I will carry out your NLP projects in Deep Learning with Python

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Proposé par fogangfokoa 4 ventes au total

Hi, my name is Fogang Fokoa, PhD student in machine learning. I have been a data scientist for more than 2 years.

This service aims to solve your NLP problems using neural networks and python. Mainly, this service will be able to solve 4 types of NLP problem: text classification, sentiment analysis, topic extraction and question answering.

The solving methodology will be based on the CRoss Industry Standard Process for Data Mining (CRISP-DM) which has 6 steps:

(STEP1) Understand the problem. Understand the objectives and requirements of the project, select the technologies used; examine data, its properties such as data format and number of records or field identities.

(STEP2) Understanding the data. Exploratory Data Analysis (EDA): draw graphs and analyze them;

(STEP3) Prepare the data. Clean, select, format, engineer and transform features;

(STEP4) Modeling. Generate a test design, perform a spot-check then select one model to solve an NLP problem (text classification, sentiment analysis, topic extraction, or question answering). The models (just one model will be chosen) that could be used are:
--- CNN;
--- LSTM;
--- seq2seq;
--- Word Embedding;
--- PCA, SVD, Latent Dirichlet allocation

(STEP5) Evaluation. Scoring model and review (1)-(4);

(STEP6) Deployment. Produce a final report, plan monitoring and maintenance, make the models operational;

The solution will be delivered as a jupyter or .py notebook depending on your needs.

**************************** Basic Formula *****************************
--- Debugging help on a Python Deep Learning script
(STEP1) Understand the problem
(STEP2) Understanding the data. Data visualization


******************************* Option 1 ************ ********************
(STEP1) Understand the problem
(STEP2) Understanding the data
(STEP3) Prepare data


**************** Option 2: TEXT CLASSIFICATION ****************
Add to Option (1) the (STEP4) and the (STEP5)
(STEP4) Modeling. Dimension reduction could be used in any of the model types depending on the problem encountered
(STEP5) Evaluation


**************** Option 3: SENTIMENT ANALYSIS ****************
Add to Option (1) the (STEP4) and the (STEP5)
(STEP4) Modeling. Dimension reduction could be used in any of the model types depending on the problem encountered
(STEP5) Evaluation


**************** Option 4: TOPIC EXTRACTION ****************
Add to Option (1) the (STEP4) and the (STEP5)
(STEP4) Modeling. Dimension reduction could be used in any of the model types depending on the problem encountered
(STEP5) Evaluation


**************** Option 5: QUESTION ANSWERING ****************
Add to Option (1) the (STEP4) and the (STEP5)
(STEP4) Modeling. Dimension reduction could be used in any of the model types depending on the problem encountered
(STEP5) Evaluation


**************** Option 6: WRITE A REPORT ****************
Add to Option (2), Option (3), Option (4) or Option (5) the (STEP6)
(STEP6) Deployment. Produce a final report.


Thank you.

I will carry out your NLP projects in Deep Learning with Python

  • 20,00 €

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À propos du vendeur

fogangfokoa Il y a 4 jours

“Hi, I am a PhD student in Machine Learning and Optimization. My research focuses on optimization methods (nonlinear convex and non-convex optimization in particular) of models applied to NLP, more precisely on languages with low-resource corpora.

I have been practicing data science for more than 2 years. I owe my experience to the data science and MLOPs projects that I had to lead. These projects aim to solve various and differing real-life problems (from images, to detect if a plant is sick; sentiment analysis; protein function prediction; landslide prevention thanks to satellite imagery; credit scoring; etc.), addressing thematics such as computer vision, bioinformatics, natural language processing and speech recognition .

I have know-how on how machine learning algorithms (linear regression, SVM, decision tree, boosting, HMM, etc.) and deep learning (embedding, CNN, seq2seq, LSTM, transformers and transformers-based models) work, how to configure them, modify them according to the problems faced.”

  • Temps de réponse moy. —
  • Commande en cours 0
  • Ventes au total 4
  • Vendeur depuis Août 2022
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