Course description
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.
Upcoming start dates
Who should attend?
Prerequisites:
This course is designed for students who have an undergraduate degree in electrical and computer engineering, computer science, or similar. Undergrauadate coursework in probabilistic methods in electrical and computer engineering and linear algebra is recommended before taking this course.
Training content
Module 1: Introduction to Deep Feedforward Networks
- Gradient-based learning
- Sigmoidal output units
- Back propagation
Module 2: Regularization for Deep Learning
- Regularization strategies
- Noise injection
- Ensemble methods
- Dropout
Module 3: Optimization for Training Deep Models
- Optimization algorithms: Gradient, Hessian-Free, Newton
- Momentum
- Batch normalization
Module 4: Convolutional Neural Networks
- Convolutional kernels
- Downsampled convolution
- Zero padding
- Backpropagating convolution
Module 5: Recurrent Neural Networks
- Recurrence relationship & recurrent networks
- Long short-term memory (LSTM)
- Back propagation through time (BPTT)
- Gated and simple recurrent units
- Neural Turing machine (NTM)
Course delivery details
This course is offered through Purdue University, a partner institute of EdX.
6-9 hours per week
Costs
- Verified Track -$2250
- Audit Track - Free
Certification / Credits
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.
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