This 20-part course consists tutorials to learn deep learning and applied to natural language processing, also called Deep NLP. The course also includes hands-on assignments and projects for you to implement neural networks and solve NLP tasks. You can think of this course as a "Free Online Nano Book".
Why Deep NLP? Natural language processing comprises of a set of computational techniques to understand natural languages such as English, Spanish, Chinese, etc. Traditional NLP techniques proved successful on tasks like filtering spam email and sentiment classification, but don't perform as well on advanced tasks such as language translation, question answering, speech recognition and music composition (the fun stuff!). Deep NLP represents the state-of-the-art for these applications. It powers systems like the Google Assistant and Amazon Alexa.
The primary objectives of this course are as follows:
- Understand what machine learning is, and learn the gradient descent algorithm.
- Understand what deep learning is, and how deep learning differs from and relates to machine learning.
- Understand what neural networks are and how they are trained using back-propagation. (and train your own neural network).
- Understand the concept of computational graphs, a core idea (often overlooked in DL courses) foundational to understanding and implementing all sorts of complex neural network architectures.
- Understand how Recurrent Neural Nets work and how to generate text and perform language translation.
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