Tag: #deep-learning

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• 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-learning

• Multimodal 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-learning

• Building 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#tokenizer

• Diffusion Models Deep Dive — DDPM, Latent Diffusion, Classifier-Free Guidance, DDIM, Stable Diffusion Complete Guide (2025)

  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

  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

  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