Deep
learning is part of a broader family of machine learning
methods based on learning data representations, as opposed
to task-specific algorithms. This course describes deep
learning techniques used by practitioners in industry,
including deep feed forward networks, regularization,
optimization algorithms, convolutional networks, sequence
modeling, and practical methodology. This course is
useful to students planning careers in either industry
or research, and for software engineers who want to
begin using deep learning in their products or platforms |
1. Ian
Goodfellow, YoshuaBengo, Aaron Courville, Deep Learning,
1e, MIT Press, 2017
2. Nikhil Buduma and Nicholas Locascio, Fundamentals
of Deep Learning: Designing Next-Generation Machine
Intelligence Algorithms, 1e, Shroff/O'Reilly, 2017
3. Josh Patterson and Adam Gibson, Deep Learning: A
Practitioner's Approach, 1e, Shroff/O'Reilly, 2017 |