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DeepLearning.AI

TensorFlow: Advanced Techniques Specialization

  • up to 5 months
  • Intermediate

The TensorFlow: Advanced Techniques Specialization introduces advanced features of TensorFlow, providing learners with more control over their model architecture and tools to create and train sophisticated ML models. This course covers custom models, layers, loss functions, distributed training, advanced computer vision, and generative deep learning.

  • Custom Models
  • Custom Layers
  • Custom Loss Functions
  • Distributed Training
  • Advanced Computer Vision

Overview

In this Specialization, you will expand your knowledge of the Functional API and build exotic non-sequential model types. You will learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. You will also explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Professional Certification
    upon course completion
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Intermediate Machine Learning Practitioners

Individuals with some experience in machine learning looking to deepen their understanding of TensorFlow and advanced techniques.

Data Scientists

Professionals aiming to enhance their skills in building and optimizing advanced machine learning models using TensorFlow.

AI Enthusiasts

Learners passionate about AI and deep learning who want to explore generative models and advanced computer vision techniques.

This Specialization offers a deep dive into advanced TensorFlow techniques, perfect for intermediate practitioners looking to enhance their skills. You'll learn to build custom models, optimize training, and explore cutting-edge computer vision and generative deep learning techniques, helping you advance your career in AI and machine learning.

Pre-Requisites

1 / 3

  • Basic understanding of machine learning concepts

  • Familiarity with TensorFlow and Python programming

  • Experience with neural networks and deep learning

What will you learn?

Custom Models, Layers, and Loss Functions with TensorFlow
Learn to build custom models, layers, and loss functions using TensorFlow's Functional API. Explore custom callbacks and residual networks.
Custom and Distributed Training with TensorFlow
Understand Tensor objects, custom training loops, graph mode, and distributed training strategies to optimize model training.
Advanced Computer Vision with TensorFlow
Dive into image classification, object detection, image segmentation, and model visualization and interpretation techniques.
Generative Deep Learning with TensorFlow
Explore neural style transfer, AutoEncoders, Variational AutoEncoders (VAEs), and Generative Adversarial Networks (GANs) to create new content.

Meet your instructors

  • Laurence Moroney

    Instructor, DeepLearning.AI

    Laurence Moroney is an award-winning AI researcher, best-selling author, and Fellow at the AI Fund who has been driving the corporate-wide narrative for AI and ML developers at Google for over 10 years.

  • Eddy Shyu

    Curriculum Product Manager, DeepLearning.AI

    Eddy Shyu is a Curriculum Product Manager at DeepLearning.AI. He designed and led the creation of Andrew Ng's Machine Learning Specialization and 14 other AI/ML courses with over 500,000 unique learners enrolled.

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