What metrics to focus on, in confusionmatrix?
Ans-> It depends on the problem statement and data one is dealing with!

#examples
1) Spam Filter -> Consider +ve class as 'spam'. Optimize for Precision/Specificity, the reason for the same is...
False Negatives(spam emails in the primary) are more acceptable than False Positives(primary emails in Spam).

2) Fraud Transactions -> Consider +ve class as 'Fraud'. Optimize for Sensitivity, the reason for the same is...
False Positives(detecting fraud, but they are not) are more acceptable than False Negatives(detecting not as a fraud, but actually they are).

Will discuss more of Classification problem in our group

#datascience #machinelearning

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https://youtu.be/9M18rc9-VWU

https://github.com/yuanming-hu/taichi_mpm

✴️ @AI_Python_EN

Implementation of Variational Auto-Encoder (VAE) and Deep Feature Consistent VAE for facial attribute manipulation using Keras and Tensorflow-dataset module.

https://github.com/iamsoroush/face_vae

✴️ @AI_Python_EN

A Full Hardware Guide to Deep Learning

🌎 http://bit.ly/2CBNi3W

#DeepLearning #MachineLearning #AI #DataScience

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Is this AI developing a sense of time?

https://www.zdnet.com/article/is-this-ai-developing-a-sense-of-time/

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https://arxiv.org/abs/1907.02544 show that GANs can be harnessed for unsupervised representation learning, with state-of-the-art results on ImageNet. Reconstructions, as shown below, tend to emphasise high-level semantics over pixel-level details.
These results showcase the potential of GANs and other generative models in unsupervised learning, as explained in our recent blog post:
https://deepmind.com/blog/unsupervised-learning/

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BigBiGAN shows that "progress in image generation quality translates to substantially improved representation learning performance." Competitive w/self-supervised approaches on ImageNet. The cycle from generative models to other methods and back again continues.

Large Scale Adversarial Representation Learning. Jeff Donahue and Karen Simonyan http://arxiv.org/abs/1907.02544

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Excited to share newest http://fast.ai
course: A Code-First Introduction to Natural Language Processing All code & videos are available for free online, please check it out!

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Ladies, if he:

- requires lots of supervision
- yet always wants more power
- can't explain decisions
- optimizes for the average outcome
- dismisses problems as edge cases
- forgets things catastrophically

He's not your man, he's a deep neural network. #AIFun

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Interesting work from Ross Wightman comparing something like EfficientNet / ResNet which uses only Imagenet data to the Facebook-IG ResNext that was trained on a lot of instagram public data. While their validation scores are close, the test scores seem to diverge more.

FacebookAI ResNeXt models pre-trained on Instagram hashtags stand out in their ability to generalized to the 'ImageNetV2' test set.
#PyTorch
https://colab.research.google.com/github/rwightman/pytorch-image-models/blob/master/notebooks/GeneralizationToImageNetV2.ipynb

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Towards Declarative Visual Reasoning… Or Not? https://buff.ly/2UkT3u4
#NeuralNetworks #DeepLearning #AI

Neural Architecture Search at CVPR 2019

https://drsleep.github.io/NAS-at-CVPR-2019/

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Anyone working with data should be concerned about missing data...or they shouldn't be working with data.

One way to address missing data is multiple imputation. This is a complex topic, and many textbooks and articles have been published about it. Four I can recommend are:

- Applied Missing Data Analysis (Enders)
- Handbook of Missing Data Methodology (Molenberghs et al.)
- Flexible Imputation of Missing Data (van Buuren)
- Outlier Analysis (Aggarwal)

The Enders book is probably the most basic and readable of the ones I've listed.

The Stata 16 reference manual on multiple imputation, linked below, also provides a good overview of this subject, and includes many examples and a glossary of key terms.

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An Awesome Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks.

Authors here present a framework for compactly summarizing many recent results in efficient and/or biologically plausible online training of recurrent neural networks (RNN).

Their framework organizes algorithms according to several criteria:
(a) past vs. future facing,
(b) tensor structure,
(c) stochastic vs. deterministic, and
(d) closed form vs. numerical.

These axes reveal latent conceptual connections among several recent advances in online learning.

Paper: https://lnkd.in/dTcMbyK

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Deep Learning For Real Time Streaming Data With Kafka And Tensorflow


#DeepLearning #Tensorflow
https://www.youtube.com/watch?v=HenBuC4ATb0

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"A machine capable of learning? That sounds wonderful." Learn how heroes of #deeplearning Yann LeCun and Ruslan Salakhutdinov first became interested in #AI:

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For Who Have a Passion For:

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

Link Group:
@DeepLearningML

We need your help to oppose the HR1044. This petition is made by the Chinese community in the US. As immigrants they believe the HR1044 will hurt the economy and skew the immigration toward one nation and only one which is India. Please Share it With your Groups and Friends.🌺

https://petitions.whitehouse.gov/petition/do-not-pass-bills-s386-or-hr1044-do-not-pass-bills-s386-or-hr1044-us-worker-and-racial-diversity

10 new posts on datahacker.rs.
Introduction to #ComputerVision using #OpenCV (#Python and #C++)

https://lnkd.in/gZtj_g6

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Welcome to @ai_machinelearning_big_data the world of :
* #Artificial #Intelligence,
* #Deep #Learning,
* #Machine #Learning,
* #Data #Science
* #Python Programming language
* and more advanced research
links🔗 and more you wanted.
Join us and learn hot topics of Computer Science together.👇👇👇

@ai_machinelearning_big_data

Python is the main trend in programming IT and technologies .The most actual information about python programming, big data , machine learning , neural networks on channel : @pythonl