Machine Learning 101 - A Talk by Shiv Baishya - Mobile Software AG
An year after I started my bachelors in computer science and engineering, I was properly introduced to Artificial Intelligence (AI). AI is a branch in computer science that deals with the art of creating machines that are capable of solving increasingly sophesticated tasks. Traditional topics in AI include reasoning, knowledge, planning, natural language processing, creativity and learning.
Learning is the process of improving onself automatically either through experience or training. It is so vast in itself that it has led to the birth of an entire topic in AI known as machine learning (ML). Classical approach to learning can be broken down into three steps - collecting training raw data, representing or extracting 'features' from the raw data, and lastly constructing a process, usually known as the 'classifier', that is able to solve the given problem using the extracted 'features'.
Very often than not, it is rather difficult to construct the 'classifier'. And even more difficult is to define a proper procedure of extracting 'features' from the raw data for a particular problem. Due to these general difficulties, tremendous amount of time is spent in the design and engineering of a machine learning solution.
A special family of learning algorithms is artificial neural networks (ANN), or more commonly known as deep neural networks (DNN) or deep learning (DL). The very fact that deep networks can automatically extract 'features' and construct the classifier makes them unique. The theories on how and why deep learning works are yet to evolve onto the levels of their counterparts from classical machine learning. Nevertheless, I believe neural networks has the potential to serve as a powerful tool for solving sophisiticated machine learning problems.
References:
Alexander Jung's Mario AI, https://github.com/aleju/mario-ai
Nervana - Deep learning and the need for unified tools, https://www.nervanasys.com/deep-learn...
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Music: www.bensound.com