Introduction to Deep Learning
This 3-credit-hour, 16-week course covers the fundamentals of deep learning. Students will gain a principled understanding of the motivation, justification, and design considerations of the deep neural network approach to machine learning and will complete hands-on projects using TensorFlow and Keras.
What you'll learn
- Justify the development state-of-the-art deep learning algorithms.
- Make design choices regarding the construction of deep learning algorithms.
- Implement, optimize and tune state-of-the-art deep neural network architectures.
- Identify and address the security aspects of state-of-the-art deep learning algorithms.
- Examine open research problems in deep learning and propose approaches in the literature to tackle them.
- Gradient-based learning
- Sigmoidal output units
- Back propagation
- Regularization strategies
- Noise injection
- Ensemble methods
- Optimization algorithms: Gradient, Hessian-Free, Newton
- Batch normalization
- Convolutional kernels
- Downsampled convolution
- Zero padding
- Backpropagating convolution
- Recurrence relationship & recurrent networks
- Long short-term memory (LSTM)
- Back propagation through time (BPTT)
- Gated and simple recurrent units
- Neural Turing machine (NTM)
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