lalit7jain Goto Github PK

a-picture-is-worth-a-1000-words

I am trying to describe complex matters in simple doodles!

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Advances in Data Science/Architecure

amazonsentimentreview

Using Gensim's Doc2Vec models (DM and DBOW) classify sentiments using Logistic, SVM, SGD and Deep Belief Network

awesome

:sunglasses: Curated list of awesome lists

captcha-solved-with-deep-learning

This is just meant as a fun and quick technical challenge. But if you are one of the remaining 1+ million users, maybe you should switch to something else :)

classification-and-regression

creditcardfrauddetection

creditcardfrauddetection_finalproject

Kaggle Competiton: Using Anomaly detection technique on a highly imbalanced high dimensional data to detect fraud. Used variety of technique including SMOTE to balance the data set and other machine learning technique to build the model.

finlandenergyprediction

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

im2txt_word2vec

Image Captioning using word2Vec technique involving Deep learning techniques like CNN and RNN (LSTM).

java

Java Project

lv10vim

mmm_stan

Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

nosql_datascience_mongodb-cassandra-neo4j

Working with BigData NoSQL solutions including MongoDB, Cassandra and Neo4J

objectclassification_cnn_keras

Using CNN with Keras and Tensorflow, we have a deployed a solution which can train any image on the fly. Code uses Google Api to fetch new images, VGG16 model to train the model and is deployed using Python Django framework

python

requests-html

Pythonic HTML Parsing for Humans™

restricted-boltzman-machine-rbm-

Using RBM to work on Classifying MNSIT handwritten data-set for classification. RBM is implemented from scratch and not using any libraries. This helps understanding the maths and how an energy function and its probability distribution is used in an Machine Learning domain

robyn

Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) code from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression with cross validation, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency and effectivity, explore adstock rates and saturation curves. It's built for granular datasets with many independent variables and therefore especially suitable for digital and direct response advertisers with rich dataset.

rstudio

supervisedlearning

tensorflow-examples

TensorFlow Tutorial and Examples for beginners

thinkstats2

Text and supporting code for Think Stats, 2nd Edition

unsupervisedlearning