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Introduction to Deep Learning
About this courseSkip About this course
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.
At a glance
- Institution: PurdueX
- Subject: Engineering
- Level: Advanced
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.
- Language: English
- Video Transcript: English
What you'll learnSkip 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.
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
Module 3: Optimization for Training Deep Models
- Optimization algorithms: Gradient, Hessian-Free, Newton
- 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)