#towardsdatascience

[vlruso]

Prompt Engineering Tips a Neural Network How-To and Other Recent Must-Reads

📢 Prompt Engineering Tips, a Neural Network How-To, and Other Recent Must-Reads Feeling that autumn energy? Our authors have been busy learning, experimenting, and launching exciting new projects. 🍂📚 Check out these ten standout articles from Towards Data Science – Medium that have been creating a buzz in our community. From program simulation techniques to building neural networks from scratch, these must-reads cover cutting-edge topics you don't want to miss. 💡 Read more here: [Link to the blog post](https://ift.tt/io0tgy4) And don't worry, we've got the action items covered too! We've identified tasks for each article's author to further explore and dive deeper into these fascinating subjects. See the full list of action items in the blog post. Keep up with the latest insights and join the conversation. Let's keep learning and growing together! #TowardsDataScience #MustReads #NeuralNetworks #PromptEngineering #DataScience #AI #MachineLearning List of Useful Links: AI Scrum Bot - ask about AI scrum and agile Our Telegram @itinai Twitter -  @itinaicom

[whats-ai]

I am now a top writer in the artificial intelligence category on @medium ! I would love to be in @towardsdatascience 's publications as well. I will keep improving the articles' quality and someday make it and become a towards data science writer 💪 Don't give up even if you fail the first few times, you have no idea how bad were my first articles compared to now, and there is still a lot of place for improvement! Check out my medium @whats_ai on medium as well! posted on Instagram - https://instagr.am/p/CISw0TPAnN-/

[oagye001-blog]

15/02/21 – 19/02/21

Obtained dataset for CO2 emissions from Hawaii where it contained monthly data so it would be the dataset used for the machine learning

Researching previous examples of LSTM being used in predicting future values. Came across this example on TowardsDataScience. Link: https://towardsdatascience.com/time-series-forecasting-with-recurrent-neural-networks-74674e289816

[theworldofbots]

3 3/3 #Philosophy for #AI Enthusiasts

Part 3 of : Intro to #ArtificialGeneralIntelligence Series Part 3 3/3

Via @TDataScience 

#MikeFerguson on #Medium 

#MediumPublication #TowardsDataScience on #Medium 

[dreamonlineevents]

Curiosity & Creativity -CC01. Today 29-06-2020. Lets celebrate the birthday of India’s First Big Data Man - PCM. The famous scientist Shri Prasanta Chandra Mahalanobis. What is Big Data ? Social Media is one of the sources of Big Data. Big data is exactly what it sounds like – a lot of information created by a lot of different people. This information adds up every time you play games, surf the internet or post on Facebook or any other social media platform. Reference : - @the_economic_times @jigsawacademyofficial @towardsdatascience @mgcu_bihar #nationalstatisticsday #curiosity #curious #creativeart #creative #creativityeveryday #curiosityandcreativity #art #artist #kolkata (at Jaipur, Rajasthan) https://www.instagram.com/p/CCAlxMilmQk/?igshid=pbc3cisf941k

[eurekakinginc]

"[D] Medium/TowardsDataScience Noise"- Detail: Lately in my work and personal research, I have noticed that whenever I google anything related to ML/AI/DS there is a whole page of medium/tds articles. Some of these are actually useful but more often than not they're garbage and doing a really bad job of presenting a topic of interest.Do other people have a way of weeding through these? I think Medium should introduce some sort of usefulness tagging mechanism to actually tell if an article does a good job in explaining a concept or not. These days every 'data scientist' with little to no communication skills is writing Medium posts.. Caption by humanager. Posted By: www.eurekaking.com

[marcotav65-blog]

In this @linkedin article (published originally on @towardsdatascience), I argue that by thinking of stochastic gradient descent as a Langevin process, one can better understand the reasons why the method works so well as a global optimizer. https://tinyurl.com/y2m6l42x https://www.instagram.com/p/B0zFLiblNIk/?igshid=l3bzv9f7r86l

[gozealouscloudcollection]

图神经网络的重要分支：时间图网络

本文最初发表于 TowardsDataScience 博客，经原作者 Michael Bronstein 授权，InfoQ 中文站翻译并分享。

许多现实世界的问题涉及各种性质的交易网络、社会互动和交往，这些都是动态的，可以将其建模为图，其中，节点和边会随着时间的推移而出现。在本文中，我们将描述时间图网络（Temporal Graph Network，TGN），这是一个用于深度学习动态图的通用框架。…

from 图神经网络的重要分支：时间图网络 via KKNEWS

[twiningsandoolong]

?Top 10 Data Science Blogs? 1. Analytics Vidya 2. Data Science Central 3. KDnuggets 4. R-Bloggers 5. Revolution Analytics 6. Data Camp 7. Codementor 8. Data Plus Science 9. Data Science 101 10. DataRobot ?Learn Statistics and Probability for free? 1. Khan Academy 2. OpenIntro 3.

[supportivy]

Energy Smart Bitcoin Mining - Vers la science des données

Energy Smart Bitcoin Mining – Vers la science des données

Energy Smart Bitcoin Mining - Vers la science des données Vers la science des données

L'énergie (sous toutes ses formes) est de l'argent! L'autre façon d'être économe en énergie et de relever le défi de l'énergie dans le monde de la crypto-activité et du déploiement croissant de l'IA.

Energy-Smart Bitcoin Mining in TowardsDataScience par Stéphane Bilodeau

Il convient de considérer comme primitif…

[canadastartupblog]

INTRODUCTION

The world of startups is a dynamic place — for entrepreneurs, there is seldom any promise of vested interest or reward once they hit the ground running. Private equity and venture capital firms enjoy a hefty profit because they are supposedly good at identifying which startups sizzle and which ones fizzle, but is there a way to automate the process? My goal was to build a model to answer the question of:

Can a computer model how much funding a startup should receive?

Note: the tables in this article were meant to be viewed on a computer!

|| THE DATA ||

The data used for this article was obtained through PitchBook, one of the best sources for research and financial information on startups and the realm of private investment. PitchBook provided me with the following information:

Last Valuation (in millions)

Last Funding Size (in millions)

Last Funding Round (Series A, Series B, etc.)

Last Funding Stage (Seed Round, Early Stage VC, etc.)

HQ Location

Number of Employees

Industry Sector (Healthcare, Consumer Products, etc.)

Industry Group (Healthcare Services, Pharmaceuticals, Software, etc.)

Total Raised

Number of Active Investors

The total sample size used in this experiment consisted of 973 startup companies that had received funding over the past two years.

EXPLORATORY DATA ANALYSIS ||

AngelList has a statistical demonstration of a couple of variables that are involved with the startup culture, but what intrigues me more is the actual startup process. The image below (taken from CBInsights) details the “Venture Capital Funnel,” or how most startups follow a particular predictable life cycle.

|| THE METHOD ||

The given startups were organized into buckets according to the rules defined below (where x is the funding amount):

                   x ≤ $5M –> Class 1(SMALL)

             $5M < x ≤ $25M  –> Class 2(MEDIUM)

             $25M < x ≤ $45M –>  Class 3(LARGE)

                    x > $45 –> Class 4(GIANT)

I did this in order to change the problem I was trying to solve from a regression problem (what is the exact value of funding the startup will receive?) into a classification problem (in which range will the funding fall into?). The loss of accuracy here is not an enormous problem since it is not necessary to try and predict exacts!

Using the R package ‘mice,’ missing values in the dataset were approximated with a neural network using variables like “Total Raised” and “Number of Active Investors” (to name a few). These variables were dropped because, for example, the model may not have access to the number of active investors until after a funding round ends.

The following models were trained on a limited set of features, and were appropriately weighted during testing according to their performances during the training period:

Random Forest

Linear XGBoost

Tree XGBoost

Extreme Learning Machine (ELM)

Conditional Inference Tree

Bagged Classification and Regression Tree (CART)

Neural Net

K-Nearest Neighbors

|| THE RESULTS ||

I was quite impressed with the results I was able to get from my fairly complex model! After the dynamic weighting I performed, I got the following results. The first table is a summary of the classification scores I calculated, and the second table is a confusion matrix.

                 ╔═════════════╦══════════════╗

                 ║ accuracy ║ kappa     ║

                 ╠═════════════╬══════════════╣

                 ║ 0.70696 ║ 0.51984    ║

                 ╚═════════════╩══════════════╝

                               Predicted

                Observed     1   2   3   4

                        1 | 71  13 0  2

                        2 | 27 110   6 4

                        3 |  1 12  11 15

                        4 |  0 0   0 1

These results demonstrate that my model was able to predict with 70.6% accuracy and had a moderate-strong measure of agreement. The models did have some difficulty estimating the “Large” category of funding sizes but performed fairly well overall.

Now, for the more interesting part. What variables did the model care about most? The ‘caret’ library in R provides an easy way to measure variable relevance.

                Class 1  Class 2 Class 3  Class 4  Average Rank

Funding Round   | 100.00   73.286 58.538   100.00 82.956 2

Employees       |  83.11   98.774 53.270    98.77 83.481 1

Funding Stage   |  73.62   72.776 60.768    73.62 70.196 3

HQ Location     |  17.79   21.155 17.786    21.15 19.470 4

Industry Group  |  12.54    0.000 13.984    12.54 9.766 5

Industry Sector |  10.48    5.845 5.972    10.48 8.194 6

From this table, it looks like the “Funding Round” (Series A, B, etc.) and the number of employees were the most important variables. This makes sense because as the startup enters more funding rounds or is larger (i.e. has more employees), the cheque size increases. More interestingly, however, is the fact that the “Industry Group” and “Industry Sector” variables contributed very little to the model — this means that the industry of the startup (Software, Pharmaceuticals, etc.) makes no difference! It was interesting to see that the funding landscape is not being partial to just software startups. Lastly, it seems as if the location of the company’s headquarters really does give some sort of indication as to whether or not that startup will receive higher cheque sums!

However, the ideal “startup conditions,” in essence, are masked in that there are a huge amount of other variablesthat play a larger factor in deciding the cheque size.

Some real-world examples of this phenomenon include Dwolla and AgCode, two very successful startups that began their journeys in Des Moines, IA and Glenwood, MN, respectively. The real predictor of their success was not their industry (financial services and agricultural technology) and a naive model trained only on location would have discredited them (seeing as how they are based in the Midwestern region of the United States). My model emphasizes the way the startup was able to grow and expand itself as a better predictor, citing the number of total employees and the corresponding funding round as stronger inputs. In other words, a startup that can grow faster (more employees) with less funding rounds is a healthy one.

Entrepreneurship and the private investment market are incredibly complex spaces. I invite you to conduct your own experiments relating to this topic and would love to hear any findings or progress! Give this article some claps if you liked it and stay tuned for more data-driven idea exploration by following TDS Team and me (Abhinav Raghunathan)!

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This Article first appeared in towardsdatascience

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[thefuturix]

“How to scale Machine Learning development across an enterprise.” by @LukeAlxdrJames #datascience #data @towardsdatascience https://t.co/keEOYYPsMk

— SeattleDataGuy (@SeattleDataGuy) January 9, 2018

[theworldofbots]

2 2/3 The Cognitive #Science of #AGI

Part 2 of : Intro to #ArtificialGeneralIntelligence Part 2 2/3

Via @TDataScience 

#MikeFerguson on #Medium 

#MediumPublication #TowardsDataScience on #Medium 

[engdashboard]

Two great websites to check https://medium.com & https://hackernoon.com

Interesting ideas that set your mind in motion!! This is very amazing website to be always motivated, the details below! https://medium.com There goal of the contributors was basically to collect good posts in data science, and now they are working on very useful concepts and sharing interesting ideas and unique perspectives. This is a statement from there project called "Toward Data Science" https://www.patreon.com/towardsdatascience _______________________________________________ Hi Everybody and thanks for making it to our Patreon page! In September 2016, we created a data science publication using Medium. Our goal was simply to gather good posts and distribute them to a broader audience. Just a few months later, we were pleased to see that we had a very fast growing audience and many contributors. Today, Towards Data Science provides a platform for thousands of people to exchange ideas and to expand our understanding of data science. Our audience is mixed, consisting of readers entirely new to the subject and expert professionals who want to share their inventions and discoveries. Now that our readership has grown and we are attracting contributions from fantastic writers every day, we feel it’s time to ‘up our game’. We want to provide robust support to allow our writers to develop their talents via editorial guidance. Also, we want to continue to present well-written, informative articles that our audience is excited to read. We need your support to make this happen. Towards Data Science is an independent publication. To keep us open and editorially liberated we are asking that our supporters pledge a small contribution to help us run. *** Currently, our team consists of: Inês - Data Science and Machine Learning Editor Cherie - General Editor Ludo - Data Science and Machine Learning Editor Edward - Health & Biomedicine Editor Our publication in numbers: +1,000 writers, +2,000 posts, +500,000 visits monthly. _______________________________________________ I think the second website https://hackernoon.com/ is also running buy the same writers, thinkers and storytellers, which include some interesting topics on AI (Artificial intelligence)  & Java scripts and more! https://hackernoon.com/artificial-intelligence/home https://hackernoon.com/javascript/home

[theworldofbots]

1 1/3 What Is  “ #ArtificialGeneralIntelligence “ ?

Part 1 of Intro to #ArtificialGeneralIntelligence Series Part 1 1/3

Via  @TDataScience 

#MikeFerguson on #Medium 

#MediumPublication #TowardsDataScience on #Medium 

[eurekakinginc]

"[D] Learning Resources for Intermediate Machine Learning"- Detail: I'm currently 9 months into my MASc with my thesis focusing on computer vision and human movement analysis with deep learning. Right now I'd say that I'm at an intermediate level in terms of my ML knowledge. I've completed two university courses so far on ML/DL, Andrew Ng's course, as well as a couple other online courses and readings. I also have taken a pattern classification course that gave me a pretty good background on statistics and its relations with ML from linear regression to HMMs.I was wondering if anyone knows of any good resources that I can turn to now. Specifically in the area of computer vision or DL would be useful. I find that many websites, like towardsdatascience, end up being the same basics that I've seen many times. I'm open to any types of resources really: textbooks, papers, youtube videos, etc.Also, I was wondering if anyone has experience with auditing classes (just sitting in and listening) during their MASc or PhD. Is it worthwhile?. Caption by NoEarlyStopping. Posted By: www.eurekaking.com