Technical Trainer, Content Creator required for Products Company in Pune.
Skills: Python, DevOps, Good Communication skills
3 to 10 years of experience in IT
Only for Pune location.
asif.immanad@amdocs.com

A collection of simple python mini projects to enhance your python skills.

https://github.com/Python-World/python-mini-projects

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

🔰 Collection Of 30+ Useful Web Tools And Services | Massive List 🔰

https://www.logoai.com/
https://www.24x7wpsupport.com/
https://mobirise.com/
https://www.startechup.com/our-clients/
https://www.taskade.com/
https://thegray.company/services/dashboarding-storytelling
https://testingbot.com/
https://thedock.io/
https://goodiewebsite.com/
https://total.wpexplorer.com/
https://www.completewebresources.com/
https://www.mailmunch.com/shopify-pages
https://unlayer.com/blog/email-marketing-for-small-businesses
https://helloflamingo.com/
https://kcwebdesigner.com/
https://seoforgrowth.com/podcast-booking-services/
https://bloggerlocal.com/
https://herothemes.com/plugins/heroic-inbox/
https://www.codester.com/
https://www.webdesignauckland.co/
https://www.digitalwebsolutions.com/
https://hirejordansmith.com/
https://xstore.8theme.com/
https://www.startupoasis.co/how-it-works/
https://www.antideo.com/email-validation/
https://photos.icons8.com/

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

Enjoy 👍❤️

Free Professional python course from Google with certificate👨‍🎓

https://inprogrammer.com/free-python-course-by-google-free-certificate/

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

https://youtu.be/1_sCX6da3s0
New Video
How to improve Machine Learning Model accuracy? What is feature engineering?
How to use Correlation Coefficient Matrix?

All answers in this video watch out now!!!

Open source release of COVID-Net CXR-3, our third-generation deep neural network tailored for COVID-19 screening using chest x-ray (CXR) imaging, as part of the COVID-Net initiative!

Trained using over 19,000 CXR images from a multinational cohort of over 16,656 patient cases in 51 countries (largest, most diverse of its kind), COVID-Net CXR-3 achieves state-of-the-art accuracy, sensitivity, and PPV of 98.3%, 97.5%, and 99%, respectively. Furthermore, we demonstrated state-of-art accuracy on predicting the severity of a COVID-positive patient's condition to illustrate its multi-faceted utility across different clinical decision support tasks.

Paper: https://arxiv.org/abs/2110.06063
Repo: https://github.com/lindawangg/COVID-Net/blob/master/docs/models.md
COVID-Net portal: http://www.covid-net.ml

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

Github: https://github.com/Ildaron/Laser_control

Paper: https://www.mdpi.com/2072-4292/13/21/4486

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

GoEmotions: A Dataset for Fine-Grained Emotion Classification
http://ai.googleblog.com/2021/10/goemotions-dataset-for-fine-grained.html

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

The fashion industry is on the verge of an unprecedented change. The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for this industry. This paper provides a comprehensive survey on this matter, categorizing more than 580 related articles into 22 well-defined fashion-related tasks. Such structured task-based multi-label classification of fashion research articles provides researchers with explicit research directions and facilitates their access to the related studies, improving the visibility of studies simultaneously. For each task, a time chart is provided to analyze the progress through the years. Furthermore, we provide a list of 86 public fashion datasets accompanied by a list of suggested applications and additional information for each.
link: https://arxiv.org/abs/2111.00905



@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

An important collection of the 15 best machine learning cheat sheets.
1- Supervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf

2- Unsupervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf

3- Deep Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf

4- Machine Learning Tips and Tricks

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf

5- Probabilities and Statistics

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf

6- Comprehensive Stanford Master Cheat Sheet

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf

7- Linear Algebra and Calculus

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf

8- Data Science Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf

9- Keras Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf

10- Deep Learning with Keras Cheat Sheet

https://github.com/rstudio/cheatsheets/raw/master/keras.pdf

11- Visual Guide to Neural Network Infrastructures

http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png

12- Skicit-Learn Python Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf

13- Scikit-learn Cheat Sheet: Choosing the Right Estimator

https://scikit-learn.org/stable/tutorial/machine_learning_map/

14- Tensorflow Cheat Sheet

https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf

15- Machine Learning Test Cheat Sheet

https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

🕷 Bayesian Active Learning (BaaL)

Blog: https://www.elementai.com/news/2019/element-ai-makes-its-bayesian-active-learning-library-open-source

Documentation: https://baal.readthedocs.io.

Github: https://github.com/ElementAI/baal

Paper: https://arxiv.org/abs/2112.06586v1

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

Brain tumor detection and segmentation from MRI images using CNN and Unet models.

The CNN model is used to detect whether a tumor is there or not. After 15 epochs of training, the calculated accuracy is about 99.6%.
The U-net model is used to segment tumors in MRI images of the brain. After 10 epochs of training, the calculated accuracy is about 98%.
These deep neural networks are implemented with Keras functional API. Use the trained models to detect and segment tumors on brain MRI images. The result is satisfactory.

You can download my U-net trained model from: "https://drive.google.com/drive/folders/1qt7l3HOGIwOguWsMKc5fuwG2NGiGOucf?usp=sharing" and CNN trained model from: "https://drive.google.com/drive/folders/1fXFzMwNG6HrbNp6-GASAgeybeSB3JWCd?usp=sharing".

To access the codes, refer to my GitHub.

Github: https://github.com/AryaKoureshi/Brain-tumor-detection

Website: https://aryakoureshi.github.io/project/BT_detection

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

Lane Detection With OpenCV (Part 1)

1. Intro
2. Thresholding
3. Perspective Correction
4. Warping

https://dzone.com/articles/lane-detection-with-opencv


@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

https://youtu.be/NMeycZYNpWc
New Video watch now
Heart Disease Prediction using Logistic Regression
How to use classification techniques in ML?

Training Machine Learning Models More Efficiently with Dataset Distillation


http://ai.googleblog.com/2021/12/training-machine-learning-models-more.html

Training Machine Learning Models More Efficiently with Dataset Distillation

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

🎓 GAN-Supervised Dense Visual Alignment

Github: https://github.com/wpeebles/gangealing

Project: https://www.wpeebles.com/gangealing

Paper: https://arxiv.org/abs/2112.04894v1

Dataset: https://paperswithcode.com/dataset/celeba

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

More than 5000+ amazing 🤩 DataSet!!!
Hope you find it useful!!!

https://paperswithcode.com/datasets

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI

How to built a data science and machine learning roadmap in 2022?

https://venturebeat.com/2022/01/10/how-to-build-a-data-science-and-machine-learning-roadmap-in-2022/amp/

@Artificial_intelligence_ai

To learn AI from basics:
YouTube: https://www.youtube.com/c/asifimmanad

Telegram: https://t.me/Artificial_intelligence_AI