Tung Thanh Le's repositories
HeteroArchGen4M2S
HeteroArchGen4M2S: An automatic software for configuring and running heterogeneous CPU-GPU architectures on Multi2Sim simulator. This tool is built on top of M2S simulator, it allows us to configure various heterogeneous CPU-GPU architectures (e.g., number of CPU cores, GPU cores, L1$, L2$, memory (size and latency (via CACTI 6.5)), network topologies (currently support 2D-Mesh, customized 2D-Mesh, and Torus networks)...). The output files include the results of network throughput and latency, caches/memory access time, and dynamic power of the cores (can be collected after running McPAT).
annotated_deep_learning_paper_implementations
🧑🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, etc. 🧠
Awsome-Deep-Learning-for-Video-Analysis
Papers, code and datasets about deep learning and multi-modal learning for video analysis
channel-attribution-model
An attention-based Recurrent Neural Net multi-touch attribution model in a supervised learning fashion of predicting if a series of events leads to conversion (purchase). The trained model can also assign credits to channels. The model also incorporates user-context information, such as user demographics and behavior, as control variables to reduce the estimation biases of media effects.
coursera-natural-language-processing-specialization
Programming assignments from all courses in the Coursera Natural Language Processing Specialization offered by deeplearning.ai.
deep-reinforcement-learning
Repo for the Deep Reinforcement Learning Nanodegree program
EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
feature_engine
Feature engineering package with sklearn like functionality
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
learning-spark
Example code from Learning Spark book
practical-statistics-for-data-scientists
Code repository for O'Reilly book