Machine Learning II

Keywords

ver. 1.0.0

Practical foundations and applied techniques across modern deep learning: supervised nets and vision (classification/detection), efficient architectures, transformers/LLMs, generative models, and reinforcement learning.

A compact, practical guide to modern deep learning covering: supervised learning foundations and neural network design; convolutional architectures and object detection; techniques for training very deep and efficient models; end-to-end transformer/LLM implementation and fine-tuning; core generative models (VAEs, diffusion, CLIP) and guidance methods for controllable image synthesis; and reinforcement learning principles and applied deep‑RL methods including RLHF and self‑play.

Full summary

This node synthesizes practical foundations, architectures, training techniques, and deployment considerations across the major areas of contemporary deep learning.

Supervised learning & neural nets: Principles of loss design, backpropagation and gradient-based optimizers (SGD, momentum, Adam), initialization and training diagnostics, regularization and generalization (bias–variance, data augmentation, dropout, early stopping, ensembling, transfer learning), and guidance on parameter counting and architecture choices. Covers linear/ReLU families and the role of depth versus width.
Convolutional vision models & detectors: Evolution of CNN classifiers (AlexNet → VGG/Inception → ResNet → compact nets) and practical design/training/evaluation tools. Core convolutional modules (convolutions, receptive field, pooling/upsampling, 1×1 convs, residual/dense connections, batch norm) and architectures such as ResNet, DenseNet, and U‑Net. Object detection families from region‑based detectors (R‑CNN → Fast/Faster R‑CNN) to single‑shot detectors (YOLO, SSD), multi‑scale and feature‑pyramid refinements, and the trade-offs between depth, parameter/compute cost, speed, and multi‑scale accuracy.
Scaling and efficiency: Techniques for training very deep networks (residual and dense connections), and building efficient models for constrained environments (SqueezeNet, MobileNet, width/resolution multipliers) with practical guidance on choosing trade-offs for deployment.
Transformers and LLMs: Why transformers suit text, tokenization into embeddings, self‑attention and transformer blocks, and a hands‑on path to implement causal multi‑head attention and a GPT‑style model (training, sampling, checkpointing). Converting pretrained models for classification and performing instruction‑tuning to produce assistants, including data preparation, decoding strategies, and evaluation.
Generative modeling & multimodal guidance: Core techniques — VAEs for latent modeling, diffusion models for high‑quality image synthesis (noise‑prediction losses, ELBO perspective, accelerated sampling, conditioning), and CLIP for image–text contrastive embeddings enabling zero‑shot and guided generation. Guidance techniques (classifier, classifier‑free, CLIP guidance), inpainting, cascaded super‑resolution, evaluation metrics, typical failure modes, and deployment/safety considerations.
Reinforcement learning foundations & modern practice: MDPs and the agent–environment loop, fundamental challenges (credit assignment, exploration, instability, reward design), algorithm classes (value, policy, model-based), and canonical methods (TD, Q‑learning, SARSA, TD(λ), policy gradients). Deep‑RL stability tools (replay buffers, target networks, GAE, trust‑region/PPO), distributed training/inference considerations, and applied topics such as supervised fine‑tuning, RLHF/RLVR, GRPO, and self‑play/MCTS for games.

Together, these topics form an integrated, hands‑on toolkit for building, training, evaluating, and deploying state‑of‑the‑art models across vision, language, generative modeling, and reinforcement learning, emphasizing practical recipes, architectural choices, trade‑offs, and common pitfalls.

Modules

Basics to Machine Learning

Foundations and practical techniques for building, training, and deploying deep neural networks for vision and general supervised tasks

Computer Vision

How convolutional image classifiers and object detectors evolved, how to design and train them, and how to choose trade-offs between depth, size, speed and accuracy

Language Models

End-to-end practical guide: how transformers work, how to build/train/finetune GPT-style LLMs, and how to apply them to tasks like classification and instruction following

Generative Models

Core modern generative modeling: VAEs and diffusion models, plus CLIP and guidance techniques for high-quality, controllable image generation

Reinforcement Learning

Comprehensive RL foundations and practical methods from classical value/policy/model algorithms to deep-RL stabilization and modern applications in LLMs, RLHF, and self-play.

--- title: "Machine Learning II" keywords: - ver. 1.0.0 --- **Practical foundations and applied techniques across modern deep learning: supervised nets and vision (classification/detection), efficient architectures, transformers/LLMs, generative models, and reinforcement learning.** > A compact, practical guide to modern deep learning covering: supervised learning foundations and neural network design; convolutional architectures and object detection; techniques for training very deep and efficient models; end-to-end transformer/LLM implementation and fine-tuning; core generative models (VAEs, diffusion, CLIP) and guidance methods for controllable image synthesis; and reinforcement learning principles and applied deep‑RL methods including RLHF and self‑play. ::: {.callout-note collapse="true" title="Full summary"} This node synthesizes practical foundations, architectures, training techniques, and deployment considerations across the major areas of contemporary deep learning. - Supervised learning & neural nets: Principles of loss design, backpropagation and gradient-based optimizers (SGD, momentum, Adam), initialization and training diagnostics, regularization and generalization (bias–variance, data augmentation, dropout, early stopping, ensembling, transfer learning), and guidance on parameter counting and architecture choices. Covers linear/ReLU families and the role of depth versus width. - Convolutional vision models & detectors: Evolution of CNN classifiers (AlexNet → VGG/Inception → ResNet → compact nets) and practical design/training/evaluation tools. Core convolutional modules (convolutions, receptive field, pooling/upsampling, 1×1 convs, residual/dense connections, batch norm) and architectures such as ResNet, DenseNet, and U‑Net. Object detection families from region‑based detectors (R‑CNN → Fast/Faster R‑CNN) to single‑shot detectors (YOLO, SSD), multi‑scale and feature‑pyramid refinements, and the trade-offs between depth, parameter/compute cost, speed, and multi‑scale accuracy. - Scaling and efficiency: Techniques for training very deep networks (residual and dense connections), and building efficient models for constrained environments (SqueezeNet, MobileNet, width/resolution multipliers) with practical guidance on choosing trade-offs for deployment. - Transformers and LLMs: Why transformers suit text, tokenization into embeddings, self‑attention and transformer blocks, and a hands‑on path to implement causal multi‑head attention and a GPT‑style model (training, sampling, checkpointing). Converting pretrained models for classification and performing instruction‑tuning to produce assistants, including data preparation, decoding strategies, and evaluation. - Generative modeling & multimodal guidance: Core techniques — VAEs for latent modeling, diffusion models for high‑quality image synthesis (noise‑prediction losses, ELBO perspective, accelerated sampling, conditioning), and CLIP for image–text contrastive embeddings enabling zero‑shot and guided generation. Guidance techniques (classifier, classifier‑free, CLIP guidance), inpainting, cascaded super‑resolution, evaluation metrics, typical failure modes, and deployment/safety considerations. - Reinforcement learning foundations & modern practice: MDPs and the agent–environment loop, fundamental challenges (credit assignment, exploration, instability, reward design), algorithm classes (value, policy, model-based), and canonical methods (TD, Q‑learning, SARSA, TD(λ), policy gradients). Deep‑RL stability tools (replay buffers, target networks, GAE, trust‑region/PPO), distributed training/inference considerations, and applied topics such as supervised fine‑tuning, RLHF/RLVR, GRPO, and self‑play/MCTS for games. Together, these topics form an integrated, hands‑on toolkit for building, training, evaluating, and deploying state‑of‑the‑art models across vision, language, generative modeling, and reinforcement learning, emphasizing practical recipes, architectural choices, trade‑offs, and common pitfalls. ::: ## Modules ### [Basics to Machine Learning](basics_to_machine_learning/index.qmd) **Foundations and practical techniques for building, training, and deploying deep neural networks for vision and general supervised tasks** - [Preliminaries to Machine Learning](basics_to_machine_learning/preliminaries_to_machine_learning/index.qmd) - [Training Models](basics_to_machine_learning/training_models/index.qmd) - [Convnets](basics_to_machine_learning/convnets/index.qmd) ### [Computer Vision](computer_vision/index.qmd) **How convolutional image classifiers and object detectors evolved, how to design and train them, and how to choose trade-offs between depth, size, speed and accuracy** - [Residual Convolutional Networks](computer_vision/residual_convolutional_networks/index.qmd) - [Early Object Detection Networks](computer_vision/early_object_detection_networks/index.qmd) - [Fast Object Detection Networks](computer_vision/fast_object_detection_networks/index.qmd) ### [Language Models](language_models/index.qmd) **End-to-end practical guide: how transformers work, how to build/train/finetune GPT-style LLMs, and how to apply them to tasks like classification and instruction following** - [Chapter 12: Transformers](language_models/chapter_12_transformers/index.qmd) - [1 Understanding large language models](language_models/1_understanding_large_language_models/index.qmd) - [2 Working with text data](language_models/2_working_with_text_data/index.qmd) - [3 Coding attention mechanisms](language_models/3_coding_attention_mechanisms/index.qmd) - [4 Implementing a GPT model from scratch to generate text](language_models/4_implementing_a_gpt_model_from_scratch_to_generate_text/index.qmd) - [5 Pretraining on unlabeled data](language_models/5_pretraining_on_unlabeled_data/index.qmd) - [6 Fine-tuning for classification](language_models/6_fine_tuning_for_classification/index.qmd) - [7 Fine-tuning to follow instructions](language_models/7_fine_tuning_to_follow_instructions/index.qmd) ### [Generative Models](generative_models/index.qmd) **Core modern generative modeling: VAEs and diffusion models, plus CLIP and guidance techniques for high-quality, controllable image generation** - [Chapter 17: Variational autoencoders](generative_models/chapter_17_variational_autoencoders/index.qmd) - [Chapter 18: Diffusion models](generative_models/chapter_18_diffusion_models/index.qmd) - [2021 Clip](generative_models/2021_clip/index.qmd) - [CLIP Guided Image Diffusion](generative_models/clip_guided_image_diffusion/index.qmd) - [2022 Classifier Free Diffusion Guidance](generative_models/2022_classifier_free_diffusion_guidance/index.qmd) ### [Reinforcement Learning](reinforcement_learning/index.qmd) **Comprehensive RL foundations and practical methods from classical value/policy/model algorithms to deep-RL stabilization and modern applications in LLMs, RLHF, and self-play.** - [I Foundations](reinforcement_learning/i_foundations/index.qmd) - [II Diving deeper](reinforcement_learning/ii_diving_deeper/index.qmd) - [III RL at scale](reinforcement_learning/iii_rl_at_scale/index.qmd)