Hi everyone working on my 1st ML project and I’m looking for some tips.

I want to predict the income of a store (clothing store for example) by comparing multiple statistics that might influence the income (rain, temperature, day in month etc.) first question will be if this is even possible at all and where to start. I think I can get 3 years of income details of a clothing store that would be the 1st step but where do I go from there?

Pre-thanks guys and gals

Various versions of this questions have been asked, but a lot of what I’ve seen focuses on people coming from statistics/analysis/etc backgrounds in math. My focus is in algebraic geometry and homotopy theory, so it’s very far removed from most of those areas that appear directly in typical ML papers. Does anyone have any reading/resource recommendations that draw from the more algebraic side of math, or that would be most interesting to someone in AG/homotopy theory? Thanks!

Just saw some discussions about Google translating the English sentence “so sad to see hong kong become China” into “so happy to see hong kong become China” in Chinese.

Video: https://www.youtube.com/watch?v=Lf5_u4x-rw4

Notice how the Chinese word corresponding to “sad” (“悲傷”) became “happy” (“高興”) in the final output.

Seems that people reported this error so the example in the video was fixed. Is it an attempt to attack Google Translate by providing false examples?

Machine Learning has numerous applications in today’s technological world. One of its key applications comes in detecting credit card frauds. Credit card fraud has become quite common with time. Detecting it can often prove to be a strenuous task and sometimes it can take a long time after the act is committed. But with machine learning, the process becomes faster and more accurate.

There are many tutorials that explain how machine learning helps in detecting credit card fraud. But this video, explains machine learning with a project.

Checkout https://youtu.be/9cXeEwOXWU8 to create project in credit card fraud detection.

Hello,

Throughout my search for promising master’s programs in Europe (and the UK 🙂 ), I’ve seen the trend that some of the top programs (mostly located in the UK) offer only one year of study. Since I’m interested in pursuing a PhD, I feel like my master’s should mostly be spent on branching out my knowledge in various ML domains as much as possible to better guide me to an interesting research area. However, with only two semesters of study, where the second one also includes a master’s thesis, this goes against my notion. Moreover, I would like to join a research group while doing master’s, and I feel like having everything in a single year could become overwhelming.

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Is my understanding “wrong”? There are many great institutions in the UK offering a 1-year master’s program and I would really like to like this shorter-term master’s idea. Ideally, I would like to do PhD in the same institution as I did my master’s and thus I don’t want to miss out.

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So, is one year of master’s sufficient?

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Thank you!

It started as a need for a dataset with images and turned into writing a tool which can download parts of Imagenet using its API. I also created a little analysis of the current state of the ImageNet URLs from its API which helped to significantly improve the downloader.

Here I wrote about the process: https://mf1024.github.io/2019/06/09/how-to-scrape-the-imagenet/

I hope it’s helpful to others and comments are appreciated.

Paper: https://arxiv.org/abs/1906.05243

Abstract: We examine the question of when and how parametric models are most useful in reinforcement learning. In particular, we look at commonalities and differences between parametric models and experience replay. Replay-based learning algorithms share important traits with model-based approaches, including the ability to plan: to use more computation without additional data to improve predictions and behaviour. We discuss when to expect benefits from either approach, and interpret prior work in this context. We hypothesise that, under suitable conditions, replay-based algorithms should be competitive to or better than model-based algorithms if the model is used only to generate fictional transitions from observed states for an update rule that is otherwise model-free. We validated this hypothesis on Atari 2600 video games. The replay-based algorithm attained state-of-the-art data efficiency, improving over prior results with parametric models.