MIT Introduction to Deep Learning

And specifically, lecture about RNN and its modifications:
https://youtu.be/qjrad0V0uJE

The course is excellent as well, but more about image processing. For NLP beginners, such clear and elegant survey about RNNs will be quite useful. So, a lot of architectures in NLP models came from image processing tasks. If you want to recap some theory or get understanding of basics of DL — strong recommendation!

@Machine_learn

Deep Reinforcement
Learning in Action #book @Machine_learn

LEAF: A Learnable Frontend for Audio Classification


http://ai.googleblog.com/2021/03/leaf-learnable-frontend-for-audio.html

@Machine_learn

Leveraging Machine Learning for Game Development


http://ai.googleblog.com/2021/03/leveraging-machine-learning-for-game.html

@Machine_learn

XLA: Optimizing Compiler for Machine Learning

Tensorflow: https://www.tensorflow.org/xla

XLA Architecture: https://www.tensorflow.org/xla/architecture

Github: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/xla

Code: https://www.tensorflow.org/xla/tutorials/jit_compile

@Machine_learn

سلام دوستان جهت کسب اطلاعات از نحوه خرید می تونین با بنده در ارتباط باشین
@Raminmousa

Recursive Classification: Replacing Rewards with Examples in RL


http://ai.googleblog.com/2021/03/recursive-classification-replacing.html

@Machine_learn

Ted Talk with Yann LeCun

in which Yann discusses his current research into self-supervised machine learning, how he's trying to build machines that learn with common sense (like humans) and his hopes for the next conceptual breakthrough in AI.

▶️ Watch

@Machine_learn

PlenOctrees For Real-time Rendering of Neural Radiance Fields

And yet another speed-up of NERF. Exactly the same idea as in FastNeRF and NEX (predict spherical harmonics coefficients k) - incredible! It's the first time I see so many concurrent papers sharig the same idea. But this one has code at least, which makes it the best!

📝 Paper arxiv.org/abs/2103.14024
🌐Project page alexyu.net/plenoctrees/
🛠Code github.com/sxyu/volrend


@Machine_learn

Data Analysis with python
#book #python

@Machine_learn

​​EfficientNetV2: Smaller Models and Faster Training

A new paper from Google Brain with a new SOTA architecture called EfficientNetV2. The authors develop a new family of CNN models that are optimized both for accuracy and training speed. The main improvements are:

- an improved training-aware neural architecture search with new building blocks and ideas to jointly optimize training speed and parameter efficiency;
- a new approach to progressive learning that adjusts regularization along with the image size;

As a result, the new approach can reach SOTA results while training faster (up to 11x) and smaller (up to 6.8x).

Paper: https://arxiv.org/abs/2104.00298

Code will be available here:
https://github.com/google/automl/efficientnetv2

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-effnetv2

@Machine_learn

500 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲

500 AI Machine learning Deep learning Computer vision NLP Projects with code

This list is continuously updated. - You can take pull request and contribute.

https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

@Machine_learn

Complete Python Bootcamp 2021.pdf
@Machine_learn

Fashion Meets Computer Vision: A Survey @Machine_learn

🧠 Lite-HRNet: A Lightweight High-Resolution Network

Github: https://github.com/HRNet/Lite-HRNet

Paper: https://arxiv.org/abs/2104.06403

@Machine_learn

Simple multi-dataset detection

Github: https://github.com/xingyizhou/UniDet

Paper: https://arxiv.org/abs/2102.13086v1

@Machine_learn

Monster Mash: A Sketch-Based Tool for Casual 3D Modeling and Animation


http://ai.googleblog.com/2021/04/monster-mash-sketch-based-tool-for.html

@Machine_learn

Flexible, Scalable, Differentiable Simulation of Recommender Systems with RecSim NG


http://ai.googleblog.com/2021/04/flexible-scalable-differentiable.html

@Machine_learn