# mlp

Published articles for mlp.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## MiniDXNN v0.4.0: Interactive neural texture compression on DirectX 12

DevFeed: [MiniDXNN v0.4.0: Interactive neural texture compression on DirectX 12](<https://devfeed.tech/articles/minidxnn-v0-4-0-interactive-neural-texture-compression-on-directx-12-15043.md>)

Original publisher: [Read original article](<https://gpuopen.com/learn/minidxnn-v040-interactive-neural-texture-compression/>)

Author: Takahiro Harada; Sho Ikeda

Published: 2026-08-13T14:30:00Z

Content type: release

Language: en

Sources: [AMD GPUOpen](<https://devfeed.tech/sources/amd-gpuopen.md>)

Topics: [Compression](<https://devfeed.tech/topics/compression.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [mlp](<https://devfeed.tech/topics/mlp.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [GUI](<https://devfeed.tech/topics/gui.md>), [shaders](<https://devfeed.tech/topics/shaders.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [agility-sdk](<https://devfeed.tech/tags/agility-sdk.md>), [announcement](<https://devfeed.tech/tags/announcement.md>), [compression](<https://devfeed.tech/tags/compression.md>), [directx](<https://devfeed.tech/tags/directx.md>), [driver](<https://devfeed.tech/tags/driver.md>), [getting-started](<https://devfeed.tech/tags/getting-started.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [gpu-open-sdks](<https://devfeed.tech/tags/gpu-open-sdks.md>), [gpu-open-tools](<https://devfeed.tech/tags/gpu-open-tools.md>), [gpuopen-sdks](<https://devfeed.tech/tags/gpuopen-sdks.md>), [gpuopen-tools](<https://devfeed.tech/tags/gpuopen-tools.md>), [graphics-apis](<https://devfeed.tech/tags/graphics-apis.md>), [gui](<https://devfeed.tech/tags/gui.md>), [inference](<https://devfeed.tech/tags/inference.md>), [maths](<https://devfeed.tech/tags/maths.md>), [memory](<https://devfeed.tech/tags/memory.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [microsoft-agility-sdk](<https://devfeed.tech/tags/microsoft-agility-sdk.md>), [microsoft-directx](<https://devfeed.tech/tags/microsoft-directx.md>), [ml](<https://devfeed.tech/tags/ml.md>), [mlp](<https://devfeed.tech/tags/mlp.md>), [neural](<https://devfeed.tech/tags/neural.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [product-release](<https://devfeed.tech/tags/product-release.md>), [quick-start](<https://devfeed.tech/tags/quick-start.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [shaders](<https://devfeed.tech/tags/shaders.md>), [technical-article](<https://devfeed.tech/tags/technical-article.md>), [technical-articles](<https://devfeed.tech/tags/technical-articles.md>), [tools](<https://devfeed.tech/tags/tools.md>)

### AI overview

MiniDXNN v0.4.0 is an open-source library for GPU-accelerated MLP inference and training on DirectX 12. The release adds D3D12 Linear Algebra support, input encoding for neural texture compression, and a real-time GUI application for training and visualizing texture representations.

### Source excerpt

MiniDXNN v0.4.0 introduces D3D12 Linear Algebra (SM 6.10) support, input encodings and neural texture compression, plus a real-time GUI app that trains and visualizes GPU-accelerated MLPs on DirectX® 12.

## Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP

DevFeed: [Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP](<https://devfeed.tech/articles/profiling-in-pytorch-part-2-from-nn-linear-to-a-fused-mlp-7522.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/torch-mlp-fusion>)

Author: Aritra Roy Gosthipaty; Rémi Ouazan Reboul; Sergio Paniego; Pedro Cuenca; Sayak Paul

Published: 2026-06-11T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [jobs](<https://devfeed.tech/topics/jobs.md>)

Tags: [cpu](<https://devfeed.tech/tags/cpu.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [jobs](<https://devfeed.tech/tags/jobs.md>), [kernels](<https://devfeed.tech/tags/kernels.md>), [liger](<https://devfeed.tech/tags/liger.md>), [mlp](<https://devfeed.tech/tags/mlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [profile](<https://devfeed.tech/tags/profile.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [torch](<https://devfeed.tech/tags/torch.md>), [traces](<https://devfeed.tech/tags/traces.md>)

### AI overview

A PyTorch profiling tutorial that moves from nn.Linear to a fused multilayer perceptron, explaining GPU kernels, CPU launch overhead, and tensor-transpose behavior in profiler traces.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Neural Networks Explained In Plain English

DevFeed: [Neural Networks Explained In Plain English](<https://devfeed.tech/articles/neural-networks-explained-in-plain-english-33575.md>)

Original publisher: [Read original article](<https://blog.algomaster.io/p/neural-networks-explained-in-plain>)

Author: Ashish Pratap Singh

Published: 2026-04-21T04:01:25Z

Content type: tutorial

Language: en

Sources: [AlgoMaster Newsletter](<https://devfeed.tech/sources/algomaster-newsletter.md>)

Topics: [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [AI Engineering](<https://devfeed.tech/topics/ai-engineering.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [ai-engineering](<https://devfeed.tech/tags/ai-engineering.md>), [applications](<https://devfeed.tech/tags/applications.md>), [concepts](<https://devfeed.tech/tags/concepts.md>), [function](<https://devfeed.tech/tags/function.md>), [mlp](<https://devfeed.tech/tags/mlp.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [neuron](<https://devfeed.tech/tags/neuron.md>), [understand](<https://devfeed.tech/tags/understand.md>)

### AI overview

A plain-English introduction to neural networks. It explains neurons or perceptrons, activation functions, weights and biases, and how stacking neurons into dense layers creates a multilayer perceptron (MLP) architecture used in AI systems.

### Source excerpt

All popular AI systems that you see today are made up of basic computing units called Neurons or Perceptrons. These connect to form a network called a Neural network that performs various complex computations.

## Diffusers welcomes FLUX-2

DevFeed: [Diffusers welcomes FLUX-2](<https://devfeed.tech/articles/diffusers-welcomes-flux-2-7202.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/flux-2>)

Author: YiYi Xu; Daniel Gu; Sayak Paul; Alvaro Somoza; Dhruv Nair; Aritra Roy Gosthipaty; Linoy Tsaban; Apolinário from multimodal AI art

Published: 2025-11-25T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [black-forest-labs](<https://devfeed.tech/tags/black-forest-labs.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [flux](<https://devfeed.tech/tags/flux.md>), [generation](<https://devfeed.tech/tags/generation.md>), [images](<https://devfeed.tech/tags/images.md>), [lora](<https://devfeed.tech/tags/lora.md>), [mlp](<https://devfeed.tech/tags/mlp.md>), [model](<https://devfeed.tech/tags/model.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [streams](<https://devfeed.tech/tags/streams.md>)

### AI overview

The article introduces FLUX.2, an image-generation and editing model that supports text-guided and image-guided generation with multiple reference images. It outlines changes to its text encoder and diffusion-transformer architecture relative to FLUX.1.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Visual Salamandra: Pushing the Boundaries of Multimodal Understanding

DevFeed: [Visual Salamandra: Pushing the Boundaries of Multimodal Understanding](<https://devfeed.tech/articles/visual-salamandra-pushing-the-boundaries-of-multimodal-understanding-6990.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/BSC-LT/visualsalamandra7b>)

Author: Iñigo Pikabea; Jaume Lozano

Published: 2025-04-11T14:21:56Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [multimodal-ai](<https://devfeed.tech/topics/multimodal-ai.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [mlp](<https://devfeed.tech/topics/mlp.md>), [AI Research](<https://devfeed.tech/topics/ai-research.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [blog](<https://devfeed.tech/tags/blog.md>), [blog-post](<https://devfeed.tech/tags/blog-post.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [grounding](<https://devfeed.tech/tags/grounding.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [language](<https://devfeed.tech/tags/language.md>), [llm](<https://devfeed.tech/tags/llm.md>), [mlp](<https://devfeed.tech/tags/mlp.md>), [models](<https://devfeed.tech/tags/models.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [multimodal-ai](<https://devfeed.tech/tags/multimodal-ai.md>), [ocr](<https://devfeed.tech/tags/ocr.md>), [research](<https://devfeed.tech/tags/research.md>), [training](<https://devfeed.tech/tags/training.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vqa](<https://devfeed.tech/tags/vqa.md>)

### AI overview

Visual Salamandra is a multilingual multimodal model built by extending the Salamandra Instructed 7B model with Google's SigLIP image encoder, an MLP projector, and late-fusion techniques. It processes text, images, and videos, with training focused on visual grounding, document understanding, mathematical reasoning, OCR, and European-language coverage.

### Source excerpt

A Blog post by Language Technologies Laboratory @ Barcelona Supercomputing Center on Hugging Face