Data science is an ever-evolving field. As data scientists, we need to have our finger on the pulse of the latest algorithms and frameworks coming up in the community.

So, if you’re a:
Data science enthusiast
Machine learning practitioner
Data science manager
Deep learning expert

or any mix of the above, this article is for you.

more to read : https://bit.ly/2Jb2JoB

#machinelearning #datascience #deeplearning #deeplearning

✴️ @AI_Python_EN

Neural nets had been batting SVM for supremacy in data mining (aka data science) since the latter's rebirth thanks to the discovery of the kernel trick in the mid-90s. SVM seemed to be winning but then deep learning enjoyed its own resurrection and computing power began to make a wider variety of ANN architectures feasible. When I was a "kid", neural nets were mainly single layer MLP or Kohonen clustering (SOM). For what little it's worth, I see reinforcement learning as the key to true AI, though other forms of learning will continue to play a role.

#machinelearning #artificialintelligence

✴️ @AI_Python_EN

Open Source Resource for Newbies to Learn Algorithms and Implement them in any Programming Language.

*** The Algortithm ***

Github Link - https://lnkd.in/edw2vHj

#pythonprogramming #python #java #scala #c #cplusplus #csharp

✴️ @AI_Python_EN

check this collection of awesome deep Learning resources

https://github.com/frontbench-open-source/Data-Science-Free

#deeplearning

✴️ @AI_Python_EN

If you want to get into AI, what should you do?


Allie K. Miller give sharp and clear answer: "it completely depends"

This is decision tree where to start.

Follow the questions to find your first step to get a little closer to your AI goals, whatever they may be.

#artificialintelligence #AI #machinelearning

✴️ @AI_Python_EN

Some random thoughts on marketing mix modeling...

The basic purpose is to estimate the impact of a brand's marketing on its sales. It is also known as market response modeling and I've also heard the term sales response modeling used.

The simplest approach is to compare brands cross-sectionally and see how they differ in terms of sales and marketing activity. This is ill-advised because it does not account for how the brands' marketing and sales have changed over time.

Another popular way is to analyze (for example) four years of weekly sales and marketing data with multiple regression. The problem here is that the data are treated as cross-sectional whereas they are time-series data.

There are many kinds of time-series analysis which, properly conducted, can give us better estimates of the effects of marketing on sales. There are statistical considerations but one reason is that lagged effects can be estimated.

The problem with this approach is that marketing activity is assumed to be exogenous; in reality, marketing budgets and marketing plans are usually influenced by a brand's own sales and share, competitors' sales and share, competitors' marketing activity and by other exogenous factors such as the economy.

Whatever method we use, it must account for endogeneity.

Note: that these core issues apply far beyond mix modeling and even econometrics, where many of the methods we use in mix modeling originated. (My post was actually sparked by a paper on how to adjust for measurement artifacts in surface temperature data, e.g., land use patterns near weather stations.)

✴️ @AI_Python_EN

Jure Leskovec, Professor at Stanford


Deep Generative Models for Graphs: Methods & Applications.
Slides from my talk at #ICLR2019 workshop on Representation Learning on Graphs and Manifolds.
https://lnkd.in/eTHRDsM

#deeplearning

✴️ @AI_Python_EN

RetinaFace: Single-stage Dense Face Localisation in the Wild.

Though tremendous strides have been made in uncontrolled face detection, accurate and efficient face localisation in the wild remains an open challenge.

This paper presents a robust single-stage face detector, named RetinaFace, which performs pixel-wise face localisation on various scales of faces by taking advantages of joint extra-supervised and self-supervised multi-task learning.


Paper: https://lnkd.in/dF48muv
#deeplearning #facerecognition #research

✴️ @AI_Python_EN

Someone ask me “Can you make more beginner friendly learning path”?


Yes, I do, Here’s the path

How to learn data science with python for complete beginner from concept to code

✅ Step 1
Know Data Science
https://lnkd.in/fMHtxYP

✅ Step 2
Understand How to answer Why
https://lnkd.in/f396Dqg

✅ Step 3
Know Machine Learning Key Terminology
https://lnkd.in/fCihY9W

✅ Step 4
Understand Machine Learning Implementation
https://lnkd.in/f5aUbBM

✅ Step 5
Understand the basics of data structures and algorithms
https://lnkd.in/gYKnJWN

✅ Step 6
Do more practice problems in Python
https://lnkd.in/gGQ7cuv

✅ Step 7
Learn the scientific libraries (NumPy, SciPy, Pandas)
Pandas: https://lnkd.in/g4DFNpJ

✅ Step 8
Machine Learning Programming
in 20min: https://lnkd.in/g-Su_um
Scikit-Learn Tutorial: https://lnkd.in/gSThdRD

✅ Step 9
Practice your machine learning skills
Kaggle Machine Learning Tutorial: https://lnkd.in/gT5nNwS

✅ Step 10
Practice advanced library
a.TensorFlow
https://lnkd.in/fXKQkGy

b.Dlib
https://lnkd.in/fzPM2Gs

#machinelearning #datascience #python

✴️ @AI_Python_EN

Microsoft launches a drag-and-drop machine learning tool

Article by Frederic Lardinois: https://lnkd.in/eXPPSbe

#ArtificialIntelligence #DeepLearning #MachineLearning

✴️ @AI_Python_EN

Applied Machine Learning Problem Solving Framework


#machinelearing #datascience

✴️ @AI_Python_EN

Text to Image

https://bit.ly/2thbmVp

✴️ @AI_Python_EN

Deepfakes were a crazy creation of the #GANs so here is an answer about their detection.

Recurrent-Convolution Approach to DeepFake Detection - State-Of-Art Results on FaceForensics++

Spread of misinformation has become a significant problem, raising the importance of relevant detection methods.

While there are different manifestations of misinformation, in this work we focus on detecting face manipulations in videos.

Here the authors attempt to detect Deepfake, Face2Face and FaceSwap manipulations in videos.

Paper: arxiv.org/abs/1905.00582
Code: Adding soon

#deeplearning
#facerecognition
✴️ @AI_Python_EN

Natural Language Processing, aka Computational Linguistics, is a vast and fascinating subject whose relevance cannot be exaggerated.

It has been greatly hyped but has a number of important applications. It is increasingly part of our daily lives and, for some of us, our jobs.

but here are some books I've found helpful:

- Foundations of Computational Linguistics (Hausser)
- The Handbook of Computational Linguistics (Clark et al.)
- Social Media Intelligence (Moe and Schweidel)
- Natural Language Processing for Social Media (Farzindar and Inkpen)
- Machine Translation (Poibeau)
- Sentiment Analysis: Mining Opinions, Sentiments, and Emotions (Liu)
- Neural Network Methods in Natural Language Processing (Goldberg)

❗️ Natural Language Processing for Social Media
and
❗️ Text Analytics: A Primer

may be of interest. They are brief interviews with two academic authorities on NLP, Anna Farzindar and Bing Liu, respectively.

✴️ @AI_Python_EN

Videos of all sessions at ICLR 2019 (including live stream)

https://www.facebook.com/pg/iclr.cc/videos/?ref=page_internal

✴️ @AI_Python_EN

Top #DataScience and #MachineLearning
#Methods Used in 2018, 2019


https://www.kdnuggets.com/2019/04/top-data-science-machine-learning-methods-2018-2019.html

#AI #ArtificialIntelligence #DeepLearning

✴️ @AI_Python_EN

How to Develop a Convolutional Neural Network From Scratch for MNIST Handwritten Digit Classification
#cnn
https://machinelearningmastery.com/blog/

✴️ @AI_Python_EN

5 Free Object-Oriented Programming Online Courses for Programmers

🌎 Object-Oriented Programming

#Programming

✴️ @AI_Python_EN

FuturePose - Mixed Reality Martial Arts Training Using Real-Time 3D Human Pose Forecasting With a RGB Camera

🌎 Paper
🌎 video

#GAN

✴️ @AI_Python_EN

How I developed a C.N.N. that recognizes emotions and broke into the Kaggle top 10

#GAN #facesrecognize

🌎 link

✴️ @AI_Python_EN

💠 Selection of resources to learn Artificial Intelligence / Machine Learning / Statistical Inference / Deep Learning / Reinforcement Learning

🌍 https://github.com/memo/ai-resources
🌍 https://medium.com/artists-and-machine-intelligence/selection-of-resources-to-learn-artificial-intelligence-machine-learning-statistical-inference-23bc56ba655

✴️ @AI_Python_EN