represents a dense network with number of layers. It is teaching machine to actually learn like humans, self-adapt and sustain. Deep learning does not require manual input of anything. It needs a large number of data or “big data.” Big data is a separate theory that you will need to understand about before you kick start with deep learning.
Deep learning is inspired by human nature and has feature detection applications. The base of deep learning is neural network present at different layers. Deep learning is a buzz because it’s uniqueness of using human brain and neural coding as a basis to make computer identify, classify and make use of the stimulus/stimuli given to it.
Professionals recommend the learning path of machine learning to be followed first. Basics of mathematics like algebra and probability, a bit of statistics, python and probably a basic course implementation of machine learning is suggested.
There is this book named Deep learning Book by Goodfellow, Bengio and Courville, which takes you to deep learning via the basics. This can be a presumed shortcut to directly jump to deep learning without learning much about machine learning. Bayesian Deep learning is another aspect crucial to deep learning protégé.
Although, as the protege is shifting towards the practical approach to study, the competition among Machine learning crash courses and machine learning classes will be at its peak. Practical knowledge par with your limits of imagination and capacity to think. The whole AI and ML and deep learning are things more of a buzz rather than people actually working on it. If you are really interested in the mechanisms, start reading theories and implementing algorithms now.
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