Mike Hung
Mike Hung
@mikehungtuo・Data Scientist・Taiwan
Applied Mathematics to Deep Learning and Machine Learning. Never stop learning new and cool things.
Understand torch.scatter
Understand torch.scatter
Cheat Sheets for Machine Learning and Data Science
Cheat Sheets for Machine Learning and Data Science
What frustrates Data Scientists in Machine Learning projects?
What frustrates Data Scientists in Machine Learning projects?
My bloody experience that worked for an AI startup.
Python Data Science Handbook | Python Data Science Handbook
Python Data Science Handbook | Python Data Science Handbook
Free Book
An Introduction to VisiData — An Introduction to VisiData
An Introduction to VisiData — An Introduction to VisiData
r/datascience - Imposter syndrome can really feel overwhelming.
r/datascience - Imposter syndrome can really feel overwhelming.
63 votes and 16 comments so far on Reddit
Data in Wonderland
Data in Wonderland
Explores communication with data in various forms through seminal and cutting-edge ideas in writing, data analyses, and visualzation.
PyTorch Internals (how pytorch works from the inside)
PyTorch Internals (how pytorch works from the inside)
PyTorch Internals (how pytorch works from the inside)
Introduction to GPUs: CUDA
Introduction to GPUs: CUDA
How to fine tune VERY large model if it doesn’t fit on your GPU
How to fine tune VERY large model if it doesn’t fit on your GPU
Memory-efficient techniques to defeat the problem of “CUDA memory error..” during training
GitHub - cybertronai/gradient-checkpointing: Make huge neural nets fit in memory
GitHub - cybertronai/gradient-checkpointing: Make huge neural nets fit in memory
Make huge neural nets fit in memory. Contribute to cybertronai/gradient-checkpointing development by creating an account on GitHub.
Gradient Accumulation in PyTorch
Gradient Accumulation in PyTorch
Increasing batch size to overcome memory constraints
Python for Data Analysis, 3E
Python for Data Analysis, 3E
Free Book
Fundamentals of Data Visualization
Fundamentals of Data Visualization
Free Book
Machine Learning Operations (MLOps): Overview, Definition, and Architecture
Machine Learning Operations (MLOps): Overview, Definition, and Architecture
The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail to deliver on their expectations. The paradigm of Machine Lea…
MLU-Explain
MLU-Explain
Visual explanations of core machine learning concepts.
GitHub - mryab/efficient-dl-systems: Efficient Deep Learning Systems course materials (HSE, YSDA)
GitHub - mryab/efficient-dl-systems: Efficient Deep Learning Systems course materials (HSE, YSDA)
Efficient Deep Learning Systems course materials (HSE, YSDA) - GitHub - mryab/efficient-dl-systems: Efficient Deep Learning Systems course materials (HSE, YSDA)
OpenML
OpenML
OpenML is an open platform for sharing datasets, algorithms, and experiments - to learn how to learn better, together.
Hugging Face – The AI community building the future.
Hugging Face – The AI community building the future.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Inference Optimization for Convolutional Neural Networks
Inference Optimization for Convolutional Neural Networks
Quantization and fusion for faster inference
Build your open-source MLOps stack | MyMLOps
Build your open-source MLOps stack | MyMLOps
Explore 50+ most popular open-source MLOps tools. Build your own stack based on our template.
Drivendata
Drivendata
Drivendata
Part 1: Key Concepts in RL — Spinning Up documentation
Part 1: Key Concepts in RL — Spinning Up documentation
Introduction to Machine Learning Interviews Book · MLIB
Introduction to Machine Learning Interviews Book · MLIB
Neural Fields: Home
Neural Fields: Home
paulbridger.com
paulbridger.com
Deep dive machine learning articles with a focus on solving the hard problems in production engineering.
Learnings from Google’s comprehensive research into activation functions
Learnings from Google’s comprehensive research into activation functions
This is a field that is heating up. Keep your eyes out on it
14.4. Anchor Boxes — Dive into Deep Learning 1.0.0-alpha1.post0 documentation
14.4. Anchor Boxes — Dive into Deep Learning 1.0.0-alpha1.post0 documentation
Interpreting the Latent Space of GANs for Semantic Face Editing
Interpreting the Latent Space of GANs for Semantic Face Editing
Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity image synthesis, there lacks enough understanding of how GANs are able to map a latent code sampled from a random distribution to a photo-realistic image. Previous work assumes the latent space learned by GANs foll…
The Principles of Deep Learning Theory
The Principles of Deep Learning Theory
This book develops an effective theory approach to understanding deep neural networks of practical relevance. Beginning from a first-principles component-level picture of networks, we explain how to determine an accurate description of the output of trained networks by solving layer-to-layer iterati…
You Only Learn One Representation: Unified Network for Multiple Tasks
You Only Learn One Representation: Unified Network for Multiple Tasks
People ``understand″ the world via vision, hearing, tactile, and also the past experience. Human experience can be learned through normal learning (we call it explicit knowledge), or subconsciously (we call it implicit knowledge). These experiences learned through normal learning or subconsciously…
Few-Shot Forecasting of Time-Series with Heterogeneous Channels
Few-Shot Forecasting of Time-Series with Heterogeneous Channels
Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set. Leveraging learning experience with similar datasets is a well-established technique for classification problems called few-shot classification. Howe…
Information Theory Basic for Machine Learning & Deep Learning
Information Theory Basic for Machine Learning & Deep Learning
Information theory
Efficient Transformers: A Survey
Efficient Transformers: A Survey
Transformer model architectures have garnered immense interest lately due to their effectiveness across a range of domains like language, vision and reinforcement learning. In the field of natural language processing for example, Transformers have become an indispensable staple in the modern deep le…
Generalized Out-of-Distribution Detection: A Survey
Generalized Out-of-Distribution Detection: A Survey
Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never…
VOS: Learning What You Don’t Know by Virtual Outlier Synthesis
VOS: Learning What You Don’t Know by Virtual Outlier Synthesis
Out-of-distribution (OOD) detection has received much attention lately due to its importance in the safe deployment of neural networks. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on OOD data. Previous ap…
Model Assertions for Monitoring and Improving ML Models
Model Assertions for Monitoring and Improving ML Models
ML models are increasingly deployed in settings with real world interactions such as vehicles, but unfortunately, these models can fail in systematic ways. To prevent errors, ML engineering teams monitor and continuously improve these models. We propose a new abstraction, model assertions, that adap…
Bounding Box Regression with Uncertainty for Accurate Object Detection
Bounding Box Regression with Uncertainty for Accurate Object Detection
Large-scale object detection datasets (e.g., MS-COCO) try to define the ground truth bounding boxes as clear as possible. However, we observe that ambiguities are still introduced when labeling the bounding boxes. In this paper, we propose a novel bounding box regression loss for learning bounding b…
Masked Visual Pre-training for Motor Control
Masked Visual Pre-training for Motor Control
Self-supervised visual pre-training from real-world images is effective for learning motor control tasks from pixels.
I Deep Faked Myself In Every Meeting For A Whole Week
I Deep Faked Myself In Every Meeting For A Whole Week
Not even one person noticed.
The Next Big Challenge for Data is Organizational - Locally Optimistic
The Next Big Challenge for Data is Organizational - Locally Optimistic
We have the tools we need to build the data world we want to live in. Now we just need a little organization. It’s time to bring the assembly line to data.
Data team structure: embedded or centralised?
Data team structure: embedded or centralised?
Embedded data teams are closer to business problems but ownership of what to do when data goes wrong is complicated
The Secret of Delivering Machine Learning to Production
The Secret of Delivering Machine Learning to Production
The vast majority of Machine Learning (ML) projects FAIL.
Home - Made With ML
Home - Made With ML
Learn how to responsibly deliver value with ML.
CS 329S: Machine Learning Systems Design
CS 329S: Machine Learning Systems Design
What I’ve learned about documentation for data teams
What I’ve learned about documentation for data teams
I used to be a hypocrite. There are numerous records of me in Slack complaining about other people’s lack of documentation. It would not be hard to track down the time I was venting furiously to a colleague about the non-existing API docs for a tool I was working to build out analytics for on a tigh…
Andrej Karpathy
Andrej Karpathy
Blog of Sr. Director of AI at Tesla
MLOps Is a Mess But That’s to be Expected
MLOps Is a Mess But That’s to be Expected
I discuss the messy state of MLOps today and how we are still in the early phases of a broader transformation to bring machine learning value to enterprises globally.
Labeling and Crowdsourcing - Data-centric AI Resource Hub
Labeling and Crowdsourcing - Data-centric AI Resource Hub
Asking someone to perform an annotation task, such as labeling an image with text or classifying it into a certain category, may seem simple. However, the huge number of different interpretations of the task makes it difficult for machine learning practitioners to effectively source new training dat…
Why 85% of Machine Learning Projects Fail - How to Avoid This – IIoT World
Why 85% of Machine Learning Projects Fail - How to Avoid This – IIoT World
According to Gartner, 85% of Machine Learning (ML) projects fail. Worse yet, the research company predicts that this trend will continue through 2022. Does this point to some weakness in ML itself? No, it points to weaknesses in the way […]
[ 李宏毅教授演講 ] 今天的人工智慧 其實沒有你想的那麼厲害
[ 李宏毅教授演講 ] 今天的人工智慧 其實沒有你想的那麼厲害
labml.ai Annotated PyTorch Paper Implementations
labml.ai Annotated PyTorch Paper Implementations
Machine Learning Papers
Machine Learning Papers
Find latest and trending machine learning papers
Aman’s AI Journal • Papers List
Aman’s AI Journal • Papers List
Aman’s AI Journal | Course notes and learning material for Artificial Intelligence and Deep Learning Stanford classes.
Deep Learning Monitor - Find new Arxiv papers, tweets and Reddit posts for you
Deep Learning Monitor - Find new Arxiv papers, tweets and Reddit posts for you
Things happening in deep learning: arxiv, twitter, reddit
Table of Contents · Crafting Interpreters
Table of Contents · Crafting Interpreters
Table of Contents · Game Programming Patterns
Table of Contents · Game Programming Patterns
GitHub - amaargiru/pyroad: Detailed Python developer roadmap
GitHub - amaargiru/pyroad: Detailed Python developer roadmap
Linux perf Examples
Linux perf Examples
Top 10 Things That Destroy Developer Productivity
Top 10 Things That Destroy Developer Productivity
Productivity is core to both individual and team success. Software engineers cannot grow in their careers if they are not productive. It does not matter how ...
Refactoring: clean your code
Refactoring: clean your code
Refactoring is the controllable process of systematically improving your code without writing new functionality. The goal of refactoring is to pay off technical debt. The mantra of refactoring is clean code and simple design.
Multiprocessing using Pool in Python
Multiprocessing using Pool in Python
Learn about Pool class in Python, and how it is used to perform multiprocessing in Python.
Python Design Patterns
Python Design Patterns
Docker Best Practices for Python Developers
Docker Best Practices for Python Developers
This article looks at several best practices to make your Dockerfiles and images cleaner, leaner, and more secure.
Pitfalls of a Non-technical Manager - DZone Agile
Pitfalls of a Non-technical Manager - DZone Agile
This post is intended towards the non-technical people working in the software industry, specifically towards the non-technical managers who lead teams of developers. I hope to have a series of at least two posts on this topic, if not more. Pitfalls of a Non-technical Software Manager (This post). W…
Python Programming Tutorials
Python Programming Tutorials
Python Programming tutorials from beginner to advanced on a massive variety of topics. All video and text tutorials are free.
Python Programming Tutorials
Python Programming Tutorials
Python Programming tutorials from beginner to advanced on a massive variety of topics. All video and text tutorials are free.
GitHub - TheAlgorithms/Python: All Algorithms implemented in Python
GitHub - TheAlgorithms/Python: All Algorithms implemented in Python
All Algorithms implemented in Python. Contribute to TheAlgorithms/Python development by creating an account on GitHub.
The Chaos Game - Introduction to the world of fractals
The Chaos Game - Introduction to the world of fractals
Explaining the chaos game, a so called random iteration algorithm to create beautiful fractals.
Mac Open Web, by Brian Warren
Mac Open Web, by Brian Warren
Mac Open Web - A collection of open and indie Mac, iOS, and web apps that help promote the open web.
styleguide
styleguide
Style guides for Google-originated open-source projects
Software Engineer interviews: Everything you need to prepare | Tech Interview Handbook
Software Engineer interviews: Everything you need to prepare | Tech Interview Handbook
What to expect, how to prepare and how to excel in Software Engineering interviews
NeetCode.io
NeetCode.io
A better way to prepare for coding interviews.
Low Coupling, High Cohesion
Low Coupling, High Cohesion
The key to creating maintainable code is adhering to “low coupling, high cohesion”.
The Art of Code - Dylan Beattie
The Art of Code - Dylan Beattie
Software and technology has changed every aspect of the world we live in. At one extreme are the ‘mission critical’ applications - the code that runs our ban...
GitHub - jwasham/coding-interview-university: A complete computer science study plan to become a software engineer.
GitHub - jwasham/coding-interview-university: A complete computer science study plan to become a software engineer.
A complete computer science study plan to become a software engineer. - GitHub - jwasham/coding-interview-university: A complete computer science study plan to become a software engineer.
Notion – The all-in-one workspace for your notes, tasks, wikis, and databases.
Notion – The all-in-one workspace for your notes, tasks, wikis, and databases.
A new tool that blends your everyday work apps into one. It’s the all-in-one workspace for you and your team
Donut math: how donut.c works
Donut math: how donut.c works
The Hitchhiker’s Guide to Online Anonymity
The Hitchhiker’s Guide to Online Anonymity
The Hitchhiker’s Guide to Online Anonymity
Binary Exploits 1 · CTF Field Guide
Binary Exploits 1 · CTF Field Guide
About | The Odin Project
About | The Odin Project
The Odin Project empowers aspiring web developers to learn together for free
What is Getting Real | Getting Real
What is Getting Real | Getting Real
Linux 核心設計: 作業系統術語及概念 - HackMD
Linux 核心設計: 作業系統術語及概念 - HackMD
# [Linux 核心設計](https://beta.hackfoldr.org/linux/): 作業系統術語及概念 Copyright (**慣C**) 2020 [宅色夫](http:/
Home | Linux Journey
Home | Linux Journey
Ben Eater
Ben Eater
I create tutorial-style videos about electronics, computer architecture, networking, and various other technical subjects.
GitHub - ForrestKnight/open-source-cs: Video discussing this curriculum:
GitHub - ForrestKnight/open-source-cs: Video discussing this curriculum:
Video discussing this curriculum:. Contribute to ForrestKnight/open-source-cs development by creating an account on GitHub.
Learn Shell - Free Interactive Shell Tutorial
Learn Shell - Free Interactive Shell Tutorial
learnshell.org is a free interactive Shell tutorial for people who want to learn Shell, fast.
The Linux Kernel Module Programming Guide
The Linux Kernel Module Programming Guide
大神jserv跟他帶領的學生一起寫的
每位程式開發者都該有的記憶體知識
每位程式開發者都該有的記憶體知識
每位程式開發者都該有的記憶體知識
Inside the linux kernel
Inside the linux kernel
一張圖說明linux kernel,說實話我有看沒有懂,畢竟我是搞ML的
Home | nand2tetris
Home | nand2tetris
Nand2Tetris