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Solving one problem at a time

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Articole: 7

  1. Building a Text-to-SQL Model: What I Learned Spending $60 and Two Weeks

    (Solving one problem at a time)

    I recently built a model that converts natural language into SQL queries.

  2. Text Classification – Classifying product titles using Convolutional Neural Network and Word2Vec embedding

    (Solving one problem at a time)

    Text classification help us to better understand and organize data.

  3. Productionizing a CRF model, Recipe Ingredients Tagger in Action.

    (Solving one problem at a time)

    A popular way to productionize a statistical model would be to expose them as a REST API, so that they can be scaled horizontally and is cost effective.

  4. Structuring text – Sequence tagging using Conditional Random Field (CRF). Tagging recipe ingredient phrases.

    (Solving one problem at a time)

    Building a food graph is an interesting problem.

  5. Setting up python development environment with buildout

    (Solving one problem at a time)

    Attn: Checkout Conda before trying this.

  6. Locality sensitive hashing (LSH) – Map-Reduce in Python

    (Solving one problem at a time)

    I’d try to explain LSH with help of python code and map-reduce technique.

  7. Clustering Text – Map Reduce in Python

    (Solving one problem at a time)

    Here I’m sharing a simple method to cluster text (product titles) based on key collision.