deep-learning - Provide.ai

ai, data-science, deep-learning, llm, Machine Learning

What Do You Need to Learn to be an AI Engineer in 2026? Where to Learn it? What to Build With it?

Youssef Hosni / March 25, 2026

In 2026, being an AI Engineer is not about knowing how to fine-tune a transformer or call an API. It is about building reliable systems…Continue reading on Towards AI »

ai, deep-learning, Machine Learning, pytorch, stable-diffusion

Why My PyTorch Diffusion Model Was Slow — and How I Made It 3× Faster

Rythmkumar / March 25, 2026

Training a Diffusion Model — even on a “simple” dataset like MNIST — is a trial by fire for your hardware. You expect the GPU to do the heavy lifting, but more often than not, your expensive silicon is sitting idle, waiting for a sluggish pipeline to t…

deep-learning, DeepSeek, deepseek-v3, expert routing, expert specialization, load balancing, Machine Learning, mixture of experts, moe, neural-networks, python, pytorch, swiglu, transformer, tutorial

DeepSeek-V3 from Scratch: Mixture of Experts (MoE)

Puneet Mangla / March 23, 2026

Table of Contents DeepSeek-V3 from Scratch: Mixture of Experts (MoE) The Scaling Challenge in Neural Networks Mixture of Experts (MoE): Mathematical Foundation and Routing Mechanism SwiGLU Activation in DeepSeek-V3: Improving MoE Non-Linearity Shared Expert in DeepSeek-V3: Universal Processing in MoE…

The post DeepSeek-V3 from Scratch: Mixture of Experts (MoE) appeared first on PyImageSearch.

Agentic AI, Artificial Intelligence, deep-learning, llm, neural-networks

Improving Deep Neural Learning Networks (Part 1): Practical Approaches and Applications to LLMs

Amelia Nguyen / March 22, 2026

From foundational Deep Learning training techniques to the algorithms powering modern Agentic AI.As you probably already know, Artificial Intelligence is becoming the new Internet, or the new electricity, as many people are saying. And of course, the f…

Artificial Intelligence, deep-learning, Machine Learning, python, reinforcement-learning

Using Reinforcement Learning to Solve Real-World Problems

Gulshan Yadav / March 22, 2026

I watched my 4-year-old nephew learn to ride a bicycle last summer. Nobody handed him a manual. Nobody showed him labeled training data of…Continue reading on Towards AI »

ai, ai-alignment, deep-learning, llm, Machine Learning

Chapter 3: Fine-Tuning for Alignment and Robustness

YUSUFF ADENIYI GIWA / March 19, 2026

Fine-Tuning for AlignmentContinue reading on Towards AI »

Artificial Intelligence, deep-learning, llm, Machine Learning, Technology

Chapter 2: The Efficiency Revolution: PEFT and Its Next Generation

YUSUFF ADENIYI GIWA / March 19, 2026

LoRA (Low-Rank Adaptation)Continue reading on Towards AI »

Artificial Intelligence, data-science, deep-learning, llm, neural-networks

The Algorithm That Cheats at Math (And Why That’s Genius)aka HNSW

DrSwarnenduAI / March 19, 2026

You Never Find the Closest Vector. And That’s the Whole Point.Continue reading on Towards AI »

attention mechanisms, deep-learning, deepseek-v3, kv cache optimization, large-language-models, mla, multi-head latent attention, pytorch, pytorch tutorial, RoPE, rotary positional embeddings, transformer architecture, transformers, tutorial

Build DeepSeek-V3: Multi-Head Latent Attention (MLA) Architecture

Puneet Mangla / March 16, 2026

Table of Contents Build DeepSeek-V3: Multi-Head Latent Attention (MLA) Architecture The KV Cache Memory Problem in DeepSeek-V3 Multi-Head Latent Attention (MLA): KV Cache Compression with Low-Rank Projections Query Compression and Rotary Positional Embeddings (RoPE) Integration Attention Computation with Multi-Head Latent…

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ai, deep-learning, llm, Machine Learning

New LLM Architecture Gallery

Sebastian Raschka, PhD / March 14, 2026

I put together a new LLM Architecture Gallery that collects the architecture figures from my recent comparison articles in one place, together with compact fact sheets and links.