Advancing Vapor Pressure Prediction: A Machine Learning Approach with Directed Message Passing Neural Networks
12 December 2024 Advancing Vapor Pressure Prediction: A Machine Learning Approach with Directed Message Passing Neural Networks Yen-Hsiang Lin1, Hsin-Hao Liang1, Shiang-Tai Lin1, Yi-Pei Li1 1. National Taiwan University
記事を読む →Hybrid Neural Networks for Improved Chemical
1 Hybrid Neural Networks for Improved Chemical Process Modeling: Bridging Data-Driven Insights with Physical Consistency Jana Mousa *, Stéphane Negny, Rachid Ouaret. Laboratoire de Génie Chimique, Université de Toulouse,
記事を読む →Leveraging spatial charge descriptor in deep learning models: Toward highly accurate prediction of vapor-liquid equilibrium
Leveraging spatial charge descriptor in deep learning models: Toward highly accurate prediction of vapor-liquid equilibrium Hsiu-Min Hung , Ying-Chieh Hung * Department of Chemical Engineering and Biotechnology, National
記事を読む →00 overview
OVERVIEW Machine Learning & Deep Learning A depth-first curriculum, from math foundations to production Phase goal Turn “I can call .fit()” into “I understand, can implement, and can ship. ” This guide is the map for the
記事を読む →1 1 Regression
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記事を読む →1 2 Bayes
P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H)BAYESIAN INFE
記事を読む →1 3 Tree Based Methods
xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? TREE-BASED METHODS FROM BEGINNER TO EXPERT A complete, visual, application-driven course — from a single decision tree through ran- dom forests and
記事を読む →1 4 Trees
xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? TREE-BASED METHODS FROM BEGINNER TO EXPERT A complete, visual, application-driven course — from a single decision tree through ran- dom forests and
記事を読む →1 6 Support Vector Machines
w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b SUPPORT VECTOR MACHINES FROM BEGINNER TO EXPERT A complete, visual, application-driven course — from the maximal-margin idea through soft margins, the kerne
記事を読む →Calculus
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記事を読む →Linear Algebra
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記事を読む →ML DL Curriculum complete
OVERVIEW Machine Learning & Deep Learning A depth-first curriculum, from math foundations to production Phase goal Turn “I can call .fit()” into “I understand, can implement, and can ship. ” This guide is the map for the
記事を読む →Probability & Statistics
P (A | B) µ, σ2 E[X] P (A | B) µ, σ2 E[X] P (A | B) µ, σ2 E[X] P (A | B) µ, σ2 E[X] P (A | B) µ, σ2 E[X] P (A | B) µ, σ2 E[X] P (A | B) µ, σ2 E[X] P (A | B) µ, σ2 E[X] P (A | B) µ, σ2 E[X] PROBABILITY & STATISTICS FROM B
記事を読む →bayes
P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H)BAYESIAN INFE
記事を読む →linear algebra
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記事を読む →phase0 math tooling
PHASE 0 Math & Tooling Foundations Linear algebra, calculus, probability, statistics, and the scientific Python stack Phase goal Build the mathematical intuition that makes every later algorithm click instead of feeling
記事を読む →phase1 core ml
PHASE 1 Core Machine Learning Classical models, evaluation, feature engineering, and tuning — implemented, not just imported Phase goal Understand classical ML deeply enough to implement the key algorithms from scratch a
記事を読む →phase2 dl foundations
PHASE 2 Deep Learning Foundations Neurons, backpropagation, optimization, regularization, and PyTorch — from first prin- ciples Phase goal Understand neural networks from the ground up — forward pass, backpropagation, op
記事を読む →phase3 computer vision
PHASE 3 Computer Vision Convolutions, CNN architectures, transfer learning, detection, segmentation, and ViTs Phase goal Understand how neural networks see: the convolution operation, the architectural lineage from LeNet
記事を読む →phase4 nlp transformers
PHASE 4 Sequence Models, NLP & Transform-ers From RNNs to attention to LLMs, tokenization, fine-tuning, and RAG Phase goal Master the architecture behind modern AI. Build a Transformer from first princi- ples, understand
記事を読む →phase5 generative models
PHASE 5 Generative Models Autoencoders, VAEs, GANs, and diffusion — how machines create Phase goal Understand how models learn to generate data: the latent-variable view (V AEs), the adversarial game (GANs), and the deno
記事を読む →phase6 reinforcement learning
PHASE 6 Reinforcement Learning MDPs, value functions, Q-learning, policy gradients, PPO, and RLHF Phase goal Understand learning by interaction and reward: Markov decision processes, the value/policy duality, deep Q-netw
記事を読む →phase7 mlops deployment
PHASE 7 MLOps, Deployment & Production Systems Experiment tracking, serving, containers, monitoring, optimization, and CI/CD for ML Phase goal Turn models into reliable products. Track experiments, package and serve mode
記事を読む →phase8 capstones
PHASE 8 Capstone Projects Compounding value by building end-to-end systems in your own domains Phase goal Convert knowledge into demonstrable capability. Ship 2–3 end-to-end projects that intersect your existing domains
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