You can now download and watch all paid data science courses for free by subscribing to our new channel

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GP-UNIT - Official PyTorch Implementation

🖥 Github: https://github.com/williamyang1991/gp-unit

⏩ Paper: https://arxiv.org/pdf/2306.04636v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/celeba-hq

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🧔 4DHumans: Reconstructing and Tracking Humans with Transformers

Fully "transformerized" version of a network for human mesh recovery.

🖥 Github: https://github.com/shubham-goel/4D-Humans

⭐️ Colab: https://colab.research.google.com/drive/1Ex4gE5v1bPR3evfhtG7sDHxQGsWwNwby?usp=sharing

📕 Paper: https://arxiv.org/pdf/2305.20091.pdf

🔗 Project: https://shubham-goel.github.io/4dhumans/

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A channel for educational Python courses

All courses here are free and available to everyone

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⚡ 21 Must-Have Cheat Sheets for Data Science Interviews: Unlocking Your Path to Success

▪SQL

1. SQL Basics Cheat Sheet
2. The Essential SQL Commands Cheat Sheet for Beginners
3. SQL Cheat Sheet – Technical Concepts for the Job Interview

▪Python

4. Python Cheat Sheet
5. Python Cheat Sheet
6. Comprehensive Python Cheatsheet

▪R

7. RStudio Cheatsheets

▪Data Structures

8. Data Structures Reference
9. An Executable Data Structures Cheat Sheet for Interviews

▪Data Manipulation

10. Pandas Cheat Sheet for Data Science
11. Pandas Cheat Sheet
12. Data Wrangling With pandas Cheat Sheet

▪Data Visualization

13. Data Visualization Cheat Sheet
14. Data Visualization Cheat Sheet
15. Data Visualization Cheat Sheets

▪Statistics & Probability

16. A Comprehensive Statistics Cheat Sheet for Data Science Interviews
17. The Most Comprehensive Stats Cheat Sheet
18. Statistics Cheat Sheet

▪Algorithms & Models

19. Top Prediction Algorithms
20. Your Ultimate Data Science Statistics & Mathematics Cheat Sheet
21. Cheat Sheet for Machine Learning Models

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Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement
at 100k Steps-Per-Second

🖥 Github: https://github.com/facebookresearch/galactic

⏩ Paper: https://arxiv.org/pdf/2306.07552v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/vizdoom

https://news.1rj.ru/str/DataScienceT

Semi-supervised learning made simple with self-supervised clustering [CVPR 2023]

🖥 Github: https://github.com/pietroastolfi/suave-daino

⏩ Paper: https://arxiv.org/pdf/2306.07483v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/imagenet

https://news.1rj.ru/str/DataScienceT

🌐 WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings

🖥 Github: https://github.com/poloclub/wizmap

⭐️ Colab: https://colab.research.google.com/drive/1GNdmBnc5UA7OYBZPtHu244eiAN-0IMZA?usp=sharing

📕 Paper: https://arxiv.org/abs/2306.09328v1

🔗 Web demo: https://poloclub.github.io/wizmap.

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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

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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

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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

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📌 LOMO: LOw-Memory Optimization

New optimizer, LOw-Memory Optimization enables the full parameter fine-tuning of a 7B model on a single RTX 3090, or a 65B model on a single machine with 8×RTX 3090, each with 24GB memory.

🖥 Github: https://github.com/OpenLMLab/LOMO/tree/main

📕 Paper: https://arxiv.org/pdf/2306.09782.pdf

🔗 Dataset: https://paperswithcode.com/dataset/superglue

https://news.1rj.ru/str/DataScienceT

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX

Jumanji is helping pioneer a new wave of hardware-accelerated research and development in the field of RL.

🖥 Github: https://github.com/instadeepai/jumanji

📕 Paper: https://arxiv.org/abs/2306.09884v1

🔗 Dataset: https://paperswithcode.com/dataset/mujoco

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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

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