TensorFlow: Advanced Techniques Specialization Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This specialization is designed for learners with prior experience in machine learning and Python programming who want to deepen their TensorFlow expertise. Over approximately 40 hours of content, you'll progress through four technical modules covering advanced model building, custom training, computer vision, and generative deep learning. Each module combines theory with hands-on coding exercises, culminating in a final project that integrates your skills. The flexible structure allows working professionals to learn at their own pace while gaining practical, production-ready knowledge.
Module 1: Custom Models, Layers, and Loss Functions with TensorFlow
Estimated time: 10 hours
- Building models using TensorFlow's Functional API
- Creating custom layers and loss functions
- Implementing Siamese networks
- Designing custom training loops
Module 2: Custom and Distributed Training with TensorFlow
Estimated time: 10 hours
- Understanding TensorFlow's execution modes
- Implementing custom training loops with GradientTape
- Scaling training using distributed strategies
- Optimizing model performance across multiple devices
Module 3: Advanced Computer Vision with TensorFlow
Estimated time: 10 hours
- Applying object detection models like Mask R-CNN
- Implementing image segmentation techniques
- Using pre-trained models such as ResNet-50
- Working with real-world image datasets
Module 4: Generative Deep Learning with TensorFlow
Estimated time: 10 hours
- Building neural style transfer systems
- Designing variational autoencoders (VAEs)
- Generating new images using deep generative models
- Applying style transfer to artistic and real-world images
Module 5: Final Project
Estimated time: 10 hours
- Design and implement a custom deep learning model
- Apply advanced techniques from multiple modules
- Submit code and a project report for evaluation
Prerequisites
- Proficiency in Python programming
- Familiarity with machine learning concepts and neural networks
- Basic understanding of TensorFlow and Keras APIs
What You'll Be Able to Do After
- Build and train custom deep learning models using TensorFlow's Functional API
- Implement flexible training loops tailored to specific model needs
- Apply advanced computer vision techniques to real-world problems
- Develop generative models for image synthesis and style transfer
- Deploy scalable training workflows using distributed strategies