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

For better #deepneuralnetwork vision, just add feedback (loops)

🌎 deep neural network


✴️ @AI_Python_EN

How to write better code on data science?


As data scientist you should write code better, here’s the steps

Before start to code, is good to know some implementation machine learning in business


✅ Step 1. Understand Data Science Implementation
https://lnkd.in/fMHtxYP
✅ Step 2. Understand what business want
https://lnkd.in/f396Dqg
✅ Step 3. Know Machine Learning Key Terminology
https://lnkd.in/fCihY9W
✅ Step 4. Know Business Implementation of Data Science
https://lnkd.in/f5aUbBM

After you have clear what to code, here’s the step

1. Following standard style
-> https://lnkd.in/gKZUjVa

2. Use a linter to enforce style standards
-> https://lnkd.in/d_prybR

3. Write modular, generic, object-oriented code -
-> https://lnkd.in/gsynW6Q
-> https://lnkd.in/dx53u53

4. Write unit tests to test your functions and methods
-> https://lnkd.in/dsy-bPu

5. Organizing your code base
-> https://lnkd.in/dviGffH

6. Separate exploration and production development, and develop production code using test-driven development (TDD)
-> https://lnkd.in/dMn-s32

#machinelearning #datascience #supervisedlearning #business

✴️ @AI_Python_EN

Machine Learning Methods - Algorithms and their outputs

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

✴️ @AI_Python_EN

There are many misconceptions regarding Null Hypothesis Significance Testing and many disagreements among statisticians about it. Statistics in general is poorly understood in marketing research and the businesses community in my experience.

https://greenbookblog.org/2019/05/08/thats-a-fact-jack/

✴️ @AI_Python_EN

TensorNetwork: A Library for #Physics and
#MachineLearning

“TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of other research areas, including machine learning. Authors demonstrate the use of the API with applications both physics and machine learning, with details appearing in companion papers.”

Paper: https://lnkd.in/e4tPaQG

✴️ @AI_Python_EN

Fundamentals of Clinical Data Science (Open-Access Book) - for healthcare & IT professionals: https://lnkd.in/eacNnjz
#
For more interesting & helpful content on healthcare & data science, follow me and Brainformatika on LinkedIn.

Table of Contents

Part I. Data Collection

- Data Sources
- Data at Scale
- Standards in Healthcare Data
- Research Data Stewardship for Healthcare Professionals
- The EU’s General Data Protection Regulation (GDPR) in a Research Context

Part II. From Data to Model

- Preparing Data for Predictive Modelling
- Extracting Features from Time Series
- Prediction Modeling Methodology
- Diving Deeper into Models
- Reporting Standards & Critical Appraisal of Prediction Models

Part III. From Model to Application

- Clinical Decision Support Systems
- Mobile Apps
- Optimizing Care Processes with Operational Excellence & Process Mining
- Value-Based Health Care Supported by Data Science

#healthcare #datascience #digitalhealth #analytics #machinelearning #bigdata #populationhealth #ai #medicine #informatics #artificialintelligence #research #precisionmedicine #publichealth #science #health #innovation #technology #informationtechnology

✴️ @AI_Python_EN

In the future, you may be fired by an algorithm

#algorithms

✴️ @AI_Python_EN