dipanjanS/adv_nlp_workshop_odsc_europe23

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dipanjanS/adv_nlp_workshop_odsc_europe23

dipanjanS

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Extensive tutorials for the Advanced NLP Session in Open Data Science Conference Europe 2023. We will leverage deep transfer learning, notably transformers to solve popular tasks in NLP including Classification, Information Retrieval, Sentiment Analysis, Search Engines, Clustering, Paraphrase Mining, Summarization, Language Translation, Q&A systems

practical-machine-learning-with-python

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

Jupyter Notebook

text-analytics-with-python

Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.

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hands-on-transfer-learning-with-python

Deep learning simplified by transferring prior learning using the Python deep learning ecosystem

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tensorflow2-crash-course

A quick crash course in understanding the essentials of TensorFlow 2 and the integrated Keras API

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training-fine-tuning-large-language-models-workshop-dhs2024

This repository will contain all the presentations, content, hands-on notebooks for a full day Generative AI workshop on Training, Fine-tuning Large Language Models for the DataHack Summit 2024 conference.

Jupyter Notebook

data_science_for_all

Code and resources for my blog and articles to share Data Science and AI knowledge and learnings with everyone

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nlp_essentials

Essential and Fundametal aspects of Natural Language Processing with hands-on examples and case-studies

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art_of_data_visualization

The art of effective visualization of multi-dimensional data

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nlp_workshop_odsc_europe20

Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models.

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learning-social-media-analytics-with-r

This repository contains code and bonus content which will be added from time to time for the book "Learning Social Media Analytics with R" by Packt

BerkeleyX-CS100.1x-Big-Data-with-Apache-Spark

This repository contains code files specifically IPython notebooks for the assignments in the course "Introduction to Big Data with Apache Spark" by UC Berkeley and Databricks on edX

Jupyter Notebook

adversarial-learning-robustness

Contains materials for workshops pertaining to adversarial robustness in deep learning.

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deep_transfer_learning_nlp_dhs2019

Contains the code and deck for the presentation on Applying Deep Transfer Learning for NLP in Analytics Vidhya's DataHack Summit 2019

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nlp_crash_course_plugin20

Contains relevant notebooks for the hands-on NLP workshop for the Analytics India Magazine Plugin Conference -2020 Edition

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nlp_workshop_dhs18

Contains code and presentation for our full day workshop, 'Getting Started with Natural Language Processing'. This is created for the purpose of being presented in Analytics Vidhya's DataHack Summit 2018. Authors: Dipanjan Sarkar & Raghav Bali

Jupyter Notebook

improving-RAG-systems-dhs2024

This repository will contain the presentation and python jupyter notebooks for the DataHack Summit 2024 conference talk, Improving Real-world Retrieval Augmented Generation Systems, focusing on the key challenges and practical solutions of how to solve them

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adv_nlp_workshop_odsc_europe22

Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage deep learning and deep transfer learning to solve popular tasks in NLP including Classification, Information Retrieval, Sentiment Analysis, Search Engines, Clustering, Paraphrase Mining, Summarization, Language Translation, Q&A systems

Jupyter Notebook

convolutional_neural_networks_essentials

Contains presentation deck and notebooks showcasing fundamental concepts and hands-on examples for Convolutional Neural Networks

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nlp_workshop_odsc19

Contains all tutorials and hands-on examples for the ODSC 2019 Workshop

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live-manning-nlpconf20

Papers, code and slides for my session at the live@manning NLP conference, 2020 covering my talk on Deep Transfer Learning for Natural Language Processing

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explainable_artificial_intelligence

Slides, code and resources for model interpretation methods in machine learning and deep learning

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BerkeleyX-CS190.1x-Scalable-Machine-Learning

This repository contains code files specifically IPython notebooks for the assignments in the course "Scalable Machine Learning" by UC Berkeley and Databricks on edX

feature_engineering_session_dhs18

Contains code and presentation for my interactive hack session, 'Effective Feature Engineering: A Structured Approach to Building Better Machine Learning Models' where we look at two interesting case studies on how to effectively leverage feature engineering and use a structured approach to build good machine learning models. This is created for the purpose of being presented in Analytics Vidhya's DataHack Summit 2018

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transformers_nlp_essentials

Contains slides and hands-on tutorials for understanding and implementing Transformers in Natural Language Processing. Uses the HuggingFace Transformers framework in the hands-on tutorials.

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practical_nlp_workshop_gids20

Contains relevant notebooks for the hands-on NLP workshop for the GIDS AIML Conference -2020 Edition

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low_code_machine_learning_pycaret_workshop_2022

This workshop was done as a part of the 1729 conference organized by Fractal Analytics and Analytics Vidhya. Key content covered was hands-on notebooks leveraging PyCaret to compare, build, tune, evaluate and interpret machine learning models

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nlp_workshop_iisc19

Hands-on examples showcasing popular NLP applications

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adversarial_learning_tfug2020

Contains the slides and hands-on tutorials showcasing adversarial learning on convolutional neural networks to build robust vision models

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transfer-learning-in-action

This repository contains detailed tutorials and notebooks as an accompaniment to our new book 'Transfer Learning in Action' which will be a live work-in-progress with additions over time.

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stanford-statistical-learning

Slides, material and solutions of the popular Statistical Learning course from Stanford's own Hastie & Tibshirani. Join me on my journey to finally try and complete this course after leaving it mid-way atleast 3-4 times due to other commitments!

Jupyter Notebook

MyShinyApps

Shiny is a web application framework for R. This repo contains all the web apps developed by me using R and Shiny.

ml_model_deployment_example

A simple example to showcase machine learning model deployment with an API

Jupyter Notebook

Digital-image-steganography

This project successfully implements an encoder-decoder system where we can hide a secret image inside another image and retrieve it secretly later using the decoder only.

flask-api-tutorials

This repository contains the RESTful APIs developed showing the capabilites of Flask and how to modularize the same code using some advanced features of Flask

student-information-system

This project has an entire template for managing students and related information pertaining to them in any University.

deeplearning.ai-generative-ai-courses

This repository will contain all the exercises, tutorials and python jupyter notebooks for all the DeepLearning.AI courses on Generative AI, ChatGPT, LangChain, LLMs and more

Jupyter Notebook

online-movie-booking

This project consist of a template which can be used for handling operations related to typical online movie booking websites.

pyslate

This is a text translation library implemented in Python, it uses the Google Translate API for language detection and translation.

text-analytics-python-improvements

This is a temporary repository for working on improvements for my book 'Text Analytics with Python'

Temperature-Aware-Linux

A temperature-aware application for creating a temperature-aware variant of the linux OS

duke-cloud-computing-for-data-coursera

Will contain all the necessary code examples for the Duke Cloud Computing Specialization on Coursera

Jupyter Notebook

adv_nlp_workshop_odsc_apac2323

Extensive tutorials for the Advanced NLP Session in Open Data Science Conference APAC 2023. We will leverage deep transfer learning, notably transformers and LLMs like ChatGPT to solve popular tasks in NLP including Classification, Information Retrieval, Sentiment Analysis, Search Engines, Summarization, Language Translation, Q&A systems

Jupyter Notebook

dipanjanS.github.io

tag-me

The main objective was to design machine learning algorithms for tagging/classifying a collection of images.

TweetSense

Sentiment analysis of live Twitter Stream

railway-reservation-system

This was one of my projects when i just started with Java so it obviously has a lot of bugs and scope for improvement. However it implements the working of a typical railway reservation system

practicalML-course-project

This detailed analysis has been performed to fulfill the requirements of the course project for the course Practical Machine Learning offered by the Johns Hopkins University on Coursera

datasciencecoursera

This repo is specially created for all the work done my me as a part of Coursera's Data Science Specialization.

gf

simple-chat-application

A simple application implemented in C, where a client can chat with the server and send messages to each other.

dipanjanS

deep-learning-cloud-gpu-setup-guide

Learn how to start using deep learning frameworks leveraging python, jupyter notebooks in the cloud using GPUs with a ready-reference guide

dataprod-project-pitch

Slidify presentation for Coursera's Developing Data Products course project

play2048usingR

Now you can make the computer play the very popular game 2048 for you by using R!

noaa-stormdatabase-analysis

Here, we will explore and analyse the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database.