#DeepLearning: What Is It Good For? - Prof. Ankit Patel Rice University [Video]
https://buff.ly/2U1irV9
#AI #MachineLearning

✴️ @AI_Python_EN

Psychometricians make use of a very wide range of statistical models, for example:

Regression analysis
Path analysis
Exploratory factor analysis
Confirmatory factor analysis
Item response theory modeling
Structural equation modeling
Growth modeling
Discrete-time survival analysis
Continuous-time survival analysis
Time series analysis

Observed outcome variables may be continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types.

Some variations of these basic models are:

Regression mixture modeling
Path analysis mixture modeling
Latent class analysis
Latent class analysis with covariates and direct effects
Confirmatory latent class analysis
Latent class analysis with multiple categorical latent variables
Loglinear modeling
Multiple group analysis
Multilevel modeling
Finite mixture modeling
Complier Average Causal Effect (CACE) modeling
Latent transition analysis and hidden Markov modeling including mixtures and covariates
Latent class growth analysis
Discrete-time survival mixture analysis
Continuous-time survival mixture analysis

Over the years, I've used many of these models in my marketing-related work. They all have practical applications in many fields - it ain't just academic stuff.

✴️ @AI_Python_EN

A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019). This is a lightweight graph convolutional neural network for the fast calculation of approximate graph similarity at scale. Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound.

https://lnkd.in/gA5tfuC

#datamining #machinelearning #deeplearning #datascience #bigdata

✴️ @AI_Python_EN

Great Statistical software for Beginners.

Here is the Gretl Tutorial by Simone Gasperin

1)Simple Linear Regression
https://lnkd.in/ecfsV9c

2)Coding Dummy Variables
https://lnkd.in/ef7Yd7f

3)Forecasting New Observations
https://lnkd.in/eNKbxbU

4)Forecasting a Large Number of Observations
https://lnkd.in/eHmibGs

5)Logistic Regression
https://lnkd.in/eRfhQ87

6)Forecasting and Confusion Matrix
https://lnkd.in/eaqrFJr

7)Modeling and Forecasting Time Series Data
https://lnkd.in/e6fqKpF

8)Comparing Time Series Trend Models
https://lnkd.in/eKjEUAE

#datascience #machinelearning #statistics #dataanalytics #dataanalysis

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

Is this AI developing a sense of time?

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

✴️ @AI_Python_EN

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/

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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!

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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/

✴️ @AI_Python_EN

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.

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

Deep Learning For Real Time Streaming Data With Kafka And Tensorflow


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

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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