Ranking Tweets with TensorFlow

Blog by Yi Zhuang, Arvind Thiagarajan, and Tim Sweeney: https://lnkd.in/eiNseET

#MachineLearning #TensorFlow #Twitter

✴️ @AI_Python_EN

The evolution of art through the lens of deep convolutional networks

“The Shape of Art History in the Eyes of the Machine”, Elgammal et al.: https://lnkd.in/dgjmqYc

#art #artificialintelligence #deeplearning

✴️ @AI_Python_EN

Training my model
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python

This is a fun application of the superres method from fastdotai lesson 7 - turning line drawings into shaded pictures! https://forums.fast.ai/t/share-your-work-here/27676/1204

✴️ @AI_Python_EN

Check out new blog post on Coconet 🥥, the #ml behind the Bach Doodle that’s live now! It’s a flexible infilling model that generates counterpoint through rewriting. http://g.co/magenta/coconet

✴️ @AI_Python_EN

PhD thesis Neural Transfer Learning for Natural Language Processing is now online. It includes a general review of #transferlearning in #NLP as well as new material that I hope will be useful to some. http://ruder.io/thesis/

✴️ @AI_Python_EN

Let's learn more about this amazing #YOLO framework - a supremely fast and accurate framework for object detection. Let's explore YOLO, know why we should use it over other object detection algorithms, the different techniques used by YOLO and then let's implement it in #Python. It’s the ideal guide to gain invaluable knowledge and then apply it in a practical hands-on manner. https://bit.ly/2uq7n9y

✴️ @AI_Python_EN

Learn:

1. linear algebra well (e.g. matrix math)
2. calculus to an ok level (not advanced stuff)
3. prob. theory and stats to a good level
4. theoretical computer science basics
5. to code well in Python and ok in C++

Then read and implement ML papers and *play* with stuff! :-)

Shane Legg

✴️ @AI_Python_EN

CS294-158 Deep Unsupervised Learning Spring 2019

About: This course covers two areas of deep learning in which labeled data is not required: Deep Generative Models and Self-supervised Learning.

Video lectures: https://lnkd.in/eq6ZKAn

#artificialintelligence #deeplearning #generativemodels

✴️ @AI_Python_EN

"AI Needs Better Data, Not Just More Data"

> https://lnkd.in/gR5E7Re

#AI #ArtificialIntelligence #MI #MachineIntelligence
#ML #MachineLearning #DataScience #Analytics
#Data #BigData #IoT #4IR #DataPedigree
#Veracity #Trust #DataQuality #BetterData

✴️ @AI_Python_EN

Data Visualization is a very important step in #DataScience, so we should try to MASTER it.

Here are the useful links for #DataVisualization -

1)Quick and Easy Data Visualizations in Python with Code.
(https://lnkd.in/fXJ-_Y8)

2)10 Useful #Python Data Visualization Libraries for Any Discipline.
(https://lnkd.in/fBxbHwr)

3)Top 50 matplotlib Visualizations – The Master Plots (with full python code).
(https://lnkd.in/fGrnGax)

4)Data Visualization Effectiveness Profile.
(https://lnkd.in/f3v52Fd)

5)The Visual Perception of Variation in Data Displays.
(https://lnkd.in/fm-TbPM)

6)Matplotlib Tutorial – A Complete Guide to Python Plot w/ Examples.
(https://lnkd.in/fFkUgQP)

7)Interactive Data Visualization in Python With Bokeh.
(https://lnkd.in/fEfQAvg)

✴️ @AI_Python_EN

Curated list of awesome ****DEEP LEARNING**** tutorials, projects and communities.



Github Link - https://lnkd.in/fJdpFMn

#deeplearning #machinelearning #datascience #منابع

✴️ @AI_Python_EN

Here are 25 awesome #deeplearning datasets handpicked by our team! We have divided them into 3 categories: Image Processing, Natural Language Processing (#NLP) and Audio/Speech Processing.

https://bit.ly/2DrzUAM

✴️ @AI_Python_EN

Deep Classifiers Ignore Almost Everything They See (and how we may be able to fix it)

Blog by Jorn Jacobsen: https://lnkd.in/eNZt5mn

#MachineLearning #ArtificialIntelligence #ComputerVision #DeepLearning

✴️ @AI_Python_EN

Monte Carlo Neural Fictitious Self-Play: Achieve Approximate Nash equilibrium of Imperfect-Information Games

Zhang et al.: https://lnkd.in/eJQYNkD

#artificialintelligence #deeplearning #reinforcementlearning #selfplay

✴️ @AI_Python_EN

Visualizing memorization in RNNs

Blog by Andreas Madsen: https://lnkd.in/dAnHYcx

#artificialintelligence #deeplearning #machinelearning

✴️ @AI_Python_EN

This book is written for persons of no-maths-background, specially IT employees,students of class 9th or more;
and also useful for non-techie business analyst and business leaders (as APIs for Face, Speech and chatbots are discussed)
This book is for beginners of deep learning,TensorFlow, Keras, Speech recognition, Face recognition and chatbot.

Chapter0:_Prerequisites of Deep Learning Numpy, Pandas and Scikit-Learn

Chapter1_Basics of Tensorflow

Chapter2_Understanding and working on Keras

Chapter 3: MultiLayer Perceptron

Chapter4_Regresson to MLP in Tensorflow

Chapter5_Regression to MLP in Keras

Chapter6_ CNN with visuals

Chapter7_CNN with Tensorflow

Chapter8_CNN with Keras

Chapter9_RNN and LSTM in visual

Chapter10_Speech to text and vice versa

Chapter11_Developing Chatbots

Chapter12_Face Recognition

Github: https://lnkd.in/fH-SjSV
Book: https://lnkd.in/fwf2aiv

Please recommend to school going students as well.
Need feedback regarding how we can make the book more lucid.
#deeplearning #democratization #ai

✴️ @AI_Python_EN

How comfortable are you working on #UnsupervisedLearning problems? Here are 5 comprehensive tutorials to help you learn this critical topic:

1. An Introduction to #Clustering and it's Different Methods - https://bit.ly/2Fwykil

2. Exploring Unsupervised #DeepLearning #Algorithms for #ComputerVision - https://bit.ly/2HPWV39

3. Introduction to Unsupervised Deep Learning (with #Python codes) - https://bit.ly/2HDkMUA

4. Essentials of
#MachineLearning Algorithms (with Python and R Codes) - https://bit.ly/2TQjJWW

5. An Alternative to Deep Learning? Guide to Hierarchical Temporal Memory (HTM) for Unsupervised Learning - https://bit.ly/2JzcyOR


✴️ @AI_Python_EN

New work yao qin, Nicholas Carlini, ian good fellow, and Gary Cottrell on generating imperceptible, robust, and targeted adversarial examples for speech recognition systems! Paper: https://arxiv.org/abs/1903.10346 Audio samples: http://cseweb.ucsd.edu/~yaq007/imperceptible-robust-adv.html

✴️ @AI_Python_EN

Knowledge Graphs: The Third Era of Computing

Blog by Dan McCreary: https://lnkd.in/epZU-Yi

#MachineLearning #KnowledgeGraphs #AI #ProceduralProgramming

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN