Fixup Initialization: Residual Learning Without Normalization

Paper by Zhang et al.: https://lnkd.in/e6egt6x

PyTorch code by Andy Brock: https://lnkd.in/evhuhdj

#artificalintelligence #deeplearning #machinelearning

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Want to become an SQL expert for Data analysis and Manipulation? If Yes, then continue reading....

Last month I shared T-SQL Interview Ques. & Ans. to help job seekers in cracking the interview. My post went viral on linkedin and I helped thousand of users across the globe.

Today I'm sharing the best resources on internet from where you can gain advance Sql knowledge and practice real life SQL scenarios.

1. Books to Read: T-SQL Fundamentals by Itzik Ben-Gan ; SQL Server T-SQL Recipes by David Dye to practice SQL excercises.

2. Community to Join: SQLSERVERCENTRAL.COM | Best SQL community to gain advance level knowledge. Stairway series on this community is very useful to gain sound knowledge with Step-by-Step approach. There are also lot of good articles to read.

3. People to follow : My favorite Jeff Moden . He is MVP and helped thousands of people with his knowledge on above community. Brent Ozar is also MVP and very good SQL Mentor.

4. Site to Register: http://www.sql-ex.ru/ - My personal favorite to practice SQL & compete with Masters. The best thing about this site is you won't be able to reach next level until you solve the current level challenge.

Please share post so that it will help maximum people.

Learn....Earn....and Return !

#sql

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I've had a strong interest in psychometrics for many years. Some basic understanding of it is essential for marketing researchers as well as researchers and scholars in a diverse range of disciplines.

It's much more than Cronbach's alpha and principal components factor analysis with varimax rotation. Here are some books I can recommend about or related to psychometrics:

- Psychometrics (Furr and Bacharach)
- Measurement Theory and Applications for the Social Sciences (Bandalos)
- Bayesian Psychometric Modeling (Levy and Mislevy)
- Handbook of Item Response Theory Modeling (Reise and Revicki)
- The Theory and Practice of Item Response Theory (de Ayala)
- Multidimensional IRT (Reckase)
- Test Equating, Scaling, and Linking (Kolen and Brennan)
- Generalizability Theory (Brennan)
- Handbook of Personality Assessment (Weiner and Greene)
- Measures of Personality and Social Psychological Constructs (Boyle et al.)
- Cognitive Psychology (Sternberg and Sternberg)
- The Cognitive Neurosciences (Gazzaniga et al.)

There are many academic journals, such as the venerable Psychometrica, and three I currently subscribe to: Structural Equation Modeling (Routledge); Journal of Educational and Behavioral Statistics (ASA); and British Journal of Mathematical and Statistical Psychology (Wiley).

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we learned about strict separating hyperplane and Farkas' theorem last week in optimization class

🌎 Link

Special Thanks to :Mona Jalal

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WHAT IS A TENSOR?

Michel Van Biezen give a simple video explanation of what is a scalar, vector, dyad, Triad. They are all essentially Rank 0, 1, 2 and 3 Tensors.

Einstein's Field Equation (3 for x,y,z axes for space and another one for time) is essentially is a Rank 4 Tensor.

In matrices representation you can see how a Rank 3 Tensor is drawn.

Can you draw the Einstein's Rank 4 (256 components) tensor now?

Detail here https://lnkd.in/g9yAGS2

#deeplearning #fundamentals #artificialintelligence #tensor #matrices

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I think this can be great place to geek out about:

- Distributed processing
- Big Data and Platform Architecture
- SQL and NoSql databases
- Visualization tools

To help you learn interesting stuff and get your ideas and knowledge on front of people. Build your reputation to easier find a job if you need one.

So, please check it out, become a writer and send in your articles 📕

Plumbers of Data Science on Medium:
https://lnkd.in/dU4fPRU

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neural network art https://lnkd.in/g75HGUx

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NLP Enthusiast? Try #NaturalQuestions(NQ) corpus by #Google has released a new question answering dataset called the #NQ. Its a new, large-scale corpus for training and evaluating open-domain question answering systems and the first to replicate the end-to-end process in which people find answers to questions

NQ is the first dataset to use naturally occurring queries and focuses on finding answers by reading an entire page, rather than extracting answers from a short paragraph. Google also claims that the quality of the annotations in the NQ corpus has been measured at 90% accuracy.
NQ is large, consisting of 300,000 naturally occurring questions, along with human annotated answers from #Wikipedia pages.

Interestingly, Google has also announced a challenge based on this data to help advance natural language understanding in computers.

Paper linked to the research: https://lnkd.in/fNg3ZtT
Challenge website and dataset: https://lnkd.in/frNYrUE

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Beautiful Playground - #styleGAN is on Github https://github.com/NVlabs/stylegan | Yes its stunning - so is its energy consumption | i cant really tell why the environmental impacts of those emerging tech is not discussed as much as they should be | #gan #MachineLearning

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Fantastic in-depth piece by skynet on job loss due to AI. The main argument: AI will (most likely) *not be significantly more disruptive* than the impact of automation in the past. https://www.skynettoday.com/editorials/ai-automation-job-loss

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NEED COMMUNITY ON DATA ENGINEERING?

Andreas Kretz created a publication on Medium dedicated to data engineering and big data. Not for himself, but for us, who need reliable data pipeline on dayjob. To bring attention to this super important topic. A place where you can write about data engineering. If you don't know, he always give quality content about data engineering. I think this can be great place to discuss out about:

- Distributed processing
- Big Data and Platform Architecture
- SQL and NoSql databases
- Visualization tools

To help you learn interesting stuff and get your ideas and knowledge on front of people. Build your reputation to easier find a job if you need one.

So, please check it out, become a writer and send in your articles 📕

Plumbers of Data Science on Medium:
https://lnkd.in/dU4fPRU

What do you think? 👍 or 👎


PS: If you are looking for a publication aimed at data scientists check out www.towardsdatascience.com

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#DataScienceInsights

Data Integrity Leads to Data Quality.
Data Quality Leads to Quality Insights.
Quality Insights Lead to Courageous Decisions.
Courageous Decisions Lead to Desired Transformation.

For one to make the Desired Transformation, the Courageous Decisions have to be Backed up by a Sustained Data Integrity and Quality, Quality Insights and Trust in the Data.

Agree?

#datainsights #transformations

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👉 If you like our channel, i invite you to share it with your friends:
Our channel in english: ✴️ @AI_Python_EN
Our Daily arXiv Channel: 🗣 @AI_Python_Arxiv

BTW: Thank you for joining :)

Here’s a list of 10 GREAT #SelfStarting #DataScience Projects to work on:

➖BEGINNER➖

⚠️ NOTE: The links provided will redirect you to a recommended Kaggle Kernel that I enjoyed. Use it as a reference before starting on your project :)

⚠️ IMPORTANT: Number 10 is a MUST DO!

1. Pokemon - Weedle's Cave 🐛
Python - https://lnkd.in/gcKWWQ2

2. Titanic ML 🚢
Python - https://lnkd.in/gafie9m
R - https://lnkd.in/gRRa7HV

3. Housing Prices Prediction 🏡
Python - https://lnkd.in/gX2FSDk
R - https://lnkd.in/ggFJSyd


➖INTERMEDIATE➖

4. Instacart Market Basket Analysis 🛒
Python - https://lnkd.in/gkNaXqH
R- https://lnkd.in/g2gthxu

5. Quora Question Pairs 👥
Project :https://lnkd.in/f3HQZsT
Tutorial (Python)- https://lnkd.in/fEzf-Xp

6. Human Resource Analytics 🕴🏻
Python - https://lnkd.in/gVUPfWm
R -https://lnkd.in/gHusQYX


➖ADVANCED➖

7. Analyzing Soccer Player Faces ⚽️
Python - https://lnkd.in/gUys_TS

8. Recruit Restaurant Visitor Forecasting 🍱
Python - https://lnkd.in/gjQvf74

9. TensorFlow Speech Recognition 🗣
Python - https://lnkd.in/g8SSPfW


➖MASTERY➖

10. Not Enough?
This is more complete guide from Analytics Vidhya
https://lnkd.in/g_QjzGe.

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TF-REPLICATOR: DISTRIBUTED MACHINE LEARNING FOR RESEARCHERS #DataScience #MachineLearning #ArtificialIntelligence
http://bit.ly/2BmoLzV

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Multiview learning is an important branch of deep learning. During learning, multiview learning explicitly uses multiple distinct representations of data, and models the relationship between them and/or the results from downstream computations.

these “representation” can either be the original features of the data or the features obtained through some computations.
The approach to utilize these representations is to simply concatenate them into a single representation to perform learning is greatly useful in Cross-Media Intelligence, Intelligent Medical AI systems, Autonomous Driving and many other industry domains.

Advantages? It better utilizes the structural information in the data, which leads to better model- ing capability. Further, multiple views can act as complementary and collaborative normalization, effectively reducing the size of the hypothesis space.

To learn more see here an example of Deep Multi-View Concept Learning: https://lnkd.in/dckzjBR
#deeplearning #machinelearning

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Troubleshooting Deep Neural Networks

A Field Guide to Fixing Your Model, by Josh Tobin: https://lnkd.in/eNB-vci

#artificalintelligence #deeplearning #neuralnetworks

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Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition #DataScience #MachineLearning
#ArtificialIntelligence
http://bit.ly/2Blf7xH

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Can neural networks learn commonsense reasoning?

ATOMIC | An Atlas of Machine Commonsense for If-Then Reasoning: https://homes.cs.washington.edu/~msap/atomic/

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Lex Fridman : First blog post on Deep Learning Basics with TensorFlow (on the official TensorFlow page):
https://medium.com/tensorflow/mit-deep-learning-basics-introduction-and-overview-with-tensorflow-355bcd26baf0

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