One of the BEST #MachineLearning Glossary by Google

It will definitely come in handy - https://lnkd.in/gNiE9JT

Link to learn more about Machine Learning:

✅ Course 1 : A comprehensive Learning Path to become Data Scientist in 2019
Link : https://bit.ly/2HOthei

✅ Course 2 : Experiments with Data
Link : https://bit.ly/2HQuQbw

✅ Course 3 : Python for Data Science
Link : https://bit.ly/2HOG5RG

✅ Course 4 : Twitter Sentiments Analysis
Link : https://bit.ly/2HR8O8A

✅ Course 5 : Creating Time Series Forecast with Python
Link : https://bit.ly/2XniU6r

✅ Course 6 : A comprehensive path for learning Deep Learning in 2019
Link : https://bit.ly/2HO1VVJ

✅ Course 7 : Loan Prediction Practice problem
Link : https://bit.ly/2IcynQl

✅ Course 8 : Big mart Sales Problem using R
Link : https://bit.ly/2JUlZIb

#announcements #datascientist #machinelearning #datascience #artificialintelligence

✴️ @AI_Python_EN

If you're interested in learning a simple and powerful data cleaning framework for your work, have a look at this post.

Data cleaning takes nearly 60 - 70 % of our time and all the fancy models & visualizations are created after slogging hours of cleaning the data.

If you need a shiny report at the end which will answer all the business questions, you have to go through the time consuming process for yourself.

Real world data is not as clean as kaggle datasets but still you can find datasets which are not ready made for analysis in UCI or Kaggle to work on.

Try these things on a dataset this weekend and share your work with the community.

Link to first post <- https://lnkd.in/fQmem8d

Link to the second post <- https://lnkd.in/ffrQqgC

✴️ @AI_Python_EN

When to Trust Your Model: Model-Based Policy Optimization

Janner et al.: https://lnkd.in/eTmBjA9

#ArtificialIntelligence #DeepLearning #ReinforcementLearning

✴️ @AI_Python_EN

open sourcing PyRobot, a lightweight, high-level interface that lets #AI researchers get up and running with #robotics experiments in just hours. No specialized robotics expertise needed! https://ai.facebook.com/blog/open-sourcing-pyrobot-to-accelerate-ai-robotics-research/

✴️ @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

the #CVPR2019 Low-Power Image Recognition Challenge (LPIIRC) winning teams from Amazon, Alibaba, Expasoft, Tsinghua, MIT and Qualcomm. Learn more about the challenge at
https://rebootingcomputing.ieee.org/lpirc .

✴️ @AI_Python_EN

#CVPR2019 presenting Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks (RCAN).

✴️ @AI_Python_EN

have released the code and data for our #CVPR2019 paper on hand-object reconstruction.
http://www.di.ens.fr/willow/research/obman/

✴️ @AI_Python_EN

the #CVPR2019 Google Booth will host demos featuring work on Increasing AR Realism Using Lighting
http://goo.gle/2KwK5ce
and teaching people how to dance with the Dance Like app.
http://goo.gle/2X18ddS .

✴️ @AI_Python_EN

Check out Off-Policy Classification, a new method to evaluate the performance of #reinforcementlearning agents trained entirely on data from prior agents, enabling selective testing of only the most promising models on real-world robots. Learn more below!

https://ai.googleblog.com/2019/06/off-policy-classification-new.html

✴️ @AI_Python_EN

Waymo just announced the release of large open dataset at #CVPR2019

https://waymo.com/open

✴️ @AI_Python_EN

Artificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with a Conditional Generative Adversarial Network

https://arxiv.org/abs/1905.08700

✴️ @AI_Python_EN

The 2019 IEEE Conference on Computer Vision and Pattern Recognition

Best Papers

https://syncedreview.com/2019/06/18/cvpr-2019-attracts-9k-attendees-best-papers-announced-imagenet-honoured-10-years-later/

✴️ @AI_Python_EN

NLP + Deep Leaning checked. Was painfully awesome. Now what's next? Can't waste it... or maybe CNN and RL? #cs224n #deeplearning #NLP

🐼🤹‍♂️ pandas trick:
Two easy ways to reduce DataFrame memory usage:
1. Only read in columns you need
2. Use 'category' data type with categorical data

Example:
df = pd.read_csv('file.csv', usecols=['A', 'C', 'D'], dtype={'D':'category'})

#Python #DataScience

✴️ @AI_Python_EN

Predicting demands using AI? Just focus on the things that matter

✴️ @AI_Python_EN

Statistical methods can and are applied to qualitative data (e.g. text). The raw data first need to be converted to a form, or represented in a way, that can be quantitatively analyzed.

It can be done by man or, increasingly, machine. This would be the case whether cluster analysis, factor analysis, deep learning or some other method is used to analyze the data.

A very simple example is cluster or factor analyzing open end codes from a consumer survey, or using them in key driver regression.

This predates AI by many years, though machine coding would now be the preferred approach in many instances.

Sparse data can be a concern, but with rapid declines in the cost of survey data collection, this is now more feasible since very large samples can be collected at reasonable cost and within a realistic time frame.

To be clear, my original motivation for this post was not to push an alternative to standard close-ended questioning, but a response to confusion about AI I frequently encounter. However, with data of sufficient quality, complex analytics which tie attitudes, behavior and demographics together, perhaps combined simultaneously with segmentation, is possible with this alternative method. AI in some form may play a part but is not absolutely essential. The basic idea goes back nearly a century.

✴️ @AI_Python_EN

We Can All Become Video Game Characters With This AI

Video: https://www.youtube.com/watch?v=Y73iUAh56iI

Paper: https://arxiv.org/abs/1904.08379

I've been thinking a bit about the growing practice of fine-tuning generic pretrained models: first in computer vision, now NLP (highly recommend Sebastian Ruders great article on this http://ruder.io/nlp-imagenet/ )...Last time I mentioned this, people were skeptical that RL would be next.
✴️ @AI_Python_EN