- Deep Learning 22: Diffusion Models (2)
- Deep Learning 21: Diffusion Models (1)
- Deep Learning 20: graph batching in PyTorch Geometric
- AAN.how: All About NLP —— New 2022 Release: over 24,000 resources and over 7,000 lecture notes!
- Deep Learning 19: Training MLM on any pre-trained BERT models
- Slideshare (11): Graph Neural Networks and Applications in NLP
- Slideshare (10): Reading notes bout Text Generation, BERT, Knowledge Distillation…
- Paddle Serving: model-as-a-service! Triggered by a single command line, deployment finishes in 10 minutes
- Slideshare (9): Transfer learning tutorial 2.0
- Slideshare (8): Graph Neural Network Paper reading notes
- Slideshare (7): NeurIPS 2019 Notes on NLP and GCNs
- Expanded AAN.how: Learning NLP Made Much Easier!
- Slideshare (6): Cross-lingual Paper reading notes
- Slideshare (5): Graph Neural Networks
- Prepare for the Interviews!
- Deep Learning 18: GANs with PyTorch
- Deep Learning 17: text classification with BERT using PyTorch
- Understanding Variational Graph Auto-Encoders
- Resources for BioNLP: datasets and tools
- Understanding Graph Convolutional Networks
- LectureBank: a dataset for NLP Education and Prerequisite Chain Learning
- ELMo in Practice
- Slideshare (4): A brief Introduction on Transfer Learning
- Transfer Learning Materials
- Slideshare (3): Unsupervised Transfer Learning Methods
- TensorFlow 08: save and restore a subset of variables
- To copy or not, that is the question: copying mechanism
- What matters: attention mechanism
- What’s next: seq2seq models
- Deep Learning 16: Understanding Capsule Nets
- Working with ROUGE 1.5.5 Evaluation Metric in Python
- Reinforcement Learning (1): Q-Learning basics
- Deep Learning 15: Unsupervised learning in DL? Try Autoencoder!
- Slideshare (2): Machine Learning Recap Slides sharing
- TensorFlow 07: Word Embeddings (2) – Loading Pre-trained Vectors
- Deep Learning 14 : Optimization, an Overview
- Deep Learning 13: Understanding Generative Adversarial Network
- Is your model good enough? Evaluation metrics in Classification and Regression
- Two sample problem(2): kernel function, feature space and reproducing kernel map
- Understanding SVM(2)
- Two sample problem(1): Parzen Windows, Maximum Mean Discrepancy
- NLP 05: From Word2vec to Doc2vec: a simple example with Gensim
- Deep Learning 12: Energy-Based Learning (2)–Regularization & Loss Functions
- Deep Learning 11: Energy-Based Learning (1)–What is EBL?
- TensorFlow 06: Word Embeddings (1)
- NLP 04: Log-Linear Models for Tagging Task (Python)
- TensorFlow 05: Understanding Basic Usage
- NLP 03: Finding Mr. Alignment, IBM Translation Model 1
- NLP 02: A Trigram Hidden Markov Model (Python)
- NLP 01: Language Modeling Problems
- PGM 02: Lots of Markov Family members: MC, PMN, CRF…
- PGM 01: Bayesian Networks
- Deep Learning 10: Sequence Modeling
- Deep Learning 09: Small Tricks(2)
- Deep Learning 08: Small Tricks(1)
- TensorFlow 04 : Implement a LeNet-5-like NN to classify notMNIST Images
- Lucky or not: Monte Carlo Method
- Deep Learning 07: Are you talking to a machine?
- Install MPI on Windows, Mac and Ubuntu
- Deep Learning 06: R-CNN for Object Detection
- TensorFlow 03: MNIST and CNN
- Deep Learning 05: Talk about Convolutional Neural Networks(CNN)
- TensorFlow 02: Play with MNIST and Google DL Udacity Lectures
- Python 03: find out intersection of two guest lists
- TensorFlow 01: multiple versions of numpy
- Life will be easier: intro on PCA
- Deep Learning Notes 1 – NN (Repost)
- Deep Learning 04, Slideshare (1): back from NIPS
- Graph Partitioning on Gibbs Sampling
- Python 02: interacts with Internet
- Cassandra Problems and VertexProgram Usage
- Titan: more about examples and confs
- Titan + Cassandra on Mac 10.9
- Random Forest: intro and an example
- Understanding SVM(1)
- TinkerpopGraph 02: Gremlin TinkerGraph: Set Properties Examples
- Deep Learning 03: about Training
- Tinkerpop3 GraphComputer: VertexPrograms
- Deep Learning 02: about RNN (Recurrent Neural Networks)
- Parallel Graph Coloring Algorithms and an Implementation of Jones-Plassmann
- Logistic Regression: a quick introduction
- TinkerpopGraph 01: Easy API usage, examples
- Parallel Gibbs Sampling and Neural Networks
- Gibbs Sampling: about Parallelization
- Gibbs Sampling: an easy Java Version on TinkerPop3
- Loopy BP: an easy implementation on Pregel Model
- MCMC:Gibbs Sampling
- Sampling Methods
- Deep Learning 01: Basis + NN on Handwritten Recognition