What will you learn in this TensorFlow: Advanced Techniques Specialization Course
Build custom models, layers, and loss functions using TensorFlow’s Functional API.
Implement custom training loops and distributed training strategies.
Apply advanced computer vision techniques, including object detection and image segmentation.
Develop generative deep learning models, such as neural style transfer and autoencoders.
Program Overview
1. Custom Models, Layers, and Loss Functions with TensorFlow
⏳ 10 hours
Learn to create custom models, layers, and loss functions using TensorFlow’s Functional API. Build models like Siamese networks and implement custom training loops.
2. Custom and Distributed Training with TensorFlow
⏳ 10 hours
Understand TensorFlow’s execution modes and implement custom training loops. Explore distributed training strategies to scale model training.
3. Advanced Computer Vision with TensorFlow
⏳ 10 hours
Delve into advanced computer vision topics, including object detection and image segmentation. Apply models like ResNet-50 and Mask R-CNN to real-world datasets.
4. Generative Deep Learning with TensorFlow
⏳ 10 hours
Explore generative models, including neural style transfer and variational autoencoders. Learn to generate new images and apply style transfer techniques.
Get certificate
Job Outlook
Prepares learners for roles such as Machine Learning Engineer, Deep Learning Specialist, and AI Researcher.
Applicable in industries like technology, healthcare, finance, and autonomous systems.
Enhances employability by providing advanced skills in TensorFlow and deep learning techniques.
Supports career advancement in fields requiring expertise in custom model development and deployment.
Specification: TensorFlow: Advanced Techniques Specialization
|
