Course Summary
Learn how to use TensorFlow to create generative deep learning models and explore the fascinating world of artificial intelligence (AI).Key Learning Points
- Gain practical skills to create generative deep learning models using TensorFlow
- Understand the fundamentals of deep learning and how it can be applied to create AI models
- Explore a range of real-world applications of generative deep learning
Related Topics for further study
Learning Outcomes
- Create generative deep learning models using TensorFlow
- Understand the fundamentals of deep learning and its applications
- Apply generative deep learning to real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video Lectures
Similar Courses
- Deep Learning Specialization
- Applied AI with Deep Learning
- Advanced Machine Learning with TensorFlow on Google Cloud Platform
Related Education Paths
Notable People in This Field
- Director of Machine Learning at Apple
- Co-founder of Coursera and Google Brain
Related Books
Description
In this course, you will:
Outline
- Week 1: Style Transfer
- Welcome to Course 4
- Style Transfer Intro
- Style Transfer Conceptual Overview
- Pre-Processing Inputs
- Extracting Style and Content Features
- Total Loss and Content Loss
- Style Loss
- Update the Generated Image
- Optional - Gram Matrix
- Optional - Einstein Notation
- Optional - Einsum in Code
- Total Variation Loss
- Fast Neural Style Transfer
- Connect with your mentors and fellow learners on Slack!
- Reference: A Neural Algorithm of Artistic Style
- Reference: Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- Reference: Visualizing and Understanding Convolutional Networks
- Reference: numpy.einsum
- Reference: Exploring the structure of a real-time, arbitrary neural artistic stylization network
- Style Transfer
- Week 2: AutoEncoders
- Introduction
- First AutoEncoder
- MNIST AutoEncoder
- MNIST Deep AutoEncoder
- Convolutional AutoEncoder
- Denoising with an AutoEncoder
- AutoEncoders
- Week 3: Variational AutoEncoders
- Variational AutoEncoders Overview
- VAE Architecture and Code
- Sampling Layer and Encoder
- Decoder
- Loss Function and Model Definition
- Train the VAE Model
- References: Kullback–Leibler divergence, Balancing reconstruction error and Kullback-Leibler divergence in Variational Autoencoders
- Convolutional Variational AutoEncoders
- Variational AutoEncoders
- Week 4: GANs
- Introduction
- First GAN Architecture
- First GAN Training Loop
- DCGANs
- Face Generator
- Face Generator Discriminator
- Conclusions
- Reference: GANs Specialization
- Reference: Self-Normalizing Neural Networks
- Reference: - Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , tf.keras.layers.LeakyReLU
- Reference: Layer Normalization
- References
- What next?
- Acknowledgments
- (Optional) Opportunity to Mentor Other Learners
- GANs
Summary of User Reviews
Generative Deep Learning with TensorFlow is a highly rated course that teaches students how to use deep learning to create generative art and music. Many users praise the course for its clear and concise explanations of complex concepts.Key Aspect Users Liked About This Course
The course is well-structured and covers a wide range of topics related to generative deep learningPros from User Reviews
- The course is engaging and well-paced
- The instructors are knowledgeable and provide clear explanations
- The course materials are comprehensive and easy to follow
- The course provides a good balance of theory and practical applications
Cons from User Reviews
- Some users found the course to be too technical and challenging
- The course may not be suitable for beginners with no background in deep learning
- The course requires a significant time commitment to complete
- Some users felt that the course could benefit from additional hands-on exercises
- The course is relatively expensive compared to other online courses
Laurence Moroney
4.9 Raiting
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