Hello guys,

I recently developed a CNN to analyse and cut images (image text documents). I saved the entire network locally as “.h5” extension and whenever I have to use it I call it. I also use “pytesseract” (OCR Library) to extract data from the cutouts. I have created a data frame using pandas where I append the results from tesseract to maintain the logs. Currently, I am using Jupyter notebook.

I want to upload it all on the server to automate this process so I can daily check the DataFrame without the hassle of running all the notebooks. I currently have a subscription for DigitalOcean’s server.

Any leads or help on how to do this will be appreciated.

So I’ve been pretty interested in this paper A Closer Look at Memorization in Deep Networks and particularly the first experiment they did where they showed that certain data points are consistently fit in the first epoch of training whereas other data points consistently take longer epochs to fit.

But I haven’t seen any discussions anywhere about why that would be the case? Like what is it about these data points that allows them to be easily fit in the first epoch? How can we formalize this notion of “simpleness”?

My first thought is that the “simple” data are just the ones which have a gradient direction that is close to the averaged gradient direction for a given minibatch?

Anyone aware of any work specifically expanding on these questions?

Unfortunately I don’t have anyone in my lab to discuss these things with so I just resort to the next best place lol.

I’ve been experimenting with word embeddings lately from different frameworks like BERT and ELMo. I’ve tried applying these to a sequence classification problem (generated sequence embeddings by taking mean of token embeddings in the second to last hidden layer of the BERT model) and running logistic regression and random forest models using these embeddings.

However, it doesn’t seem like this works that well for small datasets (in my case, 500 data points for a 3-label classification problem). Am I correct in saying that classification using these embeddings only works well given tens of thousands of data points? All the sequence classification problems I’ve seen using these embeddings seem to support this since they have way more data (e.g. Google’s IMDB movie review sentiment example). Or are there ways you can get robust classification models with less data to work with? I was thinking of trying fine-tuning or PCA to reduce the dimensionality of the sequence embeddings and ultimately build a better classification model.

Doesn’t related directly with ML research, but still interesting to see whether it applies as much as other scientific fields:

Article from Nature Communications: Early coauthorship with top scientists predicts success in academic careers

Abstract

We examined the long-term impact of coauthorship with established, highly-cited scientists on the careers of junior researchers in four scientific disciplines. Here, using matched pair analysis, we find that junior researchers who coauthor work with top scientists enjoy a persistent competitive advantage throughout the rest of their careers, compared to peers with similar early career profiles but without top coauthors. Such early coauthorship predicts a higher probability of repeatedly coauthoring work with top-cited scientists, and, ultimately, a higher probability of becoming one. Junior researchers affiliated with less prestigious institutions show the most benefits from coauthorship with a top scientist. As a consequence, we argue that such institutions may hold vast amounts of untapped potential, which may be realised by improving access to top scientists.

https://www.nature.com/articles/s41467-019-13130-4

I am working on a case study where they gave me 3 text, 2 categorical, 1 numerical features to classify 6 classes.

However, the data is very imbalanced. Its splits like this:

Case_1: 5215/5899 = 88.4%

Case_2: 631/5899 = 10.7%

Case_3: 23/5899 = 0.39%

Case_4: 16/5899 = 0.27%

Case_5: 2/5899 = 0.03%

Case_6: 12/5899 = 0.2%

and Case_5 comes to only 1 observation after splitting data to training.

To me, it seems like over sampling minorities might result in serious overfitting. Undersampling from 5215 might result in some serious data loss. I don’t know what to do. I did do the bias to weights to log reg, but only got decent results:

normalized confusion matrix (True Positive percents):

Category_1: 96% which is 1.08 times better

Category_2: 86% which is 8.03 times better

Category_3: 100% which is 256 times better

Category_4: 80% 296 times better

Category_5: 0% since it was only 1 example in test data

Category_6: 75% which is 375 times better

Hi, I wanted to work with machine learning as a project task and I was able to get my hands on a real problem. As I’m still testing the waters with machine learning, I would like to get input of the experienced community here.

Someone used a drone to gather flight data, their overall goal is to find out what influences the network quality so that they can predict it in unknown territory.

I have flight data (time/coordinates,cell tower, network quality) for the territory A, however territory A is only a very small part of the overall territory.

So I have to predict the network quality of untested territories, with the help of the exisitng data. Fun!

So much about the problem, what I was trying to learn over the last week is what kind of machine learning task I could use for this.

It seems to me that I would need a supervised regression task. Am I correct in that assumption? Am I thinking to simple?

Thanks for any and all input.

In many companies today there are talks about using chatbots, often that means using an existing framework. But what are the current parts and their algorithms that are used in those systems.

Intent detection and NER are the ones I am familiar with.

What are examples of common algorithms/papers used in production? What are the SOTA alternatives?

What else is part of the commercial chatbot pipelines besides intent and NER?