๐ป ๐ฆ๐ฒ๐๐๐ถ๐ผ๐ป ๐ฎ ๐ผ๐ณ ๐บ๐ ๐ฝ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฎ๐น ๐ฑ๐ฒ๐ฒ๐ฝ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฐ๐ผ๐๐ฟ๐๐ฒ: ๐ฃ๐๐ง๐ผ๐ฟ๐ฐ๐ต ๐๐ฒ๐ป๐๐ผ๐ฟ๐ ๐ฎ๐ป๐ฑ ๐๐๐๐ผ๐ฑ๐ถ๐ณ๐ณ
By the end of this session, you'll be able to code your own reverse mode autodiff to train a MLP
code ๐https://t.co/JLGiwnVDCG
๐งต
Just uploaded the code for the solution of the practicals so you can check your results:
https://t.co/Yz1ofLTXoA
Here are my confusion matrices for vgg and resnet. What's yours?
Up for another challenge, keep your notebook running. You will need your trained networks...๐ต๏ธ
๐ฉ๐๐๐ก๐ฒ๐ is a convolutional neural network architecture developed by the Visual Geometry Group at the University of Oxford, proposed in 2014.
This is the architecture we are fine-tuning in ๐ปSession 1 of the practical deep learning course ๐
https://t.co/Ka3MAVSABj
Why? ๐งต
Starting my ๐ฝ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฎ๐น ๐ฑ๐ฒ๐ฒ๐ฝ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฐ๐ผ๐๐ฟ๐๐ฒ!
๐ป Session 1: Start right away and train a deep neural network on a GPU to classify ๐ถ vs๐บ
This course will allow you to understand papers and codes available online and to adapt them to your own projects ๐งต
@mishadavinci Perfect timing! Sharing the tips and tricks for training deep learning models. Course starting this week on Twitter:
https://t.co/pvFwJl25Pi
In ๐๐ฐ๐ผ๐ฟ๐ฒ-๐ฏ๐ฎ๐๐ฒ๐ฑ ๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐๐ฒ ๐บ๐ผ๐ฑ๐ฒ๐น๐, generation is done by reversing the SDE perturbing the data. ๐A simple proof explaining the appearance of the score function in the time-reversed SDE. Estimating the reverse SDE can then be done with score matching.
Get ready for a ๐ฝ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฎ๐น ๐ฑ๐ฒ๐ฒ๐ฝ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฐ๐ผ๐๐ฟ๐๐ฒ:
Learn #PyTorch to build and train DL models. By the end of the course, you'll be able to understand and run top-notch DL techniques like transformers, diffusions, NERF, and more...
The good news is you can now access over 30 deep-learning notebooks for self-paced learning and for ๐ณ๐ฟ๐ฒ๐ฒ!๐๐ง
Check out our materials at๐https://t.co/GxtHvQ7A7C
Stay tuned for helpful solutions and expert tips!
๐ฅ๐๐ฒ๐ฒ๐ฝ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด: releasing more than 30 ๐ป๐ผ๐๐ฒ๐ฏ๐ผ๐ผ๐ธ๐ to learn #PyTorch ๐ป
๐https://t.co/GxtHvQ87Xa
Starts with the basics (autodiff, convolutions) all the way to attention, transformers, diffusion models, and more to come...
Links to videos and slides included
The denoising diffusion algorithm takes as input the noise schedule and a neural network estimating the mean of the denoiser๐
Here the variance of the denoiser is kept fixed, and better performances are obtained when the variance is also modeled by another neural network.
In decision tree learning, ๐๐๐ฏ (๐๐๐ฒ๐ฟ๐ฎ๐๐ถ๐๐ฒ ๐๐ถ๐ฐ๐ต๐ผ๐๐ผ๐บ๐ถ๐๐ฒ๐ฟ ๐ฏ) is an algorithm invented by Ross Quinlan (1986) that iterates through every unused attribute and selects the attribute with the largest information gain. https://t.co/FXnbEc3hJh
๐ฅ๐ป๐ฎ๐ป๐ผ-๐ฑ๐ถ๐ณ๐ณ๐๐๐ถ๐ผ๐ป and ๐บ๐ถ๐ฐ๐ฟ๐ผ-๐ฑ๐ถ๐ณ๐ณ๐๐๐ถ๐ผ๐ป: code, train, and finetune your ๐๐ฒ๐ป๐ผ๐ถ๐๐ถ๐ป๐ด ๐๐ถ๐ณ๐ณ๐๐๐ถ๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐๐ถ๐ฐ ๐ ๐ผ๐ฑ๐ฒ๐น from scratch on MNIST, CIFAR and more.
๐ขReleasing minimal implementations of denoising diffusions ๐งต
๐๐ฒ๐ป๐ผ๐ถ๐๐ถ๐ป๐ด ๐๐ถ๐ณ๐ณ๐๐๐ถ๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐๐ถ๐ฐ ๐ ๐ผ๐ฑ๐ฒ๐น๐ introduced a new type of generative model (like VAEs, GANS, or flow-based models). Diffusion models are inspired by non-equilibrium thermodynamics, and they learn to generate by denoising.
๐ข๐๐ Ready to play with ๐๐ฒ๐ป๐ผ๐ถ๐๐ถ๐ป๐ด ๐๐ถ๐ณ๐ณ๐๐๐ถ๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐๐ถ๐ฐ ๐ ๐ผ๐ฑ๐ฒ๐น๐ to generate ships, horses, or trucks!
๐ฅ Soon releasing course and code to build your own diffusion model and train it from scratch! ๐ค
Stay tuned! ๐ค
๐๐ฒ๐ป๐ผ๐ถ๐๐ถ๐ป๐ด ๐๐ฐ๐ผ๐ฟ๐ฒ ๐บ๐ฎ๐๐ฐ๐ต๐ถ๐ป๐ด
Perturb data and train a score-based model on the noisy data. For large noise, it improves the accuracy of estimated scores (in low data density regions). With multiple noise perturbations, you get a score-based generative model.
๐๐๐๐ถ๐บ๐ฎ๐๐ถ๐ป๐ด ๐ถ๐ป๐๐ฟ๐ฎ๐ฐ๐๐ฎ๐ฏ๐น๐ฒ ๐ฝ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐๐ถ๐ฐ ๐บ๐ผ๐ฑ๐ฒ๐น๐ ๐๐ถ๐ฎ ๐๐ฐ๐ผ๐ฟ๐ฒ-๐บ๐ฎ๐๐ฐ๐ต๐ถ๐ป๐ด
Score = gradient of the log-likelihood. Minimize the expected distance between the scores of the model and the data without knowing the data score๐
๐๐๐๐ผ๐บ๐ฎ๐๐ถ๐ฐ ๐ฑ๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐๐ถ๐ฎ๐๐ถ๐ผ๐ป: ๐ฉ๐๐ฃ ๐ฎ๐ป๐ฑ ๐ถ๐ป๐๐ฟ๐ผ ๐๐ผ ๐๐๐ซ
Backpropagation computes the gradient of the loss with respect to the weights of the neural network.
New course with a numpy functional programming implementation: https://t.co/DVoCxEkQH9
๐ก๐ผ๐ฟ๐บ๐ฎ๐น๐ถ๐๐ถ๐ป๐ด ๐๐น๐ผ๐ ๐ ๐ผ๐ฑ๐ฒ๐น๐: A flow-based ๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐๐ฒ ๐บ๐ผ๐ฑ๐ฒ๐น is constructed by a sequence of invertible transformations, and can learn the data distribution.
โจnew course ๐ https://t.co/SxamFcVaqn
๐ฅ new code ๐ https://t.co/LPLb1mHHYk
A spanning tree chosen randomly among all the spanning trees with equal probability is a ๐๐ป๐ถ๐ณ๐ผ๐ฟ๐บ ๐๐ฝ๐ฎ๐ป๐ป๐ถ๐ป๐ด ๐๐ฟ๐ฒ๐ฒ. Graphs have exponentially many spanning trees; ๐ช๐ถ๐น๐๐ผ๐ป'๐ ๐ฎ๐น๐ด๐ผ๐ฟ๐ถ๐๐ต๐บ generates uniform spanning trees more quickly than the cover time.
๐* was created in 1968 by Peter Hart, Nils Nilsson, and Bertram Raphael as part of the Shakey project, which aimed to build a mobile robot that could plan its own actions.
A beautiful blog post by @redblobgames with #Python code๐
https://t.co/0MymTLxPQg