dfitripelago 🍁

kalau dunia udah jahat, jadilah yang paling baik sama diri kamu sendiri 🥰 | born to shine 💫

[dfitripelago 🍁 @dfitripelago]

Be good to yourself, first.
The rest of the world can wait.

[dfitripelago 🍁 @dfitripelago]

Propaganda u have to give up :
2026 and someone with no instagram acc

[dfitripelago 🍁 @dfitripelago]

Now am thinking about PhD at UC Davis or Caltech

[dfitripelago 🍁 @dfitripelago]

ngakak knp tiba2 nyanyi jawa ni orang 😂

[dfitripelago 🍁 @dfitripelago]

seriously unseriously

[dfitripelago 🍁 @dfitripelago]

never try a small chat to a professor otherwise it will end up with long paragraph full of philosophy then i lost my mind what to answer

[dfitripelago 🍁 @dfitripelago]

Selain firasat yang gak pernah meleset, Bukti juga datang tanpa dicari. Lihatlah bagaimana jalur langit berpihak kepada orang yang 'tulus'.

[dfitripelago 🍁 @dfitripelago]

Ramadhan day 27th

[dfitripelago 🍁 @dfitripelago]

am waiting happily 🥰

[dfitripelago 🍁 @dfitripelago]

Bangalore tonight drop to 15°C 🥶🥶 better i sleep in library cz it get me warm than my room 📚

[dfitripelago 🍁 @dfitripelago]

Bismillah flight back to Bangalore

[dfitripelago 🍁 @dfitripelago]

turns out i realize that i only need the head 😌🥰

[dfitripelago 🍁 @dfitripelago]

تمنا یہی ہے کہ تم ہمیشہ ساتھ رہو-
Tamanna yahi hai ke tum hamesha saath raho
My only wish is that you always stay with me

[dfitripelago 🍁 @dfitripelago]

Homeeey semarang alhamdulillah

[Vaishnavi @_vmlops]

A collection of Python implementations for ML algorithms built from scratch

https://t.co/7jtcie4Kkj

[Tech with Mak @techNmak]

Make the most of your weekend.

Don't sleep on this.

Stanford's Autumn 2025 Transformers & LLMs course. 8 lectures. Free.

While others scroll, you could understand how Flash Attention achieves 3x speedup, how LoRA cuts fine-tuning costs by 90%, and how MoE makes models efficient.

➕ What's covered:

➡️ Lecture 1: Transformer Fundamentals
→ Tokenization and word representation
→ Self-attention mechanism explained
→ Complete transformer architecture
→ Detailed implementation example

➡️ Lecture 2: Advanced Transformer Techniques
→ Position embeddings (RoPE, ALiBi, T5 bias)
→ Layer normalization and sparse attention
→ BERT deep dive and finetuning
→ Extensions of BERT

➡️ Lecture 3: LLMs & Inference Optimization
→ Mixture of Experts (MoE) explained
→ Decoding strategies (greedy, beam search, sampling)
→ Prompting and in-context learning
→ Chain-of-thought reasoning
→ Inference optimizations (KV cache, PagedAttention)

➡️ Lecture 4: LLM Training & Fine-tuning
→ Pretraining and scaling laws (Chinchilla law)
→ Training optimizations (ZeRO, model parallelism)
→ Flash Attention for 3x speedup
→ Quantization and mixed precision
→ Parameter-efficient finetuning (LoRA, QLoRA)

➡️ Lecture 5: LLM Tuning
→ Preference tuning
→ RLHF overview
→ Reward modeling
→ RL approaches (PPO and variants)
→ DPO

➡️ Lecture 6: LLM Reasoning
→ Reasoning models
→ RL for reasoning
→ GRPO
→ Scaling

➡️ Lecture 7: Agentic LLMs
→ Retrieval-augmented generation
→ Advanced RAG techniques
→ Function calling
→ Agents
→ ReAct framework

➡️Lecture 8: LLM Evaluation
→ LLM-as-a-judge overview
→Best practices and benefits
→Biases and pitfalls

From Stanford Online:
Rigorous instruction. Latest techniques. Free access.

Perfect for:
→ ML engineers building with LLMs
→ AI engineers understanding transformers
→ Researchers working on language models
→ Anyone learning beyond API calls

This weekend: learn the techniques that separate good engineers from great ones.

(I will put the playlist in the comments.)

♻️ Repost to save someone $$$ and a lot of confusion.
✔️ Follow @techNmak for more AI/ML insights.

[dfitripelago 🍁 @dfitripelago]

Perpus pagi ini syahdu top banget enak buat belajar.. bismillah

[dfitripelago 🍁 @dfitripelago]

In the silence between two hearts, the fear of losing one another becomes the clearest thing of all

[dfitripelago 🍁 @dfitripelago]

September has begun and my third semester going hectic but I love that spark ✨️