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.

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