***What is pip***

A Guide for New Pythonistas.

Credits - Isaac Rodriguez

Link - https://lnkd.in/fPf8MWZ

#python #pythonprogramming

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****Web Scraping in Python****

#DataScience #DataMining #WebMining #WebScraping

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120 Machine Learning business ideas from the latest McKinsey report

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning

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Listen to Bob Ross. Bob Ross is never wrong. #AIFun

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Andriy Burkov
I often receive questions from people in my network about what should they learn and master to become a data scientist. While I personally think that the term "data scientist" is very unfortunate and without a clear definition, this is what a good modern #dataanalyst has to master:
#DataScience

– Data structures (local and distributed)
– Data indexing
– Data privacy and anonymization
– Data lifecycle management
– Data transformation (deduplication, handling outliers, and missing values, dimensionality reduction)
– Data analysis (experiment design, classification, regression, unsupervised methods)
– #Machinelearning methods (feature engineering, regularization, hyperparameter tuning, ensemble methods, and #neuralnetwork s)
– Computer and database programming, numerical optimization
– Distributed data processing
– Real-time and high-frequency data processing
– Linux (my personal bias)

A modern data analyst also has to be a good popularizer of complex ideas. Having a Ph.D. is not a requirement, but a very big plus: it contributes to the popularizing skill and teaches the scientific approach to problem-solving.

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San Francisco became the first major U.S. city to ban the use of facial recognition technology by police and other municipal agencies

https://www.nytimes.com/2019/05/14/us/facial-recognition-ban-san-francisco.html?smtyp=cur&smid=tw-nytimes

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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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Build a chat widget with Python and JavaScript
http://bit.ly/2JnD8d0

#python #javascript #development

http://bit.ly/2JI78jc

✴️ @AI_Python_EN

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Natural Language Processing with Deep Learning in Python ☞ http://bit.ly/2HlcwXV #DeepLearning #TensorFlow

Machine vision is the newest weapon against crop loss
https://zd.net/2Vq1AvV
#ai #ArtificialIntelligence #farming

✴️ @AI_Python_EN

Accelerating quantum technologies with materials processing at the atomic scale #quantum #QuantumComputing
https://t.co/mHuuywfESG
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A 2019 guide to 3D Human Pose Estimation
https://blog.nanonets.com/human-pose-estimation-3d-guide/
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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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Chris Lattner: Compilers, LLVM, Swift, TPU, and ML Accelerators | Artificial Intelligence Podcast
https://www.youtube.com/watch?v=yCd3CzGSte8

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How to tell whether machine-learning systems are robust enough for the real world
http://news.mit.edu/2019/how-tell-whether-machine-learning-systems-are-robust-enough-real-worl-0510


#MachineLearning

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Deep Learning Determinism

🌎 Deep Learning
🌎 This is a talk from GTC 2019 in San Jose, California. Slides: http://bit.ly/dl-determinism-slides
#DeepLearning

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MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations has been accepted as a long paper at #ACL2019. With D. Hazarika, N. Majumder, G. Naik, E. Cambria,.
Arxiv - https://arxiv.org/abs/1810.02508
Dataset -
https://affective-meld.github.io

✴️ @AI_Python_EN

Discover 3D graphics capabilities for #TensorFlow >> https://github.com/tensorflow/graphics … | #DeepLearning

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Deep Learning: GANs and Variational Autoencoders ☞ http://bit.ly/2JdVTP3 #tensorflow #ai

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Expression Conditional GAN for Facial Expression-to Expression Translation https://arxiv.org/pdf/1905.05416.pdf

✴️ @AI_Python_EN