Yapay Zeka Çalışması Yapanlar için Kaynak Dökümanlar
https://techgrabyte.com/
http://datahacker.rs/
http://www.aiobjectives.com/
https://mediapipe.dev/
Araçlar :
1- gpt4all :
https://github.com/nomic-ai/gpt4all
https://www.nomic.ai/gpt4all
2-Discover, download, and run local LLMs
https://lmstudio.ai/
Kişiler:
https://twitter.com/CansuUzunsimsek
Zafer Demirkol : https://www.linkedin.com/in/zaferdemirkol/
E-Kitap:
Yapay Zeka>Makine Öğrenmesi üzerine Ücretsiz e-kitap:
https://mlbook.explained.ai/
https://mlbook.explained.ai/notebooks/index.html - Kaynak Kodları
Ücretsiz, açık kaynak güzel kategorize edilmiş kurslar, yazılım kütüphaneleri ve diğer kaynaklar.
Oldukça bilinen bir adres, hala tanışmadıysanız kesinlikle tavsiye ederim.
Zafer Demirkol : https://www.linkedin.com/in/zaferdemirkol/ : https://www.fast.ai/
Introduction to Artificial Intelligence : https://link.springer.com/book/10.1007%2F978-3-319-58487-4
Data Science and Machine Learning
https://lnkd.in/gb2inKt
https://lnkd.in/gJydUVf
Robotic Process Automation
https://lnkd.in/gcZFcji
Eğitim:
2019′un en iyi Yapay zeka > Makine öğrenmesi online kursları:
https://medium.com/free-code-camp/top-5-machine-learning-courses-for-2019-8a259572686e
Yapay zeka ve makine öğrenmesine başlamak için harika bir fırsat ! Finlandiya’nın güzel insanları ücretsiz bir web uygulaması geliştirmişler. Öğrenmesi inanılmaz eğlenceli dersler hazırlamışlar
https://course.elementsofai.com/
Blog:
http://newsroom.gehealthcare.com/ai-backbone-deep-learning-easier-doctors-read-spine-scans/?utm_source=linkedin.com&utm_medium=GESocial&utm_content=ECR19&utm_campaign=bone+vcar
Makine Öğrenmesi İçin En İyi 50 Ücretsiz Veri Seti
https://lionbridge.ai/datasets/the-50-best-free-datasets-for-machine-learning/
Top 10 Python Libraries for Data Science
https://towardsdatascience.com/top-10-python-libraries-for-data-science-cd82294ec266
The Hitchhiker’s Guide to Python!
https://docs.python-guide.org/
Python Projects with Source Code – Practice Top Projects in Python
https://data-flair.training/blogs/python-projects-with-source-code/
Demo Projeler :
Image Inpainting lets you edit images with a smart retouching brush. Use the power of NVIDIA GPUs and deep learning algorithms to replace any portion of the image.
Demo : https://www.nvidia.com/research/inpainting/
Açık Kaynak :
Yapay zeka > otonom sürüşler günümüzün en popüler teknolojilerinden. Şerit ve çizgi takibi bu teknolojide kritik bir rol oynuyor. Buna dair basit bir örnek, çizgi takibi yapabilen bir uygulama:
https://github.com/gsurma/street_lanes_finder
30-Days-Of-Python https://github.com/Asabeneh/30-Days-Of-Python
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
https://www.linkedin.com/feed/update/urn:li:activity:6614120128609116160/
https://github.com/Yuheng-Li/MixNMatch
https://mediapipe.dev/
https://github.com/UniversalDataTool/universal-data-tool
I cannot end 2019 without sharing the Top 10 #Github Repos which are my go-to destinations for exploring #AI, ML(#machinelearning), DL(Deep Learning) in the easiest way possible.
- Study plan to become a ML Engineer: https://lnkd.in/fdMg-AK
- ML Algos with interactive Jupyter Demos: https://lnkd.in/fZq3yW6
- List of AI courses, books, video lectures, papers: https://lnkd.in/fTwJMcc
- Making Production-level DL systems: https://lnkd.in/fWbWqHd
- Roadmap for reading DL papers: https://lnkd.in/fgzKhdC
- Exploring ML in 100 Days: https://lnkd.in/fqX8SGE
- List of Computer Vision Resources: https://lnkd.in/fuGPnQ6
- Awesome DL tutorials, projects: https://lnkd.in/fqeeCpg
- List of DL/ AI/ ML Tutorials: https://lnkd.in/f3mq3xv
- Barebone implementation of ML models, algorithms: https://lnkd.in/fpapv7x
TEMEL NESNE ALGILAMA ALGORİTMALARINA ADIM ADIM GİRİŞ
Yapay zekanın popüler konularından biri olan nesne algılamayla ilgili başlangıç seviyesinde bir makale:
OpenCodeInterpreter
Integrating Code Generation with Execution and Refinement
https://github.com/OpenCodeInterpreter/OpenCodeInterpreter
