I’m a Master’s student at a university of no repute. I’m not stupid. In fact I would say I’m fairly intelligent. I graduated near the top of my class. I’ve always been performed well in academia and have been decent at math. But I feel like I’ve always had to work harder to get it than others. I’m not a prodigy.

When it comes to ML, specifically ML Engineering (which is where I want to be), it feels like there’s a mountain of things you have to know: Software Engineering principles, a variety of languages, algorithms and their complexities, software frameworks, statistics, mathematics, domain specific requirements. And I feel like the field is always changing and I’m never going to be “informed” as it were.

I feel like I’ve spent most of my Master’s degree just checking off the boxes to get my degree (while also paying for it) and I haven’t had enough time to delve into ML and now that I’m 8 months out from being done I don’t have the knowledge I need to actually move into this field.

But when I read this sub I think that I’m never going to be ready to move into the field. I’m always going to be fighting to understand the math well enough but then I’m not going to have enough time to understand the frameworks or the software engineering. It feels like I’m the jack of all trades but the master of none.

How do I navigate this field to feel like I’m learning effectively? Is it even worth it to pursue this field if I’m not a math prodigy? I want feel competant and that I’m not just another surface level ML practitioner.

I am using BERT for sentence embedding, and currently my best solution is a highly optimized rainbow dictionary, but it’s not scaling well. Attempts to do matching on sub-strings has proved unsuccessful.

I am about to try and train a bidirectional RNN, but that is a huge direction. I was wondering if anyone had advice to look in.

FAIR paper that claims to “obviate RAdam”. It writes RAdam(W) is functionally equivalent to Adam(W) with “linear warmup over 2 / (1 – beta2) training iterations”.

Legit? This makes either MSR or FAIR look pretty bad.

https://arxiv.org/abs/1910.04209

A new dataset and detection algorithms for DeepFake Forensics
http://www.cs.albany.edu/~lsw/celeb-deepfakeforensics.html

Hi,
I’ve recently read CycleGAN paper and wanted to train a toy translator to have fun.
Jokeriser takes an image or a video or a webcam frame as an input and finds faces using `facenet-pytorch` before translating those into jokerish faces using the CycleGAN generator that I trained. Despite the small dataset (300 jokers and 300 CelebA faces) the image translator seems to work okay with unseen faces.

Here’s the repo: https://github.com/junkwhinger/jokerise

Sometimes it’s hard to keep up with all the advancements in ML.
I’ve recently been searching for good telegram channels and found a lack of them.
ML World telegram and Open Data Science telegram are the only decent channels I’ve found.

Please share any decent resources you follow. Not limiting with telegram.

Hi Everyone,

I would like to use Bayesian Optimization to tune some hyperparameters. The problem is that the number of hyperparameters I want to tune is also a hyperparameter. These hyperparameters all relate to the same thing, i.e. all of them are learning rate, but I am not sure of the number of learning rate hyperparameters I need.

So I want to tune the number of hyperparameters n and tune n hyperparameters. I was thinking of justing having the max length of hyperparameters and then also have a parameter for the num of hyperparameters and selectiviely choosing only n parameters. Also since number of hyperparameters is an int, I know this is not ideal. Is there a more principled approach? Would I need to implement a specialized kernel function for this?

Hey, I m working on a code completion eclipse plugin for creating xml based data models. These models describe e.g. an iot device, which could be an air temperature sensor or whatever. To make this efficient and not compare to all already existing data models I will need to classify my existing data models. Any idea how to start? Thanks for your help 🙂

In a CNN the image sizes get reduced through valid convolutions and padding layers (while the depth increases). In popular NN, e.g. VGG16 it goes down to (7x7x512) (width x height x depth). Would it be problematic to reduce the dimension even further to 1×1 (width x height) or when can this be beneficial? Unfortunately, I couldn’t find anything related to the question online.