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Sayak Paul Profile picture
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3 Nov, 3 tweets, 1 min read
This has to be the best #Keras example in 2021 so far. My criteria are: brief coverage of the underlying concepts, code understandability, and correctness, lucidity in the language. But beyond these factors, this example is just too much awesomeness.

keras.io/examples/gener…
The author András Béres goes to great lengths to provide a comprehensive overview of the SoTA in GANs providing tips, tricks, and lessons from their experiences.

IMO, this is what makes an article truly fantastic.

Of course, the code is really high-quality.
Still `model.compile()` and `model.fit()`, btw! Pretty audacious, ain't it?

Can't help mentioning this time and time again 😂

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More from @RisingSayak

Sayak Paul Profile picture
1 Nov
@soumikRakshit96 and I have been working on this project for a while now. Today, we are delighted to share our progress.

Point cloud segmentation in the wild with @TensorFlow:

github.com/soumik12345/po…

1/
Our repository comes with full TPU support. You can also use multiple GPUs with mixed-precision (when supported). Here's a blog post to get started:

keras.io/examples/visio…

Thanks to @fchollet for your reviews.

2/
We provide standalone scripts and also notebooks for training and testing our models. We open-source all the experimental results and pre-trained models:

github.com/soumik12345/po…

3/ Image
Read 4 tweets
Sayak Paul Profile picture
1 Jun
Recipes that I find to be beneficial when working in low-data/imbalance regimes (vision):

* Use a weighted loss function &/or focal loss.
* Either use simpler/shallower models or use models that are known to work well in these cases. Ex: SimCLRV2, Big Transfer, DINO, etc.

1/n
* Use MixUp or CutMix in the augmentation pipeline to relax the space of marginals.
* Ensure a certain percent of minority class data is always present during each mini-batch. In @TensorFlow, this can be done using `rejection_resampling`.

tensorflow.org/guide/data#rej…

2/n
* Use semi-supervised learning recipes that combine the benefits of self-supervision and few-shot learning. Ex: PAWS by @facebookai.
* Use of SWA is generally advised for better generalization but its use in these regimes is particularly useful.

3/n
Read 4 tweets
Sayak Paul Profile picture
26 Apr
New #Keras example is up on *consistency regularization*or an important recipe for semi-supervised learning and tackling distribution shifts as shown in *Noisy Student Training*.

keras.io/examples/visio…

1/n
This example provides a template for performing semi-supervised / weakly supervised learning. A few things one can plug right in:

* Incorporate more data while training the student.
* Filter the high-confidence predictions while training the student.

2/n
The example uses Stochastic Weight Averaging during training the teacher to induce geometric ensembling. With elements like Stochastic Dropout, the performance might even be better.

Here are the full experiments: git.io/JO55v.
Read 5 tweets
Sayak Paul Profile picture
2 Dec 20
Got the @TensorFlow Developer Certification.

Thanks to the #ML @GoogleDevExpert program for sponsoring the exam.

In this thread, I will summarize my experience.

⬇️
If you use @TensorFlow in your work moderately, I think you already have the prerequisites. Definitely take the *TensorFlow in Practice* specialization by @lmoroney & @DeepLearningAI_. It will get you up to speed.

Study the contents rigorously.
Review the certificate handbook carefully. It really has all the information you need to know about the certification - tensorflow.org/extras/cert/TF….

* Install @pycharm & get sufficiently comfortable with it.
* Set up the exam environment properly.
Read 6 tweets

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