As I delve deeper into DL, I feel I am losing on my pure stats, classical ML skills. Will prioritize revising -
Logistic reg, decision trees, RFs, XGB-LGB-CB, SVMs, KNN, TS-ARIMA for the next week.
Reading research papers shouldn't feel like learning a new language
Introducing real-time AI voice chat for arXiv papers 🎙️
Talk directly about any research paper in any language. Ask questions and request explanations, all through natural conversation 🚀
This year has been so defining. More on that later.
Target for the next 10 days :
1. Product recommendations using two-tower retrieval and XGB ranker.
2. Credit risk modeling
3. Noise reduction using GANs/AE
4. Brush up on my maths and coding skills.
Spent the weekend exploring the transformer architecture. Finally!
@SerranoAcademy's intuition behind attention being sort of a gravitational pull was so beautiful! I'll explore it even further and try to consolidate my understanding by implementing just the attention part.
used mat multiplications, vectorization, and broadcasting to improve legacy codebase. nothing feels more beautiful than applying maths and improving systems manifold. nothing is more exciting and peaceful than working on something that you so dearly love. grateful 🙏
i am using ransac homography transformation to try to match objects from 2 different images of the same scene (with different camera positions)
is there anyone out there with some experience on this? would really love some help.
#deepleaning#AI#computervision#cv#vision
started learning cuda! seems really fun. exploring gpu architectures on the go. the developments in computing in general.
following the pmpp book.
hope I continue on this journey along with my deep learning quest!
i don't like to update here quite often, but the progress of my knowledge has been good
worked on aws ec2 storage side of things, configured a gpu instance.
trained a yolov8s on sku110k dataset
next job is to fine-tune it on a custom dataset that i am yet to build.
This is an appreciation tweet for @2wlearning. The way you approach learning is beautiful ❤️ - the low level, intuition.
You're an inspiration. Keep going!
I did 2 things - normalized and added some transformations to images. Added BN layers after every Conv.
Have been training it for a week on 4 google acc colab free tier. Cutrently, at 50% accuracy.
Made me realise training from scratch is such a compute extensive process.
I did 2 things - normalized and added some transformations to images. Added BN layers after every Conv.
Have been training it for a week on 4 google acc colab free tier. Cutrently, at 50% accuracy.
Made me realise training from scratch is such a compute extensive process.
i am tryin to work on food101 image dataset. goal was to achieve any accuracy possible and then incrementally improve with tweaks to 'build an intuition' for convnets. train accuracy - 0.99% (random chance probability). here's my model. help me fix it pls #deeplearning
while dlcv by justin is amazing, i couldn't get my first cnn architecture beyond the random classification probability. let's see, the furnace is still active. will update here once i make some progress
envs setup, gpus spun up, ec2s ssh'd into, it all begins now.
task for the next week : set up a functioning baseline model.
easy, hoping ill be able to do this quickly.
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