İsmail Güzel

PhD, Senior Researcher at TÜBİTAK ( @TubitakUlakbim) 62R40, 55N31, 62M10

[Atabey Kaygun @Atabey_Kaygun]

https://t.co/dnzgcz9JtZ

[Oscar Arias @OACerebro]

Enjoy!

https://t.co/ym1LsY9geD

[Frank Nielsen @FrnkNlsn]

"Probability and Statistics for Data Science"

Book home page: https://t.co/j7tCO3ixVj

include
A free preprint
103 Python notebooks
118 videos with slides
Solutions to 200 exercises

[İsmail Güzel @Guzel__Ismail]

@GundemOtesi #merakediyorum dolmabahçe sarayı

[Tom Yeh @ProfTomYeh]

[Transformer] by Hand ✍️
Make Your Own 🛠️👉 https://t.co/ZuXhITccaL

Over the past few months, I've collaborated with several AI educators to customize my AI by Hand exercises. I am glad that my materials are being used and appreciated in many classrooms around the world!

However, because the customization process is entirely by hand, sometimes my solution contains errors, only to be caught by students, which actually made me happy that students are paying attention. 😅

Lately, I have been thinking about developing a tool to let people create their own AI by Hand exercises, with custom numbers and solutions.

After considering a range of technologies, I decided to use Google Sheets. My goal is to maximize reach and broaden access.

Because this tool is still in the early stage, I would really appreciate your feedback!

What other topics do you want to see next?

[İsmail Güzel @Guzel__Ismail]

I am honored to be part of the TRUBA team and so glad to be to share that I am looking forward to taking part in next projects. #HPC #AI #ArtificialIntelligence #DataScience #Innovation #TechnologyNews #artificialsupercomputer

[TRUBA (Türk Ulusal Bilim e-Altyapısı) @TrubaUlakbim]

🎨 TRUBA Ekibi olarak, renkli çalışmalarımız devam edecek!.

Teknoloji ve inovasyonla dolu projeler için sabırsızlanıyoruz.

Takipte kalın! 🚀
#TRUBA #Teknoloji #İnovasyon #Yaratıcılık

[Turing Post @TheTuringPost]

Introducing Fully Sharded Data Parallel (FSDP)!

@PyTorch FSDP presented by @AIatMeta is a game-changer for training massive neural network models that exceed the memory capacities of individual GPUs.

Traditional Distributed Data Parallel (DDP) in PyTorch is limited by GPU memory, hindering large model training. FSDP is the solution with cutting-edge techniques and seamless PyTorch integration.

Key Benefits of FSDP:

• Optimized resource utilization:
Shards model parameters across GPUs, enabling the training of significantly larger models.

• Seamless integration:
Works hand-in-hand with @PyTorch's core components, like Tensor implementation, the dispatcher system, and CUDA memory caching allocator, for a user-friendly experience.

• Deferred initialization:
Simplifies memory management by creating models on a dummy device before sharding them.

• Configurable sharding strategies:
Adapts to various hardware configurations for optimal performance.

• Communication Optimization
Overlaps communication with computation, achieving near-linear scalability in TFLOPS.

Why FSDP?

• Scalability: Train models larger than ever before.
• Efficiency: Maintain high performance with near-linear scalability.
• Flexibility: Customize sharding strategies to your specific setup.
• Ease of use: Integrates seamlessly into existing workflows.

Embrace FSDP to efficiently train cutting-edge models and unlock new possibilities in AI.

More info about FSDP in our article: https://t.co/1NLxENNBOo

[TRT Haber Canlı @trthabercanli]

Sanayi ve Teknoloji Bakanı Mehmet Fatih Kacır, MODSİMMER TRUBA Veri Merkezi Açılışı ve ARF Süper Bilgisayarı Tanıtım Töreni'nde konuşuyor. https://t.co/RrnYbeGa8f

[Sanayi ve Teknoloji Bakanlığı @TCSanayi]

Bakanımız Sayın @mfatihkacir, MODSİMMER TRUBA Veri Merkezi Açılışı ve ARF Süper Bilgisayarı Tanıtım Töreni'nde konuşuyor.

📍ODTÜ

📡 #CanlıYayın https://t.co/XhA99QqkBV

[Cemal Başargan  @cemalbasargan]

Makine Öğrenmesi için Matematik

Garrett Thomas - Berkeley Üniversitesi

(Lineer Cebir, Kalkülüs ve Optimizasyon, Olasılık)
Link: https://t.co/nWu9pHkQ6G

[Mehmet Songur @mehmetsongur_]

Makine öğrenimi yöntemlerinin finans alanında nasıl uygulanabileceğini inceleyen kapsamlı (ve açık erişimli) bir çalışma...

🔗https://t.co/vu5hhRER9L

[Atabey Kaygun @Atabey_Kaygun]

Lectures on Mathematical Models of Generative Linguistics by one of my favorite people https://t.co/qzpFbJe7j6

[Anil Ozturk @anil_ozturkk]

CUDA programlama ve PyTorch entegrasyonu hakkında video, slayt ve kodlara erişebileceğiniz adeta bir müfredat reposu. CUDA ile akıl sağlığını kaybetmek isteyen arkadaşlar bakabilir. 😄

GitHub: https://t.co/Fz4HjShT2z

[Ivory Mind @ivorymindai]

This is the best new AI tool for research.

Just upload a PDF file, ask a question, and get an answer in seconds.

It's like ChatGPT, but built for researchers.

Try it for free.

[Ozan Sihay @ozansihay]

Bugün tanıtılan GPT-4o ile simultane çevirinin ruhuna El-Fatiha diyebiliriz.

[Anil Ozturk @anil_ozturkk]

ML çözümleri geliştirme ve daha önemlisi ürünleştirme üzerine örnekler ve ders sayfaları içeren bir repo.

Link: https://t.co/Gv2VzbODqL

[Uraz Akgül @urazdev]

River, online ML modelleri oluşturmak için geliştirilmiş bir kütüphane. Çeşitli veri setleri ile sınıflandırma, regresyon, anomali tespiti, zaman serisi tahmini vb. görevleri yapmanızı sağlıyor. Konsepti çok sevdim.

https://t.co/lCIf5PgfYG

[İnan Ünal @drinanunal]

Çok faydalı ders notlarının olduğu, Prof. Song Ahn tarafından oluşturulan bir web sayfası. Çok iyi anlatımlar mevcut👍

https://t.co/Y6Vra5xXzC

[İsmail Güzel @Guzel__Ismail]

@urazdev YSK'nın sunduğu veriler: https://t.co/GbTKq48OSR

[Mustafa Hajij @HajijMustafa]

Discover Topological Deep Learning (TDL) – a fascinating new area of research expanding deep learning's reach across various domains like cell complexes, hypergraphs, and more. Interested? Check out my recent introductory talk on TDL at NeurIPS 2023: https://t.co/Ers3QbVZUy