I’m familiar with debiasing word2vec embedding [https://arxiv.org/pdf/1607.06520.pdf] and GloVE embedding[http://cs229.stanford.edu/proj2016/report/BadieChakrabortyRudder-ReducingGenderBiasInWordEmbeddings-report.pdf]

Are there any researches with ElMO or Bert architectures?

Way too many businesses reach for deep learning solutions when they shouldn’t.

There are several factors that make relatively simpler models more suitable than their deep learning counterparts: Why deep learning may not be the right solution for your business

• Costs – The problems most, especially small, businesses are facing do not really require very complex and sophisticated methods which only increase costs and time.
• Not enough good-quality data – in some cases data sets are not big enough for deep learning which usually demands huge sample sizes.
• Limited interpretability – It is important because of new insights into relationships between numerous variables and expected outcomes, it increases the trust and understandability.

I’ve anecdotally seen the connection between dropout and ensemble learning (i.e. dropout essentially trains a subnetwork so over the course of training it trains like an ensemble) mentioned in several places; however, I couldn’t find any theoretical references. Are there any known theoretical results that make the connection between ensemble learning and dropout more concrete?

While following the guide on How to Develop a Neural Machine Translation System from Scratch, I’m trying to create a translation system for Portuguese-English. After the first fit, I checked the results and while most are just gibberish, this one is quite amusing:

src=[ele e culpado de assassinato], target=[he is guilty of murder], predicted=[i dont believe anything]

Hi y’all,

I am a second-year at the University of California, Merced and this is a project I’ve been working on over the last few months. Its not state-of-the-art or anything like that, but any feedback on my work would be much appreciated. Keep in mind, I don’t have a degree (yet) in Computer Science, so any form constructive criticism will be helpful!

You can find my code at: https://github.com/PhilipFelizarta/epsilon-Explore

Quick Summary:

Since the creation of AlphaZero, a majority of Deep Learning research and engineering for computer chess has been centered around the “Zero” doctrine; that is, focusing on creating a chess engine utilizing zero human knowledge. While AlphaZero (and Leela Zero) are grand milestones for AI, a common critique is the computational costs required to execute these reinforcement learning algorithms. Motivated to create an efficient, yet scalable learning algorithm, I propose an elementary, yet novel solution: 𝝐-Explore. 𝝐-Explore is a handcrafted adaptation of greedy-epsilon exploration, Go-Explore, and supervised learning that frames exploration tasks as continual learning and utilizes significantly less computational resources when compared to state-of-the-art reinforcement learning algorithms. All experimentation uses only a single GPU (RTX Titan) and a single CPU (Threadripper 16-core). The results of 𝝐-Explore are not state-of-the-art with our experimental setup, but provide a foundation for creating more efficient handcrafted algorithms in other large search spaces given an available expert policy.

​

Note: I’ll be continually updating this GitHub repository as I do more tests!

Project link: http://l7.curtisnorthcutt.com/the-best-4-gpu-deep-learning-rig

Hi Reddit! This is a follow-up to the previous post [P] I built Lambda’s $12,500 deep learning rig for $6200 which had around 480 upvotes on Reddit. That previous build had only 3-GPUs and took some shortcuts. In response to the hundreds of comments on that post, including comments by the CEO of Lambda Labs, I built and tested multiple 4-GPU rigs. I’m back to share a near-perfect 4-GPU deep learning rig with the highest performance and reliability, no thermal throttling, and lowest cost. This build is nearly identical to Lambda’s 4-GPU workstation, but costs around $4000 cheaper. Happy building!

Sorry if these types of posts aren’t allowed but I didn’t get any replies on /r/learnmachinelearning.

​

Basically, is it possible to group together input features in scikit so that rather than reading a single variable, the input feature is a vector? This would make the shape of X a 3D array rather than 2D . Is there a term for this that I can search for or can someone point me in the right direction? I want to use it for simple ML algorithms like random forest or XGBOOST.