You can’t just look at electricity consumption. You have to look at total energy consumption. Including the energy necessary to produce the goods you consume. And that is increasing exponentially.
In a way, the US (and Western countries) are outsourcing their energy consumption.
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Location: London, UK
Remote: No
Willing to relocate: No
Technologies: Scala, Python, scikit-learn, Apache Spark
Résumé/CV: https://www.linkedin.com/in/ghyslaingaillard
Email: [email protected]
Hi! I am a Data Scientist based in London with a strong background in Engineering Sciences and Business Analytics, and with a passion for entrepreneurship.
Thanks everyone for the positives feedbacks.
I did not have much time yet to write down full reviews of all the books, but I'll work on it - so far this page is more of a personal "bookmark". But to reply to @Nekopa and @carlsednaoui, here is a short review of the first books.
I have had a really pragmatic approach about reading them - only focusing first on parts relevant to my projects.
# An Introduction to Statistical Learning (ISL) / The Elements of Statistical Learning (ESL)
I focused on chapter 8-9 of ISL about Tree Based Methods and SVMs, two algorithms I used for my dissertation project. I found ISL to provide very clear explanations of the algorithms with just enough mathematical formalism.
I have a good math background so ESL was interesting to go through. But I am more of a practical person, and I found ISL to be more suited for me when it came down to working on my project and supporting my choices.
# Python Machine Learning
Really great hands-on book ! Sebastian Raschka manages well to guide you through all steps of a ML project data: pre-processing, feature engineering, model selection... - all the steps are defined and covered with practical examples.
I strongly recommend this book if you are just starting out with ML and feel "lost" about how to start your own project.
# Taming Text
I decided to use text data I had available for my dissertation project. However, half-way through the book I realized my dataset was to small to apply any of the techniques described there. I still like the practical approach and in the end the book gave me a good idea of what can be done with text.
# Advanced Analytics with Spark
I picked this book once I started working on the implementation of my project into production - we use Apache Spark (Scala) at work.
It provided me with a good introduction to Spark BUT it's based on the RDD-api and as stated on Spark website: "As of Spark 2.0, the RDD-based APIs in the spark.mllib package have entered maintenance mode. The primary Machine Learning API for Spark is now the DataFrame-based API in the spark.ml package."
I'm now mostly relying on Spark Doc / API, I'm not aware of any up-to-date books yet :)
I found this paper in the references of a previous similar paper discussed on HN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2801385
I am using it to write a chapter of my dissertation. Here are some relevant quotes from the paper.
TLDR:
>> Introduction
Start-up firms are particularly difficult to finance because their prospects are highly uncertain, they lack tangible assets that can be used as collateral, and they face severe information problems (Hall and Lerner (2010)). Given these problems, how do investors choose which start-ups to fund? What factors drive their selection process? (…)
This paper provides, to the best of our knowledge, the first experimental evidence of the causal impact of start-up characteristics on investor decisions. (…)
Based on competing theories of the firm, we focus on three key characteristics of start-ups: the founding team, the start-up’s traction (such as sales and user base), and the identity of current investors. (…)
We sent approximately 17,000 emails to nearly 4,500 investors on the platform, spanning 21 different start-ups, during the summer of 2013. The randomized experiment reveals that the average investor is highly responsive to information about the founding team, whereas information about traction and current investors does not lead to a significantly higher response rate. This suggests that information about the human capital of the firm is uniquely important to potential investors, even after controlling for information about the start-up’s idea. (…)
We find that the more experienced and successful investors react strongly only to the team information, which provides indirect evidence of the viability of an investment strategy based on selecting on team information.
>> Why do Investors React to Information on Founding Team?
We find evidence that human capital is important at least in part due to the operational capabilities and expertise of the founders. (…)
>> Is it Rational to Invest Based on Founding Team?
It is challenging to answer this question directly, for several reasons (…)
We can, however, take an indirect approach by exploring how successful and experienced investors react to various information (…)
The inexperienced investors with no prior investments, who make up 18% of the sample, react not only to the team information, but also to the traction and current investors.(…)
The experienced investors still only respond to information about the team, while the significance of the response to the traction and current investors categories among inexperienced investors weakens somewhat.(…)
>> Conclusion
Overall, the results in this paper present evidence for the causal importance of human capital assets for the success of early stage firms, and contribute to the debate around the importance of various key assets to organization success. Our results, however, do not suggest that non-human assets are not essential. Rather, the results are consistent with the model by Rajan (2012), in which human capital is initially important for differentiation, but needs to be replaceable in later stages so that outside investors can obtain control rights, thus allowing the firm to raise large amounts of external funding
In a way, the US (and Western countries) are outsourcing their energy consumption.