Keras VS gensim

Pandas comes with many complex tabular data operations. And, since it exists in a Python environment, it can be coupled with lots of other powerful libraries, such as Requests (for connecting to other APIs), Matplotlib (for plotting data), Keras (for training machine learning models), and many more.

Our trick is to support GitHub Codespaces, which provides a web-based Visual Studio Code IDE. The best thing is you can specify a Dockerfile with all the required dependency software installed. With one click on the repo’s webpage, your contributors are ready to code. Here is our setup for your reference.

If you’re looking for further resources on running TensorFlow and Keras on a newer MacBook, I recommend checking out this YouTube video: How to Install Keras GPU for Mac M1/M2 with Conda

The thing that pops right into my mind is the following issue: https://github.com/keras-team/keras/issues/13118 People are still reporting memory leaks when calling model.predict and I wouldn't be surprised if model.fit also leaked when called multiple times. Maybe this is a good starting point for your investigation. If this is unrelated, I'm sorry in forward.

Hello world. This tutorial is a gentle introduction to building modern text recognition system using deep learning in 15 minutes. It will teach you the main ideas of how to use Keras and Supervisely for this problem. This guide is for anyone who is interested in using Deep Learning for text recognition in images but has no idea where to start.

- statsmodel (https://keras.io);

E.g. If you consider it image classification (you already have the pedestrians extracted and just need to classify their intent), you might find that easier to do with Keras, just butcher one of the examples on keras.io. You might also find fast.ai more to your liking.

Keras

This is where machine learning takes over. Using libraries such as TensorFlow Recommenders with Keras models, it's easy to shape the data in ways that will allow the items and users to be viewed and compared in a multidimensional perspective. Qualitative features such as item categories and user profile attributes can be mapped into mathematical concepts that can be quantitatively compared with one another, ultimately providing new insights and better recommendations.

Keras – An open-source software library that provides a Python interface to TensorFlow for artificial neural networks

This is our optimization problem. Now, we hope that you have an idea of what our goal is. Luckily for us, this is already implemented in a Python module called gensim. Yes, these guys are brilliant in natural language processing and we will make use of it. 🤝

Standout python NLP libraries include Spacy and Gensim, as well as pre-trained model availability in Hugginface. These libraries have widespread use in and support from industry and it shows. Spacy has best-in-class methods for pre-processing text for further applications. Gensim helps you manage your corpus of documents, and contains a lot of different tools for solving a common industry task, topic modeling.

For the topic modelling itself, I am going to use Gensim library by Radim Rehurek, which is very developer friendly and easy to use.

We will be using gensim to load our Google News pre-trained word vectors. Find the code for this here.

> Problem statement: the Levenshtein distance is a string metric for measuring the difference between two sequences

Another variant is "I have a bunch of words (a dictionary) and one query word, and want to find all words from the dictionary that are close to the query word".

This leads to an interesting class of problems, because you can do clever things where you precompute search structures (Levenshtein automata [0]) from the dictionary. The similarity queries then run (much) faster – in production, performance matters.

We recently merged a PR like that into Gensim [1].

This gave a ~1,500x speed-up compared to naively comparing all pairwise strings with Levenshtein distance. A difference between the training step running for years (=unusable) and minutes.

[0] http://blog.notdot.net/2010/07/Damn-Cool-Algorithms-Levensht...

[1] https://github.com/RaRe-Technologies/gensim/pull/3146

Apparently it did: https://github.com/RaRe-Technologies/gensim/issues/1873