Tag: #deep-learning
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 77 posts
77 posts are tagged #deep-learning; the most recent was published on 2026-07-17.
Often tagged together: #ai 38 #2026-03 27 #reinforcement-learning 21 #ai-papers 14 #transformer 12
Most read in the last 90 days:
- HunyuanVideo and LTX-2 Complete Analysis: Architecture, Performance, and Practical Guide to Open-Source Video Generation Models
- The Complete Guide to vLLM & Ollama: LLM Serving Engine Setup, Parameters, and Environment Variables
- Wan Text-to-Video/Image-to-Video and Z Image Turbo Complete Analysis: Architecture and Applications of Next-Gen Video/Image Generation Models
torchcomms in PyTorch 2.13 — The New Distributed Communication Backend Coming for c10d
Among the highlights of PyTorch 2.13, released on July 8, 2026, the most structural change is torchcomms — PyTorch Distributed's new communication backend has begun landing in core's CI and DeviceMesh paths. torchcomms i
2026-07-17 · 10 min read #pytorch#distributed-training#nccl#deep-learningMultimodal AI Training Methods — Many Senses in One Model
A walkthrough of how multimodal AI learns to handle images, text, audio, and video in a single model. We cover modality alignment and contrastive learning, fusion strategies, shared embedding spaces, pretraining and fine
2026-06-26 · 15 min read #ai-papers#multimodal#clip#vision-language#contrastive-learningBuilding an LLM from Scratch — A Stanford CS336 Style Learning Roadmap
Stanford CS336 (Language Modeling from Scratch) keeps hitting the Hacker News front page, and from-scratch LLM building is hot again. This post dissects the full curriculum — tokenizers, attention, distributed training,
2026-06-12 · 15 min read #llm#transformer#cs336#deep-learning#tokenizerDiffusion Models Deep Dive — DDPM, Latent Diffusion, Classifier-Free Guidance, DDIM, Stable Diffusion Complete Guide (2025) ♪ Listenable
Diffusion models — the foundation of Stable Diffusion, DALL-E, Midjourney, and Sora. This post dissects diffusion models from scratch. Forward/Reverse diffusion process, DDPM and the variational lower bound, the score-ba
2026-04-15 · 25 min read #diffusion#generative-ai#stable-diffusion#ddpm#machine-learning[Deep RL] 20. Deep RL Summary: Algorithm Comparison and Selection Guide
Systematic comparison and selection criteria for deep RL algorithms: value-based, policy-based, Actor-Critic, and model-based approaches
2026-03-19 · 9 min read #reinforcement-learning#deep-learning#ai[Deep RL] 17. Model-Based Reinforcement Learning: Imagination-Augmented Agent
Principles of model-based RL and I2A (Imagination-Augmented Agent): learning environment models and planning through imagination
2026-03-19 · 8 min read #reinforcement-learning#deep-learning#ai[Deep RL] 14. Continuous Action Spaces: DDPG and Distributional Policies
Methods for handling continuous action spaces: A2C extension, DDPG deterministic policy gradient, and distributional policy gradient
2026-03-19 · 7 min read #reinforcement-learning#deep-learning#ai[Deep RL] 11. A3C: Asynchronous Advantage Actor-Critic
How A3C solves the correlation problem of A2C through asynchronous parallel learning: data parallelism and gradient parallelism implementation
2026-03-19 · 7 min read #reinforcement-learning#deep-learning#ai[Deep RL] 08. Building a Stock Trading Agent with Reinforcement Learning ♪ Listenable
Define stock trading as a reinforcement learning problem, design a custom trading environment, and train trading agents using feedforward and CNN models.
2026-03-19 · 19 min read #reinforcement-learning#deep-learning#ai#finance[Deep RL] 05. Bellman Equation and Value Iteration
Understand the concepts of state value and action value, and apply value iteration and Q-learning to FrozenLake based on the Bellman optimality equation.
2026-03-19 · 9 min read #reinforcement-learning#deep-learning#ai[Deep RL] 02. Getting Started with Reinforcement Learning Using OpenAI Gym
Understand the structure and API of OpenAI Gym, and practice with random agents and wrappers in the CartPole environment.
2026-03-19 · 10 min read #reinforcement-learning#deep-learning#ai[Deep RL] 18. AlphaGo Zero: AI That Learns by Playing Itself
Core principles of AlphaGo Zero: MCTS and self-play, plus implementing a Connect4 bot
2026-03-19 · 9 min read #reinforcement-learning#deep-learning#ai[Deep RL] 15. Trust Region Methods: TRPO, PPO, ACKTR
Trust region techniques for stable policy updates: PPO clipping objective, TRPO constrained optimization, and ACKTR
2026-03-19 · 8 min read #reinforcement-learning#deep-learning#ai[Deep RL] 12. Training Chatbots with Reinforcement Learning
Combining Seq2Seq models with reinforcement learning for chatbot training: from RNN basics to Self-Critical Sequence Training (SCST)
2026-03-19 · 7 min read #reinforcement-learning#deep-learning#ai#nlp[Deep RL] 09. Policy Gradient: Policy-Based Reinforcement Learning
Understand the difference between value-based and policy-based methods, and apply the mathematical derivation of Policy Gradient and the REINFORCE algorithm to CartPole and Pong.
2026-03-19 · 6 min read #reinforcement-learning#deep-learning#ai#policy-gradient[Deep RL] 06. Deep Q-Network: Principles and Implementation of DQN
Understand the core ideas of DQN (experience replay, target network) that overcome the limitations of table-based Q-learning, and implement it in the Pong environment.
2026-03-19 · 10 min read #reinforcement-learning#deep-learning#ai#dqn[Deep RL] 03. PyTorch Deep Learning Basics: From Tensors to Neural Networks ♪ Listenable
Learn PyTorch tensor operations, automatic differentiation, and neural network components, then implement TensorBoard monitoring and a GAN example using Atari images.
2026-03-19 · 20 min read #reinforcement-learning#deep-learning#ai#pytorch[Deep RL] 19. Practical Applications of Deep Reinforcement Learning
Diverse real-world applications of deep RL: robot control, autonomous driving, resource management, recommendation systems, NLP, and game AI
2026-03-19 · 7 min read #reinforcement-learning#deep-learning#ai[Deep RL] 16. Black-Box Optimization: Evolution Strategies and Genetic Algorithms
Optimizing policies without gradients: principles and implementation of Evolution Strategies (ES) and Genetic Algorithms (GA)
2026-03-19 · 8 min read #reinforcement-learning#deep-learning#ai[Deep RL] 13. Web Navigation and Reinforcement Learning
Combining browser automation with reinforcement learning: implementing web agents using the Mini World of Bits benchmark and OpenAI Universe
2026-03-19 · 8 min read #reinforcement-learning#deep-learning#ai