Welcome to the official code and experiment hub for the DeepLearning MegaThread — a 5-part blog series by the GDGC ML Team that dives into the practical side of deep learning with a perfect blend of theory, code, and good vibes. 🚀
We’re talking hands-on implementation, clear explanations, and models that actually work — all wrapped into weekly drops designed to make deep learning more accessible, exciting, and deployable.
📅 New blog every Friday! (And yes, we’re having a blast writing them.)
Author: Anaant Raj
Anaant kicked things off with a banger 💥 by walking us through how to fine-tune MobileNetV2 on CIFAR-10.
From loading pre-trained weights to understanding which layers to freeze, this post is a must-read if you’re diving into transfer learning.
Author: Nidhi Rohra
Nidhi broke down the GPT-2 architecture and the entire Transformer revolution with crystal-clear visuals and that golden example:
“In ‘The cat sat on the mat,’ the word ‘sat’ pays attention to both ‘cat’ and ‘mat.’” 🤯
She also included runnable code and a clean walkthrough of why decoder-only models like GPT-2 are so powerful.
Author: Arihant Bhandari
CNNs are great at extracting local features. Transformers are amazing at global reasoning. So... why not both?
MobileViT does just that — blending MobileNet’s efficiency with ViT’s attention mechanism for a hybrid model that punches way above its size.
We experiment with MobileViT-XXS under multiple settings using Tiny ImageNet to test how image resolution, pretraining, and architecture affect performance.
All the code, experiments, and notebooks from the series, neatly organized for you to run, tweak, and learn from.
| Notebook | Description |
|---|---|
mobilevit_xxs_scratch_64.ipynb |
Trains MobileViT-XXS from scratch on 64×64 Tiny ImageNet. |
mobilevit_xxs_pretrained_64.ipynb |
Fine-tunes pretrained MobileViT-XXS on 64×64 input. |
mobilevit_xxs_scratch_96.ipynb |
Trains from scratch on 96×96 resolution images. |
mobilevit_xxs_pretrained_96.ipynb |
Fine-tunes pretrained MobileViT-XXS on higher-res input. |
Because let’s face it — deep learning can feel like a black box sometimes.
This series is our way of turning that box transparent.
- 🤖 From pre-trained models to attention mechanisms
- 🧠 From “how it works” to “why it matters”
- 💻 From paper to practice
Whether you're a beginner or a practitioner brushing up on fundamentals, we’ve got something here for you.
🧠 Arihant Bhandari
🧠 Sahil Garje
🧠 Anaant Raj
🧠 Nidhi Rohra
🧠 Samyak Waghdare
We're the GDGC ML Team — on a mission to make deep learning more practical, fun, and open to all.
Spotted a bug, want to contribute, or just want to say hi?
Open an issue, fork the repo, or comment on the blog posts — we love hearing from fellow learners and builders.
Thanks for stopping by. Now go train something cool. 🧠💻🔥