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accel-amplicon-trimming

Snakemake workflow implementing Accel Amplicon (Swift) trimming guidelines.

advanced-cnn-architectures

advanced-house-price-prediction-

ai

airlines-passengers-forecasting-lstm

all-hyperparamter-optimization

alpaca-lora

Instruct-tune LLaMA on consumer hardware

amazon-review-scraper

A python library to scrape data from amazon.com

amazon-use-cases

analytics_vidhya

Codes related to activities on AV including articles, hackathons and discussions.

aqi-project

attention_keras

Keras Layer implementation of Attention

augmented-reality

autogen

Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ

autokeras-regression

AutoKeras is an open-source library for performing AutoML for deep learning models based on Keras.In this video, I'll show you how you can use AutoKeras for Regression.

automl

Google Brain AutoML

automl-in-healthcare-review

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

automl-resources

A list of resources, papers, and articles on AutoML, Automatic Hyperparameter Tuning, and Neural Architecture Search.

awesome-artificial-intelligence-research

A curated list of Artificial Intelligence (AI) Research

awesome-automl-papers

A curated list of automated machine learning papers, articles, tutorials, slides and projects

awesome-customer-analytics

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services. In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics. By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant. With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience. This tsunami of data has changed the customer analytics forever. Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization. A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics. From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation. Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure. Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before. Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical. There are various ways customer analytics is carried out: Acquiring all the customer data Understanding the customer journey Applying big data concepts to customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers & churn patterns Applying predictive analytics Implementing continuous improvement Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time. Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect. Tomorrow there may not be just plain simple customer sentiment analytics based on feedback or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time. There’s no doubt that customer analytics is absolutely essential for brand survival.

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

awesome-distributed-deep-learning

A curated list of awesome Distributed Deep Learning resources.

awesome-dl4nlp

A curated list of awesome Deep Learning for Natural Language Processing resources

awesome-healthcare

Curated list of awesome open source healthcare software, libraries, tools and resources.

awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

aws-sagemaker

azuredeployment

azureml-bert

End-to-End recipes for pre-training and fine-tuning BERT using Azure Machine Learning Service