Deep Learning Specialization Course Syllabus

Overview: This specialization is designed to provide a comprehensive and practical understanding of deep learning, spanning approximately 18 weeks of self-paced learning. Each course builds on the previous one, covering foundational concepts to advanced architectures. Learners will gain hands-on experience implementing models using industry-standard frameworks, with a strong emphasis on real-world applications and project structuring. Estimated total time: 110 hours.

Module 1: Neural Networks and Deep Learning

Estimated time: 20 hours

• Foundations of deep learning and binary classification
• Implementing neural network forward and backward propagation
• Vectorized computation for efficient training
• Building a shallow neural network from scratch

Module 2: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization

Estimated time: 20 hours

• Initialization techniques: Xavier and He initialization
• Regularization strategies including dropout and L2 regularization
• Optimization algorithms: Momentum, RMSprop, and Adam
• Hyperparameter tuning and batch normalization

Module 3: Structuring Machine Learning Projects

Estimated time: 10 hours

• Diagnostics for bias and variance in machine learning systems
• Error analysis and data mismatch handling
• Best practices for prioritizing project improvements

Module 4: Convolutional Neural Networks

Estimated time: 25 hours

• Architecture of convolutional neural networks (CNNs)
• Applications in image classification and object detection
• Neural style transfer and face recognition systems

Module 5: Sequence Models

Estimated time: 25 hours

• Recurrent neural networks (RNNs) and their variants
• Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs)
• Attention mechanisms and sequence-to-sequence models
• Applications in speech recognition and language modeling

Module 6: Final Project

Estimated time: 10 hours

• Design and train a deep learning model using TensorFlow
• Apply techniques such as dropout, batch normalization, and hyperparameter tuning
• Submit a working model with documentation for real-world application

Prerequisites

• Intermediate Python programming skills
• Familiarity with basic machine learning concepts (e.g., supervised learning)
• Understanding of linear algebra and calculus fundamentals

What You'll Be Able to Do After

• Build and train deep neural networks with vectorized implementations
• Apply regularization and optimization techniques to improve model performance
• Develop convolutional networks for computer vision tasks
• Construct sequence models for NLP and speech applications
• Structure and debug machine learning projects effectively