My bloody experience that worked for an AI startup.

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63 votes and 16 comments so far on Reddit

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)

Memory-efficient techniques to defeat the problem of “CUDA memory error..” during training

Make huge neural nets fit in memory. Contribute to cybertronai/gradient-checkpointing development by creating an account on GitHub.

Increasing batch size to overcome memory constraints

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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…

Visual explanations of core machine learning concepts.

Efficient Deep Learning Systems course materials (HSE, YSDA) - GitHub - mryab/efficient-dl-systems: Efficient Deep Learning Systems course materials (HSE, YSDA)

OpenML is an open platform for sharing datasets, algorithms, and experiments - to learn how to learn better, together.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Quantization and fusion for faster inference

Explore 50+ most popular open-source MLOps tools. Build your own stack based on our template.

Drivendata

Deep dive machine learning articles with a focus on solving the hard problems in production engineering.

This is a field that is heating up. Keep your eyes out on it

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…

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…

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…

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

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…

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…

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…

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…

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…

Self-supervised visual pre-training from real-world images is effective for learning motor control tasks from pixels.

Not even one person noticed.

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.

Embedded data teams are closer to business problems but ownership of what to do when data goes wrong is complicated

The vast majority of Machine Learning (ML) projects FAIL.

Learn how to responsibly deliver value with ML.

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…

Blog of Sr. Director of AI at Tesla

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.

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…

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 […]

Find latest and trending machine learning papers

Aman’s AI Journal | Course notes and learning material for Artificial Intelligence and Deep Learning Stanford classes.

Things happening in deep learning: arxiv, twitter, reddit

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 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.

Learn about Pool class in Python, and how it is used to perform multiprocessing in Python.

This article looks at several best practices to make your Dockerfiles and images cleaner, leaner, and more secure.

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 from beginner to advanced on a massive variety of topics. All video and text tutorials are free.

Python Programming tutorials from beginner to advanced on a massive variety of topics. All video and text tutorials are free.

All Algorithms implemented in Python. Contribute to TheAlgorithms/Python development by creating an account on GitHub.

Explaining the chaos game, a so called random iteration algorithm to create beautiful fractals.

Mac Open Web - A collection of open and indie Mac, iOS, and web apps that help promote the open web.

Style guides for Google-originated open-source projects

What to expect, how to prepare and how to excel in Software Engineering interviews

A better way to prepare for coding interviews.

The key to creating maintainable code is adhering to “low coupling, high cohesion”.

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...

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 new tool that blends your everyday work apps into one. It’s the all-in-one workspace for you and your team

The Hitchhiker’s Guide to Online Anonymity

The Odin Project empowers aspiring web developers to learn together for free

# [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:/

I create tutorial-style videos about electronics, computer architecture, networking, and various other technical subjects.

Video discussing this curriculum:. Contribute to ForrestKnight/open-source-cs development by creating an account on GitHub.

learnshell.org is a free interactive Shell tutorial for people who want to learn Shell, fast.

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

一張圖說明linux kernel，說實話我有看沒有懂，畢竟我是搞ML的

Nand2Tetris

Hope there are less programmer struggles like me.

Hope there are less programmer struggles like me.

A lightweight, dependency-free Python library (and command-line utility) for downloading YouTube Videos. - GitHub - pytube/pytube: A lightweight, dependency-free Python library (and command-line ut...

A project template and directory structure for Python data science projects.