Course Summary
Credit Type:
5 months (8 hours per week)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 2 Neural Networks and Deep Learning
Lower-Division Baccalaureate 2 Improving Deep Neural Networks
Lower-Division Baccalaureate 1 Structuring Machine Learning Projects
Lower-Division Baccalaureate 2 Convolutional Neural Networks
Lower-Division Baccalaureate 3 Sequence Models


This course is offered through Coursera, which is an ACE Authorized Instructional Platform.

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

Learning Outcomes:

  • build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications
  • train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
  • build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
  • build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

General Topics:

  • Introduction to Deep Learning Neural Networks Basics Shallow Neural Networks Deep Neural Networks Practical Aspects of Deep Learning Optimization Algorithms Hyperparameter Tuning, Batch Normalization and Programming Frameworks Introduction to ML Strategy Foundations of Convolutional Neural Networks Deep Convolutional Models: Case Studies Object Detection Special Applications: Face recognition & Neural Style Transfer Recurrent Neural Networks Natural Language Processing & Word Embeddings Sequence Models & Attention Mechanism Transformer Network
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Case Studies
  • Lectures
  • Practical Exercises

Methods of Assessment:

  • Other
  • Quizzes
  • Programming assignment

Minimum Passing Score:

Supplemental Materials