Group For Who Have a Passion For:

1. Artificial Intelligence
2. Machine Learning
3. Deep Learning
4. Data Science
5. Computer vision
6. Image Processing

https://t.me/joinchat/Ly1-vFOq9aR4mjpIDwzoHA

✴️ @AI_Python_EN

Discovering Neural Wirings (https://lnkd.in/dQ3aauz)
In the past years developing deep neural architectures either required manual design (e.g. AlexNet, ResNet, MobileNet, ...) or required expensive search among possible predefined block structures of layers (NAS, MNas, DART,...). What if we see a neural network as a completely unstructured graph? where each node is running a simple sensing operation over a single data-point or a channel (e.g. 2d filter) and all the nodes are wired up massively in the network. In this paper we explain how to discover a good wiring of a neural network that minimizes the loss function with a limited amount of computation. We relax the typical notion of layers and instead enable channels to form connections independent of each other. This allows for a much larger space of possible networks. The wiring of our network is not fixed during training – as we learn the network parameters we also learn the structure itself.

✴️ @AI_Python_EN

Convolutional Neural Networks cheatsheet By Afshine Amidi and Shervine Amidi

https://lnkd.in/dxEcGFf

#deeplearning

✴️ @AI_Python_EN

100 Plus data Visualizations explained with examples - DataVizProject
https://datavizproject.com/

✴️ @AI_Python_EN

If you're following machine learning, statistics, artificial intelligence posts and even memes, you would have seen usually people sharing comments like machine learning is nothing but bunch of if-else statements and remove your mask - machine learning: I'm just fancy statistics.

Statistics is the key to understand and work with machine learning models. It is the grammar of data science.

You want to understand more about the important concepts? Found a great series of posts which discusses about the important concepts. Use the below link.

Link - https://lnkd.in/f53FP2H

✴️ @AI_Python_EN

DeepMind Made a Math Test For Neural Networks

https://www.youtube.com/watch?v=f9z1I_81_Q4

✴️ @AI_Python_EN

Artificial Intelligence Full Course | Artificial Intelligence Tutorial for Beginners

https://www.youtube.com/watch?v=JMUxmLyrhSk

✴️ @AI_Python_EN

Learning Perceptually-Aligned Representations via Adversarial Robustness

Article: https://arxiv.org/abs/1906.00945
Github: https://github.com/MadryLab/robust_representations

✴️ @AI_Python_EN

Introducing TensorNetwork, an Open Source Library for Efficient Tensor Calculations

http://ai.googleblog.com/2019/06/introducing-tensornetwork-open-source.html

✴️ @AI_Python_EN

Free COURSE. CS Deep Reinforcement Learning UC Berkeley

Video Lectures: https://www.youtube.com/playlist?list=PLkFD6_40KJIxJM..

Lecture Material: http://rail.eecs.berkeley.edu/deeprlcourse/

✴️ @AI_Python_EN

Welcome to TensorWatch

TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key visualizations of your models and data.

https://github.com/microsoft/tensorwatch/

✴️ @AI_Python_EN

Group For Who Have a Passion For:

1. Artificial Intelligence
2. Machine Learning
3. Deep Learning
4. Data Science
5. Computer vision
6. Image Processing

https://t.me/joinchat/Ly1-vFOq9aR4mjpIDwzoHA

✴️ @AI_Python_EN

ice collection of PyTorch (and some TF) Jupyter notebooks for everything deep learning by Sabastian Raschka. https://github.com/rasbt/deeplearning-models

✴️ @AI_Python_EN

Nice to see our World Models paper used to teach a lecture on Representation Learning in Reinforcement Learning as part of Berkeley’s course on Deep Unsupervised Learning.

They described the paper as “the simplest thing that can be done. I wouldn’t have expected it to work so well.” 🍰

https://lnkd.in/gjH3gHU
✴️ @AI_Python_EN

ML Resources

By Sam Finlayson: https://lnkd.in/dArtQB8

#ArtificialIntelligence #DeepLearning #MachineLearning

✴️ @AI_Python_EN

We are back with a new blog post for our PyTorch Enthusiasts. If you are new to this field, Semantic Segmentation might be a new word for you.
Simply put it is an image analysis task used to classify each pixel in the image into a class which is exactly like solving a jigsaw puzzle and putting the right pieces at the right places!

Today's blog by Arunava Chakraborty is about Semantic Segmentation using torchvision and will help explore more about this interesting topic.

https://lnkd.in/gG5fW3M

#Semantic #segmentation #torchvision #PyTorch #ai #deeplearning #machinelearning #computervision #opencv

✴️ @AI_Python_EN

If you are into statistical analysis, don't miss this paper on variable selection!

#statistical_analysis #regression #variable_selection #model_building #epidemiology
https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.201700067
✴️ @AI_Python_EN

MelNet: A Generative Model for Audio in the Frequency Domain

Sean Vasquez and Mike Lewis: https://lnkd.in/dp36Nwk

Blog: https://lnkd.in/dnEacxY

#ArtificialIntelligence #DeepLearning
#MachineLearning

✴️ @AI_Python_EN

Watch the Mona Lisa turn her head, in today's edition of stupid StyleGAN encoder tricks... #StyleGAN https://github.com/pbaylies/stylegan-encoder

Can we learn to detect objects without any supervision? Yes, if we assume that an object is a part of an image that can be redrawn while keeping the image realistic. With Mickael Chen and Thierry Artieres - https://arxiv.org/abs/1905.13539

✴️ @AI_Python_EN

Deep reinforcement learning algorithms are impressive, but only when they work. In reality, they are largely unreliable and can yield very different results. larocheromain proposes two ways to achieve reliability in RL: https://aka.ms/AA5ann9 #ICML2019