DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

Paper: http://ow.ly/Nk6Y50uAmII
#artificialinteligence #ai #ml #machinelearning #bigdata #deeplearning #technology

✴️ @AI_Python_EN

Why Should you Learn AI and Machine Learning

Why Machine Learning Fascinates Me?

Supervised Machine Learning

Do you know what is Machine Learning All About?

The Science of Machine Learning is about Learning the Models that Generalize Well Machine learning is an area of artificial intelligence and computer science This includes the development of software and algorithms that can make predictions based on data.

Data Science Enthusiasts, I have Created a Community for Us to Learn Together🗝

Interested people let me know in the Comments and I will send you the invite link to our Community🎟🗣

#reinforcementlearning #machinlearning #Datascience #ArtificialIntelligence #gans
#SupervisedMachineLearning #ML #dl #iot #bigdata

✴️ @AI_Python_EN

Machine Learning VS Deep Learning

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #technology #python

✴️ @AI_Python_EN

It is a good feeling when a popular Python package adds a new feature based on your article :-)

#Yellowbrick is a great little #ML #visualization library in the Python universe, which extends the Scikit-Learn API to allow human steering of the model selection process, and adds statistical plotting capability for common diagnostics tests on ML.

Based on my article "How do you check the quality of your regression model in Python? they are adding a new feature to the library - Cook's distance stemplot (outlier detection) for regression models.

#python #datascience #machinelearning #data #model
https://www.scikit-yb.org/en/latest/

✴️ @AI_Python_EN

#ICML2019 live from Long Beach, CA, via icmlconf Learn more
→ https://mld.ai/icml2019-live #machinelearning #ML #mldcmu #ICML

✴️ @AI_Python_EN

11 things I learned from the Machine Learning for Coders course at fastdotai"
https://medium.com/yottabytes/11-things-i-learned-from-the-machine-learning-for-coders-course-at-fast-ai-799468b089bc?source=friends_link&sk=337416e814280d88e7bfad994cac8533
#machinelearning #datascience #ml #python #bigdata

✴️ @AI_Python_EN

#Machineearning for Everyone
http://bit.ly/2RvRRnj

#AI #ML #DataScience #Algorithms

✴️ @AI_Python_EN

Quantile Regression Deep Reinforcement Learning

Researchers: Oliver Richter, Roger Wattenhofer
Paper: https://lnkd.in/fnwiYXi
#artificialintelligence #ai #ml #machinelearning #bigdata #deeplearning #technology #datascience

✴️ @AI_Python_EN

Anticipatory Thinking: A Metacognitive Capability
Researchers: Adam Amos-Binks, Dustin Dannenhauer
Paper: http://ow.ly/wEyC50uR9q1

#artificialintelligence #ai #ml #machinelearning #bigdata #deeplearning #technology #datascience

✴️ @AI_Python_EN

#AI/ #DataScience/ #MachineLearning/ #ML:
7 Steps for Data Preparation Using #Python

Link => https://bit.ly/PyDataPrep

#datamining #statistics #bigdata #artificialintelligence

✴️ @AI_Python_EN

Module 3: Core Machine Learning (May-October Semester)
July 6th by FAST-NU AI/ML Training Center

Module 3 (Core Machine Learning) of our ongoing cohort (May October semester) for the AI-ML training program. It covers basic to intermediate Machine Learning and lays a solid foundation to build or transition into a career of ML and Data Science, and also to provide a thorough grounding for the next Deep Learning Module.

https://www.facebook.com/events/2195319697439547/

#deeplearning #machinelearning #opencv #AI #ML #Python

✴️ @AI_Python_EN

Artificial Intelligence: the global landscape of ethics guidelines

Researchers: Anna Jobin, Marcello Ienca, Effy Vayena
Paper: http://ow.ly/mDA430p2R0q

#artificialintelligence #ai #ml #machinelearning #bigdata #deeplearning #technology #datascience

✴️ @AI_Python_EN

Classifying Legendary Pokemon Birds 🐦🐦🐦

👉👉👉 Try it yourself:
https://lnkd.in/eYhKNAh 👈👈👈

After only the second fastai "Practical Deep Learning for Coders" class I was able to complete an end-to-end deep learning project! 🤖🤖🤖

The main goal is to classify an image as either one of the Legendary Pokemon Birds - Articuno, Moltres or Zapdos - or an alternative class which includes everything else. Needless to say that sometimes my model gets confused about the alternative class since not so many diverse images were feed into it...

Source code:
https://lnkd.in/eRfkBx8
Forked from:
https://lnkd.in/e_k4nqN

#ai #ml #dl #deeplearning #cnn #python

✴️ @AI_Python_EN

Remember the #BachDoodle? We’re excited to release paper on Behind-the-Scenes design, #ML, scaling it up, and dataset of 21.6M melodies from around the world!
📜 http://arxiv.org/abs/1907.06637

✴️ @AI_Python_EN

Library for Scikit-learn parallization

Operations like grid search, random forest, and others that use the njobs parameter in Scikit-Learn can automatically hand-off parallelism to a Dask cluster.

Link: https://ml.dask.org/joblib.html

#ML

❇️ @AI_Python_EN

Microsoft Open Source Engineer pythiccoder

explores nine advanced tips for production #ML. Read:

https://medium.com/microsoftazure/9-advanced-tips-for-production-machine-learning-6bbdebf49a6f

❇️ @AI_PYTHON_EN

What's the purpose of statistics?

"Do you think the purpose of existence is to pass out of existence is the purpose of existence?" - Ray Manzarek

The former Doors organist poses some fundamental questions to which definitive answers remain elusive. Happily, the purpose of statistics is easier to fathom since humans are its creator. Put simply, it is to enhance decision making.

These decisions could be those made by scientists, businesspeople, politicians and other government officials, by medical and legal professionals, or even by religious authorities. In informal ways, ordinary folks also use statistics to help make better decisions.

How does it do this?

One way is by providing basic information, such as how many, how much and how often. Stat in statistics is derived from the word state, as in nation state and, as it emerged as a formal discipline, describing nations quantitatively (e.g., population size, number of citizens working in manufacturing) became a fundamental purpose. Frequencies, means, medians and standard deviations are now familiar to anyone.

Often we must rely on samples to make inferences about our population of interest. From a consumer survey, for example, we might estimate mean annual household expenditures on snack foods. This is known as inferential statistics, and confidence intervals will be familiar to anyone who has taken an introductory course in statistics. So will methods such as t-tests and chi-squared tests which can be used to make population inferences about groups (e.g., are males more likely than females to eat pretzels?).

Another way statistics helps us make decisions is by exploring relationships among variables through the use of cross tabulations, correlations and data visualizations. Exploratory data analysis (EDA) can also take on more complex forms and draw upon methods such as principal components analysis, regression and cluster analysis. EDA is often used to develop hypotheses which will be assessed more rigorously in subsequent research.

These hypotheses are often causal in nature, for example, why some people avoid snacks. Randomized experiments are generally considered the best approach in causal analysis but are not always possible or appropriate; see Why experiment? for some more thoughts on this subject. Hypotheses can be further developed and refined, not simply tested through Null Hypothesis Significance Testing, though this has been traditionally frowned upon since we are using the same data for multiple purposes.

Many statisticians are actively involved in designing research, not merely using secondary data. This is a large subject but briefly summarized in Preaching About Primary Research.

Making classifications, predictions and forecasts is another traditional role of statistics. In a data science context, the first two are often called predictive analytics and employ methods such as random forests and standard (OLS) regression. Forecasting sales for the next year is a different matter and normally requires the use of time-series analysis. There is also unsupervised learning, which aims to find previously unknown patterns in unlabeled data. Using K-means clustering to partition consumer survey respondents into segments based on their attitudes is an example of this.

Quality control, operations research, what-if simulations and risk assessment are other areas where statistics play a key role. There are many others, as this page illustrates.

The fuzzy buzzy term analytics is frequently used interchangeably with statistics, an offense to which I also plead guilty.

"The best thing about being a statistician is that you get to play in everyone's backyard." - John Tukey

#ai #artificialintelligence #ml #statistics #bigdata #machinelearning
#datascience

❇️ @AI_Python_EN

What are the three types of error in a #ML model?

👉 1. Bias - error caused by choosing an algorithm that cannot accurately model the signal in the data, i.e. the model is too general or was incorrectly selected. For example, selecting a simple linear regression to model highly non-linear data would result in error due to bias.

👉 2. Variance - error from an estimator being too specific and learning relationships that are specific to the training set but do not generalize to new samples well. Variance can come from fitting too closely to noise in the data, and models with high variance are extremely sensitive to changing inputs. Example: Creating a decision tree that splits the training set until every leaf node only contains 1 sample.

👉 3. Irreducible error - error caused by noise in the data that cannot be removed through modeling. Example: inaccuracy in data collection causes irreducible error.

❇️ @AI_Python_EN

Decision trees are extremely fast when it comes to classify unknown records. Watch this video to know how Decision Tree algorithm works, in an easy way - http://bit.ly/2Ggsb9l

#DataScience #MachineLearning #AI #ML #ReinforcementLearning #Analytics #CloudComputing #Python #DeepLearning #BigData #Hadoop

ANNOUNCING PYCARET 1.0.0 - An amazingly simple, fast and efficient way to do machine learning in Python. NEW OPEN SOURCE ML LIBRARY If you are a DATA SCIENTIST or want to become one, then this is for YOU....

PyCaret is a NEW open source machine learning library to train and deploy ML models in low-code environment.

It allows you to go from preparing data to deploying a model within SECONDS.

PyCaret is designed to reduce time and efforts spent in coding ML experiments. It automates the following:

- Preprocessing (Data Preparation, Feature Engineering and Feature Selection)
- Model Selection (over 60 ready-to-use algorithms)
- Model Evaluation (50+ analysis plots)
- Model Deployment
- ML Integration and Monitoring (Power BI, Tableau, Alteryx, KNIME and more)
- ..... and much more!

Watch this 1 minute video to see how PyCaret can help you in your next machine learning project.

The easiest way to install pycaret is using pip. Just type "pip install pycaret" into your notebook.

To learn more about PyCaret, please visit the official website https://www.pycaret.org

#datascience #datascientist #machinelearning #ml #ai #artificialintelligence #analytics #pycaret

❇️ @AI_Python_EN