tglcourse 0.0.5

work-in-progress course

The Generative Landscape

This is still a work in progress, but most of the important bits should be done in time for a launch on Friday, Novermer 25! In the meantime, you can check out my previous course, AIAIART or join the Discord where I’ll be streaming final lesson checks and updates.

If you want to support this effort, I now have a Patreon: https://www.patreon.com/johnowhitaker

The material will show in github pages at https://johnowhitaker.github.io/tglcourse/ (and the http://thegenerativelandscape.com will redirect there now too). Hooray for the magic of nbdev.

Join our Discord to discuss the course, join study groups or chat about all things generative. That’s also the place to go for notifications of live lesson walkthroughs and course updates.

Check out the Getting Started page for an overview of the course and more information on things like study groups.

Check out the Library page for information on the tglcourse library that accompanies the course.

The Plan

The idea is to have a core curriculum building up to an understanding of key generative modelling techniques, split into three rough sections. The first 5 lessons will cover basics of building NNs and crafting loss functions. Lessons 6-10 will introduce generative modelling, GANs and working with sequences using transformers. Lessons 11-15 will be a deep dive into diffusion models, and lesson 16 will wrap up and give sugestions for new directions to explore.

Alongside this will be a number of ‘bonus’ notebooks that don’t need to be completed but which augment the core content. Managing datasets, experiment tracking, sharing demos and so on. Some will augment specific lessons, some will add functionality to the library and some will just be standalone topics I think are cool. The latter category is likely to continue to grow even after the course launches :)

There will be three suggested projects (after lessons 5, 11 and 14) to mark milestones in the course, and the final lesson will also encourage you to do a larger project at the end. Once we launch there’ll be a place to everyone’s projects and some prizes for the best.

This table has a rough status on the main lessons.

Lesson	Description	Status
Lesson 1: PyTorch Basics	Intro to PT, tensor manipulation, images as tensors, LeastAverageImage exercise	Done
Lesson 2: Optimization	Intro to GD, optimization examples exercise	Rough Draft Done
Lesson 3: Building NNs	nn.Module, building blocks, CNNs	Rough Draft Done
Lesson 4: Learning Representations	What do networks learn, style transfer	WIP
Lesson 5: CLIP	Demo use as a loss function, video deep dive into VQGAN notebooks	Rough Draft Done
Lesson 6: Generative Modelling	VAE part, latent walks, PCA	WIP
Lesson 7: GANs 1	Intro to GANs, DC GAN, Conditioning	WIP
Lesson 8: GANs 2	GAN training tricks, NOGAN, using modern GANs, VQGAN	Not Started
Lesson 9: Sequence Modelling Intro	idea, language modelling concept, transformer demo	WIP
Lesson 10: Transformers	Intro to transformes, attention, comparing to lstm, reading minGPT	Not Started
Lesson 11: Everythign is a sequence	Show whistlegen, protein, VQGAN, parti…	Not Started
Lesson 12: DM 1	Intro to diffusion models, toy example, comparison to DDPM	Rough Draft Done
Lesson 13: DM2	Conditioning, CFG, guiding, sampling, better training	WIP
Lesson 14: DM3	SD deep dive	Rough Draft Done
Lesson 15: DM4	Other modalities - ideally demo class-conditioned audio generation. Might not be done by the time the course launches but would be nice to have.	WIP
Lesson 16: Going Further	Finding your niche, exploring less common areas	WIP

FAQs

Some course-related questions that have tricked in:
+ ‘Any prerequisites?’: If you’re comfortable with a bit of Python and using Jupyter Notebooks you should be ready to take this course. No prior deep learning knowledge is assumed, and although we will dive fairly deep fairly quickly I’ve tried to link lots of resources wherever possible.
+ ‘How long is the course?’: There are 15 core lessons plus a number of bonus notebooks. You can take them at whatever pace you find enjoyable, or join in with a study group on Discord to work through one a week.
+ ‘Does the HuggingFace Diffusion Model Class supercede this?’: I’ve teamed up with HF to help build their diffusion model class, sharing a lot of material between it and this course. Both will have unique things to add - I’d recommend signing up for theirs even if you’re working through ‘The Generative Landscape’ as well, since there will be extra projects and fun community activities to get involved in with that one too.
+ ‘Can genereative landscape be real?’: Yes, if you subscribe to David Chalmers ‘simulation realism’ ;)
+ ‘Will it include stable diffusion?’: Yes, see lesson 14
+ ‘Will we learn how to adapt these method to 3d?’: At some point I’d love to add more 3D related content - stay tuned for bonus notebooks once the craziness of the course launch calms down.
+ ‘I hope this includes generating text!’: It does! Lessons 9 - 11 deal with modelling sequences, and should set you up with everything you need to make models which can spew out AI-generated gibberish all day long.
+‘My question isn’t in the FAQs?’: OK, so I made this one up. But if you have a burning question, send it to me and I’ll add it here.

CI status:

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