Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two. Machine Learning incorporates " classical" algorithms for various kinds of tasks such as clustering, regression or classification. Machine Learning algorithms must be trained on data. The more data you provide to your algorithm, the better it gets. The "training" part of a Machine Learning model means that this model tries to optimize along a certain dimension.

You can think of deep learning, machine learning and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest and working out. Deep learning is a subset of machine learning, which is a subset of AI. AI is any computer program that does something smart, broadly speaking. It can be a pile of if-then statements or a complex statistical model. Usually, when a computer program designed by AI researchers actually succeeds at something – like winning at chess – many people say it's "not really intelligent", because the algorithm's internals are well understood.

You can think of deep learning, machine learning and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest. Deep learning is a subset of machine learning, which is a subset of AI. AI is any computer program that does something smart. It can be a pile of if-then statements or a complex statistical model. Usually, when a computer program designed by AI researchers actually succeeds at something – like winning at chess – many people say it's "not really intelligent", because the algorithm's internals are well understood.

You can think of deep learning, machine learning and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest and working out. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth.

Assume you are given a task to fill a bag with 10 Kg of sand. You fill it up till the measuring machine gives you a perfect reading of 10 Kg or you take out the sand if the reading exceeds 10kg. Just like that weighing machine, if your predictions are off, your loss function will output a higher number. As you experiment with your algorithm to try and improve your model, your loss function will tell you if you're getting(or reaching) anywhere. "The function we want to minimize or maximize is called the objective function or criterion. When we are minimizing it, we may also call it the cost function, loss function, or error function" - Source At its core, a loss function is a measure of how good your prediction model does in terms of being able to predict the expected outcome(or value).