Here is the image:

• https://medium.com/@ageitgey/try-deep-learning-in-python-now-with-a-fully-pre-configured-vm-1d97d4c3e9b

It’s a a fully configured deep learning VM. It’s about 7.7GB.

I am running on:

• an i7 3770 quad core at 3.4 Ghz (not the i7 3770K which has no VT-d virtualization extensions – this one does.)

• 16 GB of DDR 3 ram that is clocked at 1600 mhz. I benchmarked it at 19000 MB/sec in Novabench.

• The GPU in the host is 1060 GPU with 6 GB of RAM – but the image file I linked above explains “Due to licensing and installation complications, there’s no GPU acceleration / CUDA support provided. So you don’t need an Nvidia GPU to try this out, but it also won’t take advantage of a GPU if you have one.”

• windows 10.

• I have an SSD. It’s not the fastest but the write speed is 214 MB/seconds and the read speed is 253 MB/seconds in Novabench.

• I turned the swap off entirely on the host system and have plenty of RAM left.

Without anything else running on the host computer, which is a pretty fresh install, this VM doesn’t feel native at all when entering full screen, regardless if I give it 2 GB (default), 4 GB, or even 8 GB of memory. I turned on the virtualization extensions in my bios and in the vmware settings too! I went from giving 1 CPU to 2 CPU’s to the guest. None of this helps.

why deson’t it feel “native”? I mean things like moving the mouse around, opening a new firefox windows, etc. It’s “obviously” a VM.

Is it not supposed to feel native or buttery smooth? I’m just at a loss. Additionally, I did an update from within it and it updated Ubuntu files at 500 KB/second! My connection is several megabytes per second.

I feel like I must be missing something….but what??

Mutual Information is an important measure of dependence between two variables and is often used in ML for various different reasons (e.g. representation learning). In the last year there were some new results and papers which made me curious about the topic and I wanted to share some of my thoughts.

In the first post I introduce a couple of new estimators based on bounds on the log marginal likelihood. Some of them are very appealing as they give us 1) both lower and upper bounds, 2) a way to make bounds tighter by putting more computation into it.

In the second post I move to the analysis of (some of) these estimators, as well as address the Formal Limitations paper – a paper that, loosely speaking, forbids good only-samples-based (blackbox) bounds on the Mutual Information. In particular, I show how this issue manifests itself in several widely used blackbox bounds, and then contrast it with bounds that use some distribution knowledge.

Discussion might not be quite introductory, so I recommend checking out a recent ICML paper On Variational Bounds of Mutual Information.

A step-by-step tutorial on how to implement and adapt Autoregressive language model to Wikipedia text.

A pre-trained BERT, XLNET is publicly available ! But, for NLP beginners, It could be hard to use/adapt after full understanding. For them, I covered whole, end-to-end implementation process for language modeling, using unidirectional/bidirectional LSTM network, we already know.

• – do not use torchtext library !
• + include trained model file, training logs

I hope that this repo can be a good solution for people who want to have their own language model 🙂

https://github.com/lyeoni/pretraining-for-language-understanding

Hi, r/MachineLearning !

As I finished implementation of Google’s MusicTransformer,

I want to share it to here.

• library : Tensorflow2.0 (beta)

• training env : v100 x 1GPU

​

paper : https://arxiv.org/abs/1809.04281

github : https://github.com/jason9693/MusicTransformer-tensorflow2.0

Google Magenta blog : https://magenta.tensorflow.org/music-transformer

​

For more details, you can see in README.md

Thank you 🙂

​

ps) A video below here is generated sample after listened “Boy with Love (BTS)”

https://reddit.com/link/cqk3to/video/mwnl0yfo0jg31/player

Selection of the optimal parameters values for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.

https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad