Deep Learning Project Building with Python and Keras ☞ http://bit.ly/2HADriH #DeepLearning #ai

✴️ @AI_Python_EN

A Step-by-Step Guide to Machine Learning Problem Framing: Diving into Machine Learning (ML) without knowing what you’re trying to achieve is a recipe for disaster. #MachineLearning #DeepLearning #DataScience

https://medium.com/thelaunchpad/a-step-by-step-guide-to-machine-learning-problem-framing-6fc17126b981

✴️ @AI_Python_EN

Rust: Programming Language Cheat Sheet.
#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #Rust #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #Linux #Programming

http://bit.ly/2HEVJzl

✴️ @AI_Python_EN

It was interesting to work on classifying duplicate questions on quora. Just uploaded the code to git.
Approach 1: Siamese Network with manhattan distance as the objective function.
Code: https://lnkd.in/fhzV_HU

Approach 2: XGBoost + TF-iDF + NLP feature engineering.
Code: https://lnkd.in/f3zqm37

Competition: https://lnkd.in/fYHtwJq

✴️ @AI_Python_EN

Deep Learning Drizzle

"Read enough so you start developing intuitions and then trust your intuitions and go for it!" - Geoffrey Hinton

By Marimuthu K.: https://lnkd.in/e6BBDVJ

#artificialintelligence #deeplearning #machinelearning

✴️ @AI_Python_EN

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



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

#deeplearning #machinelearning #datascience

✴️ @AI_Python_EN

This guide gives a complete understanding about various #machinelearning algorithms along with R & Python #codes to run them. These #algorithms can be applied to any data problem:
Linear Regression,
Logistic Regression,
Decision Tree,
SVM,
Naive Bayes,
kNN,
K-Means,
#Random Forest.
If you are keen to master machine learning, start right away.

Link : bit.ly/2CpWIjH

#machinelearning #deeplearning #python #coding #linkedin #decisiontrees #logisticregression #linearregression #forest #analytics #randomization #computervision

✴️ @AI_Python_EN

😢
❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN

Have you heard of SuperTML?

Two-Dimensional Word Embedding and Transfer Learning Using ImageNet Pretrained CNN Models for the Classifications on Tabular Data


SuperTML: Two-Dimensional Word Embedding and Transfer Learning Using ImageNet Pretrained CNN Models for the Classifications on Tabular Data

Tabular data is the most commonly used form of data in industry. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data.

DNN models using categorical embeddings are also applied in this task, but all attempts thus far have used one-dimensional embeddings. The recent work of Super Characters method using two-dimensional word embeddings achieved the state of art result in text classification tasks, showcasing the promise of this new approach.

The SuperTML method, which borrows the idea of Super Characters method and two-dimensional embeddings to address the problem of classification on tabular data. It has achieved state-of-the-art results on both large and small datasets.


Here’s the paper: https://lnkd.in/djGFf63

❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN

Data-Driven Careers Dechipered


Seems Different with what I believed, but still worth it to discuss

#business #technology #datascience

❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN

Yann LeCun Tweeted:

I gave two talks in Harvard's Mind, Brain, & Behavior Distinguished Lecture Series last week.

The slides are here:
- 2019-03-13 "The Power and Limits of Deep Learning":...

https://t.co/Pp4flTGlZ8

✴️ @AI_Python_EN

✴️ @AI_Python_EN

Our Computer Vision and Deep Learning Group:

https://t.me/joinchat/ECtp7VVFvEwjIrMrdgI-2w

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
❇️ @AI_Python

Many misunderstandings persist regarding logistic regression (LR).

Though it can be used for classification, it is not a classification method. Its predictions are model-based estimates of the probabilities of group/class membership.

Cutoffs can be drawn anywhere that are meaningful to decision makers, not just at probability = .50. Automatically using the .50 cutoff - the default for most LR programs - is a mistake, especially when group/class sizes are highly imbalanced.

Relationships between the predictors (independent variables) and outcome (dependent variable) do not have to be "linear" - any sort of relationship, including curvilinear and moderated relationships (interactions), can be modeled.

Binary LR is just one member of the GLM family. There are also LR models for multinomial, ordinal and count data, as well as probit analysis.

More advanced extensions include multilevel and mixture models, and SEM with multiple categorical outcomes. There are also Bayesian alternatives to maximum likelihood estimation.

BIG topic, with very practical implications for marketing research, data science and many other fields. I'm still learning about it.

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
❇️ @AI_Python

A Brief History of Data Science (Pre-2010, i.e. prior to rise of deep learning & popular usage of the term "data science")
#
Note: Modified original version of infographic to add 3 seminal developments in the history of Artificial Intelligence:

- 1943: Artificial neuron model (McCulloch & Pitts)
- 1950: Turing Test (Alan Turing)
- 1956: Dartmouth Conference (McCarthy, Minsky, Shannon)

#datascience #statistics #analytics #machinelearning #bigdata #artificialintelligence #innovation #technology #history #ai #datamining #informatics #infographics #informationtechnology #computerscience #dataanalysis #deeplearning #neuroscience #mathematics #science

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
❇️ @AI_Python

Comprehensive Collection of #DataScience and #MachineLearning Resources for #DataScientists includes “Great Articles on Natural Language Processing” +much more 👉https://bit.ly/2nvMXIx #abdsc #BigData #AI #DeepLearning #Databases #Coding #Python #Rstats #NeuralNetworks #NLProc

✴️ @AI_Python_EN

Coloring massive graphs is a notoriously difficult problem to solve…Deep reinforcement learning to the rescue! Jiayi Huang et al use a novel architecture (FastColorNet) to learn new state of the art heuristics for graph coloring: https://lnkd.in/gcE8cWz #TechRec

✴️ @AI_Python_EN

Statisticians often refer to observed variables and latent variables. Put simply, an observed variable is what we have in our data file. It may be useful as is but, in fields such as marketing research, may be an indicator of a latent variable, often called a factor, which we cannot directly observe. In a more familiar context, a high fever may be an indication of influenza, which our doctor cannot observe directly.

The distinction between the two has practical importance. For instance, if a survey respondent selects Convenience as one of the reason she shops at Chain X, it could indicate many things, for example, that the store is physically nearby, that parking is easy, that checkout is fast, or that the store layout makes it easy to find what she wants.

In questionnaire design and when analyzing survey data, marketing researchers frequently confuse observed and latent variables - "Effective" is another example. We tend to zero in on specific items or statements when we should be thinking about the constructs they represent. Also, the questions we ask may be confusing or meaningless to respondents because of this.

It's best to think about these issues when designing our research and planning our analytics.

✴️ @AI_Python_EN

How to Combine Neural Networks and Decision Trees

#NeuralNetworks #decisiontree

✴️ @AI_Python_EN

While #AI hype machine is in overdrive we are thrilled to see that #DeepLearning is making steady inroads into making lives of #opthalmologists easier.

We believe in this too and include practical and hands-on datasets in our own trainings so more engineers and doctors can work together to diagnose such diseases!

Diabetic retinopathy (DR), a major microvascular complication of diabetes, has a significant impact on the world's health systems. Globally, the number of people with DR will grow from 126.6 million in 2010 to 191.0 million by 2030.

In U.S alone, more than 29 million people have diabetes, and are at risk for diabetic retinopathy, a potentially blinding eye disease. People typically don't notice changes in their vision in the disease's early stages. But as it progresses, diabetic retinopathy usually causes vision loss that in many cases cannot be reversed. That's why it's so important that people with diabetes have yearly screenings.

Unfortunately, the accuracy of screenings can vary significantly. One study found a 49 percent error rate among internists, diabetologists, and medical residents. This is really bad!

Read Google's research https://lnkd.in/gDMW-fD
#artificialintelligence #diabeticretinopathy

✴️ @AI_Python_EN