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followers: 2.0 following: 10.0 repos: 85.0 gists: 6.0

Name: Daniel Asfaw

Type: User

Bio: Machine learning

Location: UK

Blog: https://dansileshi.github.io/

Hi there 👋

I'm Daniel 😀, Data Scientist, with technical/research experience and interest in:

Deep Learning
Time series analysis
Computer Vision
Natural Language Processing

Languages and Tools:

Featured Repositories

Data Science (Tools & Knowledge)

Repo	Description	Link
LLMS	Examples that show how to apply LLMs for different use cases. Mostly in Jupyter Notebook for easy accessibility	Link
MLOps	This repository contains MLOps implementations with Microsoft Azure, providing detailed examples of Azure DevOps and Azure Machine Learning in action.	Link
Recommendation System	Production-level Implementations of Recommender systems	Link
Natural Language Processing (NLP) Examples	Full implementation examples of several Natural Language Processing methods in Python.	Link
Time series data anlaysis Examples	Full implementation examples of multiple time series data or signal analysis	Link
PySpark	PySpark functions and utilities with Real-world Data examples.	Link

Other

Repo	Description	Link
Python Projects	Python Portfolio mostly in Jupyter Notebooks by ML/DL framework (mainly using Tensorflow)	Link

How to Use These Repositories

These repositories are like learning libraries for data science and machine learning.
You can use the code and methods in these repositories as references for your own projects and research.

Feel free to reach out if you have any questions or just want to connect! 😊

Daniel Asfaw's Projects

acute-inflammations-dataset-classification

anomalib

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

awesome-computer-vision

A curated list of awesome computer vision resources

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

awesome-dataviz

:chart_with_upwards_trend: A curated list of awesome data visualization libraries and resources.

awesome-deep-learning

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

awesome-deep-vision

A curated list of deep learning resources for computer vision

awesome-gan-resources

🤖A list of resources to help anyone getting started with GANs 🤖

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-nlp

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

awesome-r

A curated list of awesome R packages, frameworks and software.

awesome-time-series

Resources for working with time series and sequence data

azure-chat-demo

Examples to use Azure with LLMs for Chat

bayesian-modelling-in-python

A python tutorial on bayesian modeling techniques (PyMC3)

bayesian-statistics

Course material for Bayesian and Modern Statistics, STA601, Duke University, Spring 2015.

bayesian-statistics-techniques-and-models-university-of-california-santa-cruz-on-coursera

Course materials for the Bayesian Statistics: Techniques and Models - University of California, Santa Cruz MOOC on Coursera

blur-videos

Blur a section of your video using manual tracking of frames

convnet-benchmarks

Easy benchmarking of all publicly accessible implementations of convnets

coursera-advanced-machine-learning-specialization

Repo for coursera specialization Advanced Machine Learning by Higher School of Economics

This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/

cs231n.github.io

Public facing notes page

dansileshi

dansileshi.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

data-science-ipython-notebooks

Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

dimension-reduction

A tour of dimension reduction techniques in Jupyter Notebook