Founder @ Spawner.ai // ex. AI @ P&G. 👨🏻‍💻 AI/ML, Trading, Data Science DatasetDaily.com twitter.com/poseysthumbs

Books, research papers, and blog posts I’m reading in September. 📚

Note: I am not affiliated with any of the writers in this article. These are simply books and essays that I’m excited to share with you. There are no referrals or a cent going in my pocket from the authors or publishers mentioned; I prefer to align my incentives with the reader rather than the publishers. Reading is a vitamin for the brain; please support your favorite writers and enjoy!

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Another month of our Data Science reading list! // Photo Creds: Unsplash

Welcome to another month of our Data Science reading list! We do these each month, and I love all the positive feedback. I especially like when folks share their own recommendations. Feel free to share your own books, blogs, and anything else you’re reading this month. …

Portfolio(s), positions, analysis, and updates. 📈

Nothing herein is financial advice. Past performance is in no way indicative of future returns. Any listed holdings are not guaranteed to be up to date or accurate. All holdings are delayed. Please do not think mirroring any of my holdings will give you any sort of positive returns or generate alpha. Please use common sense as mileage may vary (considerably).

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I am not invested in the FTSE 100. Well maybe indirectly. Photo Creds: Unsplash

For the full portfolio and commentary you can follow me on Twitter where I’ll announce updates to lukeposey.com/investing. The tables with holdings will update automatically. I’ll do my best to give consistent updates and semi-intelligent rationale for my decisions.

About my portfolio

I’m interested in investing across various asset classes. …

Books and essays I’m reading in August. 📚

Note: I am not affiliated with any of the writers in this article. These are simply books and essays that I’m excited to share with you. There are no referrals or a cent going in my pocket from the authors or publishers mentioned. Reading is a vitamin for the brain; please support your favorite writers and enjoy!

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Photo Creds: Susan Yin on Unsplash

Welcome to this month’s reading list.

Surveying the landscape: present and future.

Applied AI continues to accelerate, largely fueled by the maturation of tooling and infrastructure. Couple this infrastructure with a strong pool of talent and enthusiasm, readily accessible capital, and high customer willingness to adopt AI/ML and you’ve got something special. We’re turning the corner into a new decade where AI/ML will create real value for both the consumer and the enterprise at an accelerating pace.

Defining Terms

Applied AI: anything to do with taking AI research from the lab to a use-case and everything in-between. From the infrastructure and tooling, to the hardware, to the deployment surfaces in industry, to the models themselves, it takes a village to get a bleeding edge advance in AI research to a use-case. One great test for maturation in our field is the time it takes for a new advance to get from paper to production.

One chap’s journey + strategies for continuous improvement. 📈

Before we start

Welcome to the Spawner blog on Medium. We want this blog to fill the gap in finance blogs on this platform. Our goal is to provide you with information that is engaging, accurate, and fun. We’re not here to spam you with listicles or tell you what stocks to buy. We’re here to share knowledge and information, amplify honest and passionate voices, and highlight the journeys of investors across the globe. By following the Spawner publication you can expect content on trading, wealth management, FinTech, and plenty of other related topics.

Thanks for reading!

Motivation

I’m often asked what gets me so excited about investing. Here’s a summary of my investing journey since placing my first limit order. There’s about a 10 year window from the time I started “trading” in high school to present day. Everyone starts their journey somewhere — mine started with a stock market game in high school consumers ed. Here’s the story of where my investing journey has been and where I think it’s going.

Books and essays I’m reading in July. 📚

Note: I am not affiliated with any of the writers in this article. These are simply books and essays that I’m excited to share with you. There are no referrals or a cent going in my pocket from the authors or publishers mentioned. Reading is a vitamin for the brain; please support your favorite writers and enjoy!

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Photo by Debby Hudson on Unsplash

Welcome to this month’s reading list. Slight change to the typical format — this month I’ll go over both some books and essays that I’ve read or plan on reading through the course of July.

On to the list!

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

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I often pick up popular data books that I don’t imagine I’ll enjoy for the sake of this reading list. This is one of those books I didn’t imagine enjoying but ended up really appreciating. I often struggle to pin an audience on books like this, but it would probably service as a spectacular introduction for beginners to the field (with a fairly strong technical background) and as an even better reference for the early-career data professionals. It’s a practical approach and explanation to tons of topics that you might have otherwise gotten overwhelmed by in a textbook. It’s technical enough to make it worth your time, but it’s also pragmatic enough to allow you to continuously identify use-cases and understand where and when knowing the nuts and bolts is relevant to application or even the road to mastery.

Understanding some key metrics on the Spawner platform.

Note: nothing herein is investment advice. Spawner AI, Inc. does not provide investment, tax, or legal advice of any kind. This is not an offer, solicitation of an offer, or advice to buy or sell securities. Everything herein is for entertainment and educational purposes.

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Photo by Stephen Dawson on Unsplash

We’ve noticed an overall lack of awareness of basic portfolio metrics throughout the broader trading community. Many of these metrics and basic approaches are based on decades of research and development of best practices, some more practical than others for the common trader or investor.

Quantifying your portfolio’s risk, exposure, and performance is important to continuously monitor the health, balance, and exposure to market forces. The metrics you follow are a personal choice based on your style and strategies. At Spawner, we decided to focus on a fairly broad set of widely used metrics for portfolio managers. …

Building the future of retail traders’ tooling.

Nothing herein is financial advice. Everything in this article is purely for informational and entertainment purposes. Nothing from Spawner AI, Inc. is a recommendation to buy, sell, trade, or own securities. Please consult a licensed advisor before trading or investing.

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Photo by Tingey Injury Law Firm on Unsplash

Our journey at Spawner has been and continues to be an amazing journey. Interfacing with early users and solidifying our early team continues to make the journey a real pleasure. …

Another month, another reading list. 📚

Note: I am not affiliated with any of the authors in this article. These are simply books that I’m excited to share with you. There are no referrals or a cent going in my pocket from the authors or publishers mentioned. Reading is a vitamin for the brain; please support your favorite authors and enjoy!

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Photo Creds: Unsplash

Welcome to this month’s reading list. This month’s reading list is a mix of Data Science careers, teams, and the future of work.

As we continue to live in an era where remote work dominates many of our lives, we’ve got an important task ahead of understanding both ourselves and the future of our work. Core to this task is understanding an approach that maximizes our happiness and creativity. And I don’t buy into the narrative that peak productivity always means compromising our happiness. …

Effective talent distribution when resource constrained.

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Photo Creds: Unsplash

Building an effective small team starts with accepting that you’re resource-constrained. You need to align realistic expectations and be especially focused on hiring the right talent, else you can expect plenty of talent churn and unfinished projects.

Optimize for your problem space.

I typically classify radar charts in the almost always an inferior choice bin, but for a subjective analysis like this, they’re great fun. After you’ve checked out the radar charts, let us know if we missed any common team compositions. What does the ideal team look like in your field?

Here are a few common examples.

Traditional company with a massive pile of untapped data.

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Pros: This seems to be a pretty good team composition for some early wins for a nascent data organization inside a large company. A healthy mix of Data Engineers and Analysts with a spectacular Senior Data Scientist as the leader or decision maker is a winning combination. …

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