#pytorch

Two Clocks, One Training Step: How TraceML Measures PyTorch Performance

A time.perf_counter() bracket and a CUDA event pair can disagree wildly about the same forward pass. Neither is wrong: they answer…

Your Agentic Stack Waste Too Many GPU Kernels

Once an agentic product moves beyond a hosted API demo, you hit a bottleneck that dominates your agentic loop: the local neural components…

Three Ways to Teach Stable Diffusion a New Concept — and Why They Produce Wildly Different File…

Stable Diffusion already knows what a “painting” is, what a “dog” is, what “golden hour lighting” looks like. What it doesn’t know is your…

Train Neural Networks without Draining your Pocket: Mixed Precision in PyTorch using Lightning

A quick guide to demonstrate model training with Mixed precision in PyTorch using Lightning. Straightforward and easy to implement!

The Stable Diffusion Bookshelf: Which Book Actually Answers Your Question?

A field guide to six books on diffusion models — and how to tell which one you need before you spend $40 finding out

The Only Four Bits of Maths You Need to Understand Machine Learning

A plain-English map of what the maths actually does — with pictures.

PyTorch Just Launched a GPU Cult and Molecules Are Joining

You know that feeling when you try to fold a fitted sheet and it just refuses to cooperate?

I Tried to Put a Diffusion Model on a Mac. NumPy Had Other Plans.

[Error: Only 0-dimensional arrays can be converted to Python scalars: exporting SDXL to Core ML in 2026]

I Ran a Photo Backwards Through Stable Diffusion’s Text Encoder — Here’s What Survived

Every article about diffusion models runs the pipe forward: type words, get an image. This one runs it backward. Take an image nobody…

Training a Model on Google Colab, A Survival Guide

It was epoch 47 out of 60. The loss was finally going down, I was already imagining the finished model, and then the tab greeted me with a…

Mask R-CNN in Deep Learning: A Complete Guide to Object Detection and Instance Segmentation with a…

Introduction

Generating Realistic Mars Images Using DCGAN: A Deep Learning Guide

Learn how Deep Convolutional Generative Adversarial Networks (DCGANs) can generate realistic images of Mars by learning directly from real…

Building a Vision Language Model From Scratch: What I Learned Reimplementing PaliGemma in PyTorch

A deep dive into the design decisions, engineering trade-offs, and unexpected lessons from building a production-grade VLM from the ground…

Learn about ‘Edge AI’ like a child

Artificial Intelligence has become part of our everyday lives, and many AI features send your data to the internet for processing. That’s…

Understanding Transformers by Implementing a miniGPT in PyTorch

What are Language Models?

Tensors in PyTorch: A Structured Guide for LLM Work

Introduction

Why Flattening an Image Throws Away the Picture

Part 2: We stop treating a photo as a bag of loose numbers, and watch the accuracy jump. Meet the Convolutional Neural Network.

How Can A Model Recognize Things It Was Never Trained On?

What if a model could look at an image and understand it without ever being trained on a fixed set of classes?

XPUGraph vs torch.compile: Why I Was Wrong About Them Competing

I’d heard “XPUGraph” and “CUDAGraph” thrown around for a while but never sat down to actually understand them. I also had a persistent…

Why 2D Networks Fail at Video Recognition: Building a Temporal-Aware 3D CNN in PyTorch

Learn to bridge the spatial-temporal gap and train frame-aware architectures without blowing up your GPU memory.

Zero-Copy Sliding Windows: How to Reduce Memory Cost in Quantitative Deep Learning

In quantitative machine learning the bottleneck is almost never the model. It is the data pipeline feeding it. If your pipeline is slow and…

Building a Multi-Layer Perceptron by Hand

With modern frameworks like PyTorch or TensorFlow, it’s relatively straightforward to build a simple neural network. For instance, the…

The Ultimate Guide to Installing PyTorch on a Dedicated GPU Server

Stop bottlenecking your AI workloads. Learn how to configure a bare-metal software stack for maximum compute efficiency.

Building My First Neural Network From Scratch with PyTorch: A Journey on the Dry Bean Dataset

When I first started learning Deep Learning, I made myself one promise:

Diagnosing a PyTorch DataLoader Bottleneck in a Real Training Run

A ResNet-18 run wasted 43% of its GPU time on data loading. A three-line DataLoader change (num_workers, pin_memory, persistent_workers)…

Diffusion U-Net — Architecture and Building Blocks

Deploying a Deep Learning Model on the STM32N6 Nucleo Board: From PyTorch to Edge AI Inference

1. Introduction

Use ComfyUI in Pure Python Code, How To Do It Step By Step

Forget the spaghetti nodes. Control your diffusion pipeline with clean code that actually makes sense

The Mental Model of PyTorch: Build a Neural Network in 5 Phases

I stopped copy-pasting training loops and finally understood what every line does — here’s the whole picture, taught the way I wish someone…

You Don’t Need ONNX to Get a PyTorch Model onto an iPhone

Why Apple’s coremltools skips the "universal format" middleman entirely — and what's actually happening when it converts a model directly.

Efficiently Utilizing Your GPU While Training AI Models in PyTorch

A practical, code-first guide to making your training loop go at a lightning speed without rewriting everything from scratch.

Research Scientist Skills in 2026: A/B Testing Nearly Ties PyTorch

The Research Scientist Title Is Hiding a Fork in the Road

The TensorRT Rabbit Hole That Led Us to torch.autocast

How chasing a complex infrastructure migration taught us the real lever behind a 45% inference speedup — and why we didn’t need TensorRT at…

Why One GPU Isn’t Enough: What Happens When the Model No Longer Fits

A beginner's first-principles guide to GPU memory: why big models don't fit, what VRAM holds, and how multiple GPUs split the load.

How I Distilled a Gemini Vision Model Into a 4.6M-Parameter

Knowledge distillation onto frozen image embeddings in PyTorch: the negative-transfer trap, the micro-class fix, no GPU.

PyTorch Tensors Explained by Building Them in C++

If you come from a science or engineering background, you’ve probably run into the word tensor more times than you can count. If you…

Study Memo | 2026–06–15 & 16 | P4, 5, 6, 7 Tensor Mismatch from Redundant CFG Batch Scaling /…

June 15, 2026 (Mon) ~ June 16, 2026 (Tue)

Lesson 5: Building a Transformer Block from Scratch

How positional embeddings, multi-head attention, residual connections, and feed-forward networks come together inside GPT models

Practical Deep Learning: Getting Started (Read this if you’re stuck)

After jumping around from course to course, never having properly completed one from the beginning to the end. I finally have found the one…

Train Neural Networks without Draining your Pocket: Speed up your Model Training with TensorFlow’s…

This blog shows how to speed up your model training using TensorFlow’s Graph Execution. Just a few minor changes to save your GPU costs!

One Slow DDP Rank Can Hold Back Your Whole PyTorch Job

A PyTorch DDP job can be slow without looking broken. No crash. No OOM. All GPUs are doing something. The training loop just takes longer…

Sunday, June 14, 2026: (2,3) Handling Tensor Dimension Shifts Caused by Concatenating Instance and…

Sunday, June 14, 2026

Visualizing Model Training in PyTorch with TensorBoard

Accuracy and Loss curves for train and test in the TensorBoard dashboard.

Scheduling, Communication, and the Roofline Model

How work gets distributed across workers, why moving data costs more than computing it, and the one graph that shows whether your…

ONNX Explained: The Universal Bridge Between AI Training and Deployment

What is ONNX?

PyTorch nn Module: Layers, Activations, Loss Functions, and Optimizers

Contents

I Implemented VGG-19 From Scratch Before Building Neural Style Transfer

Table of Contents

Building Custom Audio Inference for Higgs Audio v3 TTS — A Hands-On Guide with Transformers

A practical walkthrough of how modern neural text-to-speech works under the hood — from 8 codebooks and delay patterns to autoregressive…

Why Your PyTorch Models Crash at Step 200: The Physics of Cumulative Memory Fragmentation

The most terrifying error message in deep learning isn’t an immediate, hard Out-Of-Memory (OOM) crash on step one.

The Silent $2,000+ OOM: Stop Guessing Your GPU Memory Overhead in Multi-Agent Loops

Why Your Local AI Agents Break at Step 200 (And How to Fix the Memory Cliff)