The Data Science and Python channel is for researchers and advanced programmers

Subscribe: t.me/DataScienceT

Create an Audiobook in Python

✅ More ♥️♥️ = more posts

@CodeProgrammer ♥️

Speech to Text using Python

✅ More ♥️♥️ = more posts

@CodeProgrammer ♥️

More ♥️👍 = more important projects

Building a Convolutional Neural Network in PyTorch

https://machinelearningmastery.com/building-a-convolutional-neural-network-in-pytorch/

✅ More ♥️♥️ = more posts

@CodeProgrammer ♥️

Don't Forget join to another channel
https://t.me/DataScienceT

How do Transformers work?

All the Transformer models mentioned above (GPT, BERT, BART, T5, etc.) have been trained as language models. This means they have been trained on large amounts of raw text in a self-supervised fashion. Self-supervised learning is a type of training in which the objective is automatically computed from the inputs of the model. That means that humans are not needed to label the data!

This type of model develops a statistical understanding of the language it has been trained on, but it’s not very useful for specific practical tasks. Because of this, the general pretrained model then goes through a process called transfer learning. During this process, the model is fine-tuned in a supervised way — that is, using human-annotated labels — on a given task

🔗 Read More

🌸 https://t.me/DataScienceT

Data Science With Python Workflow Cheat Sheet

Creator: business Science
Stars ⭐️: 75
Forked By: 38

https://github.com/business-science/cheatsheets/blob/master/Data_Science_With_Python_Workflow.pdf

https://t.me/DataScienceT

80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains
📌 Agriculture and Food
📌 Medical and Healthcare
📌 Satellite
📌 Security and Surveillance
📌 ADAS and Self Driving Cars
📌 Retail and E-Commerce
📌 Wildlife

Classification library
https://github.com/Tessellate-Imaging/monk_v1

Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo

Detection and Segmentation Library
https://github.com/Tessellate-Imaging/

Monk_Object_Detection
Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo

✅ More ♥️♥️ = more posts

@CodeProgrammer ♥️

Don't Forget join to another channel
https://t.me/DataScienceT

Google just dropped Generative AI learning path with 9 courses:

🤖: Intro to Generative AI
🤖: Large Language Models
🤖: Responsible AI
🤖: Image Generation
🤖: Encoder-Decoder
🤖: Attention Mechanism
🤖: Transformers and BERT Models
🤖: Create Image Captioning Models
🤖: Intro to Gen AI Studio

🌐 Link: https://www.cloudskillsboost.google/paths/118

https://t.me/DataScienceT

Get started in Data Science with Microsoft's FREE course for beginners.

- 10 weeks
- 20 lessons
- Lecture notes
- 100% FREE

https://microsoft.github.io/Data-Science-For-Beginners/

https://t.me/DataScienceT

Building Transformer Models with Attention Crash Course. Build a Neural Machine Translator in 12 Days

https://machinelearningmastery.com/building-transformer-models-with-attention-crash-course-build-a-neural-machine-translator-in-12-days/

Please react with our posts 👍
@CodeProgrammer ♥️

Don't Forget join to another channel
https://t.me/DataScienceT

Please move to our new channel

Current channel @datascience_books is banned 😔
t.me/DataScienceM

Automatically find issues in image datasets and practice data-centric computer vision.

CleanVision automatically detects potential issues in image datasets like images that are: blurry, under/over-exposed, (near) duplicates, etc. This data-centric AI package is a quick first step for any computer vision project to find problems in the dataset, which you want to address before applying machine learning. CleanVision is super simple -- run the same couple lines of Python code to audit any image dataset!

https://github.com/cleanlab/cleanvision

Please move to our new channel

Current channel @datascience_books is banned 😔
t.me/DataScienceM