This series will teach you how to use Keras, a neural network API written in Python. Each video focuses on a specific concept and shows how the full implementation is done in code using Keras and Python. We will learn how to preprocess data, organize data for training, validation and testing, build an artificial neural network from scratch, train an artificial neural network, build a convolutional neural network (CNN) and much more!

#Deep_Learning #Keras #python #Neural_Network

🌎 Link Review

🗣 @AI_Python_Arxiv
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The Eye In The Sky"

Satellite Image Classification using Semantic Segmentation

By Manideep Kolla, Apoorva Kumar, Aniket Mandle

GitHub repository: https://lnkd.in/eB2g-dX

#artificialintelligence #deeplearning #machinelearning #keras #tensorflow


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🗣 @AI_Python_arXiv
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Auto-Keras

Auto-Keras is an open source software library for automated machine learning (AutoML).

GitHub: https://lnkd.in/eJAKXy5

#keras #deeplearning #machinelearning


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🗣 @AI_Python_Arxiv

What is Reinforcement Learning?
Reinforcement Learning(RL): Type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.
Reinforcement learning uses rewards and punishment as signals for positive and negative behavior

Introduction to Reinforcement Learning: https://lnkd.in/gnywQgC

Build your own AI to play when you got on internet connection. The code is provided, try it yourself.

Article: https://lnkd.in/guARH7G
GitHub: https://lnkd.in/grkwSKs

#reinforcementlearning #machinelearning #deeplearning #python #keras #tensorflow

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🗣 @AI_Python_arXiv

Francesco Cardinale

I'm happy to announce that we just open-sourced a major update for our image super-resolution project: using an adversarial network and convolutional feature maps for the loss, we got some interesting results in terms realism and noise cancellation.
Pre-trained weights and GANs training code are available on GitHub!
If you want to read up about the process, check out the blog post.
Also, we released a pip package, 'ISR' (admittedly not the most creative name:D), with a nice documentation and colab notebooks to play around and experiment yourself on FREE GPU(#mindblown). Thanks to Dat Tran for the big help.

💻Blog: https://lnkd.in/dUnvXQZ
📝Documentation: https://lnkd.in/dAuu2Dk
🔤Github: https://lnkd.in/dmtV2ht
📕Colab (prediction): https://lnkd.in/dThVb_p
📘Colab (training): https://lnkd.in/diPTgWj

https://lnkd.in/dVBaKv4

#opensource #deeplearning #gans #machinelearning #keras

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TensorFlow is dead, long live TensorFlow!

#TensorFlow just went full #Keras! (!!!!!) Here's why that's an earthquake for #AI and #DataScience...

🌎 TensorFlow

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The best way to learn #DeepLearning is by practicing it. But which framework to use? Here are 5 articles to get you started!

A Comprehensive Introduction to #PyTorch - https://bit.ly/2L8Rj7n

Learn How to Build Quick & Accurate Neural Networks using PyTorch (& 4 Case Studies) - https://bit.ly/2Vts9nY

Get Started with Deep Learning using #Keras and #TensorFlow in #R - https://bit.ly/2Iro2BY

TensorFlow 101: Understanding Tensors and Graphs - https://bit.ly/2GNg195

An Introduction to Implementing #NeuralNetworks using TensorFlow - https://bit.ly/2V17cBs

✴️ @AI_Python_EN

A #Keras usage pattern that allows for maximum flexibility when defining arbitrary losses and metrics (that don't match the usual signature) is the "endpoint layer" pattern. It works like this: https://colab.research.google.com/drive/1zzLcJ2A2qofIvv94YJ3axRknlA6cBSIw
In short, you use add_loss/add_metric inside an "endpoint layer" that also has access to model targets. The layer then returns the inference-time predictions. You compile without an external "loss" argument, and you fit with a dictionary of data that contains the targets.
Of course logistic regression is a basic case that doesn't actually need this advanced pattern. But endpoint layers will work every time, even when you have losses & metrics that don't match the usual fn(y_true, y_pred, sampl_weight) signature that is required in compile.

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A Convolutional Neural Network Tutorial in Keras and Tensorflow 2

https://medium.com/@isakbosman/a-convolutional-neural-network-tutorial-in-keras-and-tensorflow-2-2bff79f477c0

#Keras #neuralnetwork #TensorFlow #ConvolutionalNeuralNetwork

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This is a neat project for reusable text generation with #Keras:

https://github.com/minimaxir/textgenrnn

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François Chollet

This is how you implement a network in Chainer. Chainer, the original eager-first #deeplearning framework, has had this API since launch, in mid-2015. When PyTorch got started, it followed the Chainer template (in fact, the prototype of PyTorch was literally a fork of Chainer).

Nearly every day, I am getting ignorant messages saying, "PyTorch is an original innovation that TensorFlow/Keras copied". This is incorrect. Subclassing is a fairly obvious way to do things in Python, and Chainer had this API first. Many others followed.

I had been looking at adding a Model subclassing API to Keras as soon as late 2015 (before the Functional API even existed, and over a year before being aware of PyTorch), inspired by Chainer. Our first discussions about adding an eager execution mode also predate PyTorch.

By the time #PyTorch came out, I had been looking at its API (which is exactly the Chainer API) for 1.5 year (since the release of Chainer). It wasn't exactly a shock. There was nothing we didn't already know.



To be clear, it's a good thing that API patterns and technical innovations are cross-pollinating among deep learning framework. The #Keras API itself has a had a pretty big influence over libraries that came after. It's completely fine, and it all benefits end users.

✴️ @AI_Python_EN

Keras notebooks


Material used for Deep Learning related workshops for Machine Learning Tokyo (MLT)

ConvNets: colab notebook with functions for constructing #keras models. Models:

AlexNet
VGG
Inception
MobileNet
ShuffleNet
ResNet
DenseNet
Xception
Unet
SqueezeNet
YOLO
RefineNet

https://github.com/Machine-Learning-Tokyo/DL-workshop-series

✴️ @AI_Python_EN

than standard Adam? - I ran 24 experiments to find out. - The answer? Meh, not really. Full tutorial w/ #Python code here:
http://pyimg.co/asash

#DeepLearning #Keras #MachineLearning #ArtificialIntelligence #AI #DataScience

❇️ @AI_Python_en

.

just published my (free) 81-page guide on learning #ComputerVision, #DeepLearning, and #OpenCV!
Includes step-by-step instructions on:
- Getting Started
- Face Applications
- Object Detection
- OCR
- Embedded/IOT
- and more!
Check it out here:
http://pyimg.co/getstarted
And if you liked it, please do give it a share to spread the word. Thank you!
#Python #Keras #MachineLearning #ArtificialIntelligence #AI

❇️ @AI_Python_EN

New tutorial! Traffic Sign Classification with #Keras and #TensorFlow 2.0

- 95% accurate
- Includes pre-trained model
- Full tutorial w/ #Python code

http://pyimg.co/5wzc5

#DeepLearning #MachineLearning #ArtificialIntelligence #DataScience #AI #computervision

❇️ @AI_Python_EN

Functional #RL with #Keras and #Tensorflow Eager

https://bit.ly/2IIyzIj

❇️ @AI_Python_EN