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Voices from the forefront of engineering

Explore our technical log, industry analyses, and strategic reflections on building world-class digital products.

PyTorch Custom Dataset and DataLoader: getitem, len, collate_fn, and num_workers

DataLoader is more than a loop — it's a parallel data pipeline. Build a correct Dataset, write a proper collate_fn, and understand num_workers to eliminate training bottlenecks.

MobiwebGS

5 min read

Technology

Custom Training Loops with GradientTape: Manual Forward and Backward Passes in TensorFlow

model.fit() hides the training loop. GradientTape exposes it. Use it when you need per-batch gradient manipulation, custom loss combinations, or training dynamics that Keras callbacks can't express.

MobiwebGS

5 min read

Technology

Optimizer Comparison: SGD, Momentum, Adam, RMSprop, and When Each Shines

Different optimizers suit different problems. SGD is stable, Momentum accelerates, Adam is adaptive. Understand why each optimizer works and pick the right one.

MobiwebGS

5 min read

LLMs, Architecture

Language Model Architectures: Transformers, Attention, and the Path from GPT-1 to GPT-4

Modern LLMs are Transformers. Understand the evolution: self-attention, positional encoding, scaling laws, and how each architectural change improved performance.

MobiwebGS

5 min read

Ai/ml

Rotary Positional Embeddings (RoPE): How It Works and Why It Beats Learned Embeddings

RoPE encodes position by rotating query and key vectors in complex space. It extrapolates beyond training length, transfers across fine-tuning, and adds zero parameters — here's the math.

MobiwebGS

5 min read

LangChain ,LLM ,Python, LCEL, Runnables

LangChain Architecture Overview: Chains, Runnables, LCEL, and the New vs Old API

LangChain's architecture changed fundamentally with v0.1. Learn chains, runnables, and LCEL so you build on the current API — not the deprecated one.

Mobiwebgs

5 min read

Voices from the forefront of engineering

PyTorch Custom Dataset and DataLoader: __getitem__, __len__, collate_fn, and num_workers

Custom Training Loops with GradientTape: Manual Forward and Backward Passes in TensorFlow

Optimizer Comparison: SGD, Momentum, Adam, RMSprop, and When Each Shines

Language Model Architectures: Transformers, Attention, and the Path from GPT-1 to GPT-4

Rotary Positional Embeddings (RoPE): How It Works and Why It Beats Learned Embeddings

LangChain Architecture Overview: Chains, Runnables, LCEL, and the New vs Old API

PyTorch Custom Dataset and DataLoader: getitem, len, collate_fn, and num_workers