Introduction to Deep Learning (edX)

Introduction to Deep Learning (edX)
Course Auditing
Categories
Effort
Certification
Languages
Knowledge of probabilistic methods in electrical and computer engineering (ECE 302 at Purdue) and undergraduate linear algebra.
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Introduction to Deep Learning (edX)
Learn how deep learning algorithms can be used to solve important engineering problems. 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.

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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.


Syllabus


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)



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MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Course Auditing
1920.00 EUR
Knowledge of probabilistic methods in electrical and computer engineering (ECE 302 at Purdue) and undergraduate linear algebra.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.