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daggerzine · 3 years

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Twin City revolt! A chat with Christian from Minneapolis label mpls ltd.

It was September 2019 and I was at my first Gonerfest in Memphis. I had posted a few pics of the bands I’d seen when a pal from Mpls named Amy messages back, “Oh, my friend Christian is there. You and he should really meet since you’re both huge music fans!” Within the next hour Christian Fritz and I were face to face and chatting music.

Not only did we like a lot of the same bands/labels/scenes (namely Sarah Records, C86, Flying Nun, to name but a few) but he told me that he had a label as well, one called mpls ltd. He was nice enough to send me a bevy of records that he’d released and it was everything from cheery/noisy indie rockers Sass to tripped out freaks like Flavor Crystals and everything in between. Yes, Christian is documenting the varied and talented music scene in the Twin Cities and beyond. Read the interview, check out some of his bands (or all of ‘em) and you may learn a thing or two and discover a new favorite. After all he is “Bringing the sounds of Minneapolis to the world, and the sounds of the world to Minneapolis.”

When did the music bug first hit you? Was there much music in your house growing up?

It goes back literally as far as I can remember. My mom gave me a Singer tabletop record player that she got with her sewing machine when I was maybe 4 or 5 years old. My dad’s albums were off limits, but he had a box with maybe a couple of hundred ‘60s pop and country 45s that I would play for hours at a time. I used to tell everybody that record player was my best friend which would terrify my parents.

Do you remember the first record you ever bought?

I grew up in rural Minnesota, and the first two 7”s I bought together with my own money at the local Ben Franklin were John Lennon’s “Nobody Told Me” and Nena’s “99 Luftballoons.”

First concert? First indie/punk show?

Probably my first live music experience was seeing Billy Joe Royal of “Down in the Boondocks” fame with my family during one of our outings to the Minnesota State Fair at one of the free stages there, but my first real show was seeing Sonic Youth on the Daydream Nation tour at First Avenue when I was 16. It was my first time being in downtown Minneapolis, and the entire experience was mesmerizing for this young rural hillbilly. My friends and I worked our way to the front, and Kim Gordon stomped on my hand when I idiotically tried to reach for her bass pedal mid-set (I totally deserved it and wore that bruise like a badge of honor for days). One of my friends got kicked out for violating First Avenue’s then “stage dive three times and you’re out” policy. While in retrospect I cringe at a lot of my behavior that night, it was also when I finally discovered a world I hoped to be a part of really did exist.

Tell us about the beginnings of the label, when did it start and why? How did you decide on the name?

For as long as I have been in love with music, I have also wanted to make records. I even remember drawing records with crayons at an early age and trying to figure out a way to make them play (unsuccessfully). When I was in college at Saint Cloud State University in the early ‘90s, I tried my first attempt at starting a label called Timbre and released a 7'' by a local punk band called The Nothingheads. I was contacted by someone in Seattle who said they already had that label name (to this day I have never seen a release), so I changed it to Idyllwise which was a word I made up so that nobody else could take it. Josh Koch from Saint Cloud had moved to Australia and joined the band Patterson’s Curse with Matty Whittle who was in God and Greg Bainbridge who had played with Kim Salmon, so I jumped at the chance to release their debut EP in the States. Future releases were in the works from Minnesota bands Chast and Silver Rocket Band but I really had no idea what I was doing, got in way over my head, and the whole thing fell apart.

Flash forward to 1999: I had been working on my own breakbeat tracks, and decided to surprise some friends and make a professional looking demo by having a 7” pressed. Even though I had no intention of launching a label, I wanted the record to look like it was on one. I was listening to a lot of 70s soul on Philadelphia International at that time, and it occurred to me that I was living in Minneapolis and I didn’t have any money. Hence, “mpls ltd” (Minneapolis Limited) was born.

Although I was involved with a few releases, things didn’t really evolve from “vanity project” to “label” until 2004. My good friend Vince Caro had just finished recording his band Basement Apartment’s latest album “Transistor!” and asked if he could use the mpls ltd name just to have the CD released on a “label.” I asked to hear it first. We were listening to it in my living room and were probably somewhere around track #5 when I knew I wanted to be involved, and asked if we could partner together on the release. That changed everything.

Were there other labels out there that influenced you to start a label?

Princeton Minnesota had a short-lived gem of an independent record store when I was in high school called McCarty Music that really didn’t fit the community, but I was so glad it was there for as long as it was. Carolyn who ran the store introduced me to the sounds of many brilliant bands on labels like 4AD, SST, and Touch and Go. Also as soon as we got driver’s licenses, my friends and I would drive from Princeton to Minneapolis just to go to Northern Lights or Let It Be Records any chance we got. I loved how those breathtaking early Sarah Records 7”s all included postcards with pictures from Bristol, and I would also buy anything on Creation that I could get my hands on. The brilliant bands from all over this earth that somehow found their way to Homestead Records before the days of the internet also had a major impact on me, in that “someday I hope I can do this too!”

How many releases does Mpls Ltd have so far?

52 as of the end of 2020, plus seven 7”s from a short-lived “super split singles club.”

How did you get the records out to the world in the beginning (and even now)? Distribution?

In addition to my primitive website, I spent a lot of time in the early days pleading with places like Carrot Top and Parasol to carry my releases with mixed results. When Flavor Crystals opened for The Brian Jonestown Massacre on their 2009 US tour, distributors from the UK and Europe started approaching me. Before the Bandcamp era, it felt like my bands could focus on tour sales and I could focus on retail. Now the artists and I discuss how our overall strategy is going to work before we do each release.

Weirdest/worst review one of your records ever got?

I can’t think of any that were terrible, but there were definitely some that were weird. The most recent one that comes to mind was Razorcake’s review of Partition’s “Prodigal Gun” last year. I was stunned when it was reviewed by Rev. Nørb of Maximum Rocknroll fame, but he talked a lot about how purple the vinyl and artwork was, with quotes: “I personally think this record is somewhat cool because it’s so purple, but I realize your situation might be entirely different” and “This vinyl is seriously the purplest vinyl I’ve ever seen in my life. Don’t be surprised if they find Barney dead at the bottom of the vinyl vat.” (He did also say “this sounds kinda like a cross between Exene Cervenka and Kathleen Hanna fronting Flipper” which spoke to me.)

Are all the acts on the label from the Twin Cities or at least Minnesota?

While the label has a stronger Minnesota representation, I’ve released records from bands all over including UK act The Projects whose members included Mira Aroyo of Ladytron, Scotland’s Abagail Grey who was produced by Chris Geddes of Belle & Sebastian and Tony Doogan, and even a 7” from Damo Suzuki of Can.

How do you explain the plethora of great Twin Cities bands over the years?

The amount of amazing music that comes from here past and present is kind of insane! While the Twin Cities Metro is a large area, it’s also small enough where it’s easy to make friends with people in the music scene here. I moved to Minneapolis in 1995 after doing five years of college radio at KVSC, so I was already a superfan of many local bands including The Hang Ups, Babes in Toyland, and Saucer when I arrived. I never imagined getting to be good friends with some of those members. Witnessing the evolution of Real Numbers, I shouldn’t have been surprised as I was to eventually learn that Eli Hansen is as excited about the whole Sarah Records scene and bands like Felt as I am. I got three original members of The Litter onstage at the Kitty Cat Klub for mpls ltd’s 20th anniversary party in 2019, and I am not sure if I can ever top that.

Current favorite bands (anywhere…not just local)?

I have been very much into the new albums from Adele & The Chandeliers, Michael Beach, The Boys with the Perpetual Nervousness, Flyying Colours, Moon Coven, and Jane Weaver so far this year. The Baudelaires who played several shows with Flavor Crystals on their Australian tour are finally getting ready to release their next record, and I cannot wait! I bought a ridiculous amount of fantastic new vinyl throughout the pandemic, and I am beyond excited to finally experience live music again.

Five bands you wish you could release records by (defunct or not)?

Broadcast, Dolly Mixture, The Go-Betweens, Sparks, and Windhand.

Any advice for other people wanting to start a label?

Always expect the unexpected (for example: the pandemic was additionally terrifying while I was waiting for test pressings of two records as I realized that nobody would be touring or playing any shows for quite some time), trust your instincts, and don’t forget this is supposed to be fun. Things will inevitably get stressful at times, but you will also learn as you go. In the immortal words of Gibby Haynes “it’s better to regret something you have done than to regret something you haven’t done.”

What’s next for the label?

I am expecting test pressings for the new album from Minneapolis sludgegazers Another Heaven any day now, and there is a collaborative effort between Flavor Crystals and The Telescopes in the works.

www.mplsltd.com

www.facebook.com/mplsltd

Christian and his assistant

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luxuryltdcars · 6 years

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A Machine Learning Guide for Average Humans

https://ift.tt/2IjrVG3

Posted by alexis-sanders

Machine learning (ML) has grown consistently in worldwide prevalence. Its implications have stretched from small, seemingly inconsequential victories to groundbreaking discoveries. The SEO community is no exception. An understanding and intuition of machine learning can support our understanding of the challenges and solutions Google's engineers are facing, while also opening our minds to ML's broader implications.

The advantages of gaining an general understanding of machine learning include:

Gaining empathy for engineers, who are ultimately trying to establish the best results for users

Understanding what problems machines are solving for, their current capabilities and scientists' goals

Understanding the competitive ecosystem and how companies are using machine learning to drive results

Preparing oneself for for what many industry leaders call a major shift in our society (Andrew Ng refers to AI as a "new electricity")

Understanding basic concepts that often appear within research (it's helped me with understanding certain concepts that appear within Google Brain's Research)

Growing as an individual and expanding your horizons (you might really enjoy machine learning!)

When code works and data is produced, it's a very fulfilling, empowering feeling (even if it's a very humble result)

I spent a year taking online courses, reading books, and learning about learning (...as a machine). This post is the fruit borne of that labor -- it covers 17 machine learning resources (including online courses, books, guides, conference presentations, etc.) comprising the most affordable and popular machine learning resources on the web (through the lens of a complete beginner). I've also added a summary of "If I were to start over again, how I would approach it."

This article isn't about credit or degrees. It's about regular Joes and Joannas with an interest in machine learning, and who want to spend their learning time efficiently. Most of these resources will consume over 50 hours of commitment. Ain't nobody got time for a painful waste of a work week (especially when this is probably completed during your personal time). The goal here is for you to find the resource that best suits your learning style. I genuinely hope you find this research useful, and I encourage comments on which materials prove most helpful (especially ones not included)! #HumanLearningMachineLearning

Executive summary:

Here's everything you need to know in a chart:

Machine Learning Resource

Time (hours)

Cost ($)

Year

Credibility

Code

Math

Enjoyability

Jason Maye's Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

'17

{ML} Recipes with Josh Gordon Playlist

'16

Machine Learning Crash Course

'18

OCDevel Machine Learning Guide Podcast

'17-

Kaggle's Machine Learning Track (part 1)

'17

Fast.ai (part 1)

$70*

'16

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

$25

'17

Udacity's Intro to Machine Learning (Kate/Sebastian)

'15

Andrew Ng's Coursera Machine Learning

'11

iPullRank Machine Learning Guide

'17

Review Google PhD

'17

Caltech Machine Learning on iTunes

'12

Pattern Recognition & Machine Learning by Christopher Bishop

150

$75

'06

N/A

Machine Learning: Hands-on for Developers and Technical Professionals

$50

'15

Introduction to Machine Learning with Python: A Guide for Data Scientists

$25

'16

Udacity's Machine Learning by Georgia Tech

'15

Machine Learning Stanford iTunes by Andrew Ng

'08

N/A

*Free, but there is the cost of running an AWS EC2 instance (~$70 when I finished, but I did tinker a ton and made a Rick and Morty script generator, which I ran many epochs [rounds] of...)

Here's my suggested program:

1. Starting out (estimated 60 hours)

Start with shorter content targeting beginners. This will allow you to get the gist of what's going on with minimal time commitment.

Commit three hours to Jason Maye's Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to.

Commit two hours to watch Google's {ML} Recipes with Josh Gordon YouTube Playlist.

Work through Google's Machine Learning Crash Course.

Start listening to OCDevel's Machine Learning Guide Podcast (skip episodes 1, 3, 16, 21, and 26) in your car, working out, and/or when using hands and eyes for other activities.

Commit two days to working through Kaggle's Machine Learning Track part 1.

2. Ready to commit (estimated 80 hours)

By this point, learners would understand their interest levels. Continue with content focused on applying relevant knowledge as fast as possible.

Commit to Fast.ai 10 hours per week, for 7 weeks. If you have a friend/mentor that can help you work through AWS setup, definitely lean on any support in installation (it's 100% the worst part of ML).

Acquire Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, and read the first two chapters immediately. Then use this as supplemental to the Fast.ai course.

3. Broadening your horizons (estimated 115 hours)

If you've made it through the last section and are still hungry for more knowledge, move on to broadening your horizons. Read content focused on teaching the breadth of machine learning -- building an intuition for what the algorithms are trying to accomplish (whether visual or mathematically).

Start watching videos and participating in Udacity's Intro to Machine Learning (by Sebastian Thrun and Katie Malone).

Work through Andrew Ng's Coursera Machine Learning course.

Your next steps

By this point, you will already have AWS running instances, a mathematical foundation, and an overarching view of machine learning. This is your jumping-off point to determine what you want to do.

You should be able to determine your next step based on your interest, whether it's entering Kaggle competitions; doing Fast.ai part two; diving deep into the mathematics with Pattern Recognition & Machine Learning by Christopher Bishop; giving Andrew Ng's newer Deeplearning.ai course on Coursera; learning more about specific tech stacks (TensorFlow, Scikit-Learn, Keras, Pandas, Numpy, etc.); or applying machine learning to your own problems.

Why am I recommending these steps and resources?

I am not qualified to write an article on machine learning. I don't have a PhD. I took one statistics class in college, which marked the first moment I truly understood "fight or flight" reactions. And to top it off, my coding skills are lackluster (at their best, they're chunks of reverse-engineered code from Stack Overflow). Despite my many shortcomings, this piece had to be written by someone like me, an average person.

Statistically speaking, most of us are average (ah, the bell curve/Gaussian distribution always catches up to us). Since I'm not tied to any elitist sentiments, I can be real with you. Below contains a high-level summary of my reviews on all of the classes I took, along with a plan for how I would approach learning machine learning if I could start over. Click to expand each course for the full version with notes.

In-depth reviews of machine learning courses:

Starting out

Jason Maye's Machine Learning 101 slidedeck: 2 years of head-banging, so you don't have to ↓

Need to Know: A stellar high-level overview of machine learning fundamentals in an engaging and visually stimulating format.

Loved:

Very user-friendly, engaging, and playful slidedeck.

Has the potential to take some of the pain out of the process, through introducing core concepts.

Breaks up content by beginner/need-to-know (green), and intermediate/less-useful noise (specifically for individuals starting out) (blue).

Provides resources to dive deeper into machine learning.

Provides some top people to follow in machine learning.

Disliked:

That there is not more! Jason's creativity, visual-based teaching approach, and quirky sense of humor all support the absorption of the material.

Lecturer:

Jason Mayes:

Senior Creative Technologist and Research Engineer at Google

Masters in Computer Science from University of Bristols

Personal Note: He's also kind on Twitter! :)

Links:

Machine Learning 101 slide deck

Tips on Watching:

Set aside 2-4 hours to work through the deck once.

Since there is a wealth of knowledge, refer back as needed (or as a grounding source).

Identify areas of interest and explore the resources provided.

{ML} Recipes with Josh Gordon ↓

Need to Know: This mini-series YouTube-hosted playlist covers the very fundamentals of machine learning with opportunities to complete exercises.

Loved:

It is genuinely beginner-focused.

They make no assumption of any prior knowledge.

Gloss over potentially complex topics that may serve as noise.

Playlist ~2 hours

Very high-quality filming, audio, and presentation, almost to the point where it had its own aesthetic.

Covers some examples in scikit-learn and TensorFlow, which felt modern and practical.

Josh Gordon was an engaging speaker.

Disliked:

I could not get Dockers on Windows (suggested package manager). This wasn't a huge deal, since I already had my AWS setup by this point; however, a bit of a bummer since it made it impossible to follow certain steps exactly.

Issue: Every time I tried to download (over the course of two weeks), the .exe file would recursively start and keep spinning until either my memory ran out, computer crashed, or I shut my computer down. I sent this to Docker's Twitter account to no avail.

Lecturer:

Josh Gordon:

Developer Advocate for at TensorFlow at Google

Leads Machine Learning advocacy at Google

Member of the Udacity AI & Data Industry Advisory Board

Masters in Computer Science from Columbia University

Links:

Hello World - Machine Learning Recipes #1 (YouTube)

GitHub: Machine Learning Recipes with Josh Gordon

Tips on Watching:

The playlist is short (only ~1.5 hours screen time). However, it can be a bit fast-paced at times (especially if you like mimicking the examples), so set aside 3-4 hours to play around with examples and allow time for installation, pausing, and following along.

Take time to explore code labs.

Google's Machine Learning Crash Course with TensorFlow APIs ↓

Need to Know: A Google researcher-made crash course on machine learning that is interactive and offers its own built-in coding system!

Loved:

Different formats of learning: high-quality video (with ability to adjust speed, closed captioning), readings, quizzes (with explanations), visuals (including whiteboarding), interactive components/ playgrounds, code lab exercises (run directly in your browser (no setup required!))

Non-intimidating

One of my favorite quotes: "You don't need to understand the math to be able to take a look at the graphical interpretation."

Broken down into digestible sections

Introduces key terms

Disliked:

N/A

Lecturers:

Multiple Google researchers participated in this course, including:

Peter Norvig

Director of Research at Google Inc.

Previously he directed Google's core search algorithms group.

He is co-author of Artificial Intelligence: A Modern Approach

D. Sculley

Senior Staff Software Engineer at Google

KDD award-winning papers

Works on massive-scale ML systems for online advertising

Was part of a research ML paper on optimizing chocolate chip cookies

According to his personal website, he prefers to go by "D."

Cassandra Xia

Programmer, Software Engineer at Google

She has some really cool (and cute) projects based on learning statistics concepts interactively

Maya Gupta

Leads Glassbox Machine Learning R&D team at Google

Associate Professor of Electrical Engineering at the University of Washington (2003-2012)

In 2007, Gupta received the PECASE award from President George Bush for her work in classifying uncertain (e.g. random) signals

Gupta also runs Artifact Puzzles, the second-largest US maker of wooden jigsaw puzzles

Sally Goldman

Research Scientist at Google

Co-author of A Practical Guide to Data Structures and Algorithms Using Java

Numerous journals, classes taught at Washington University, and contributions to the ML community

Links:

Machine Learning Crash Course

Tips on Doing:

Actively work through playground and coding exercises

OCDevel's Machine Learning Guide Podcast ↓

Need to Know: This podcast focuses on the high-level fundamentals of machine learning, including basic intuition, algorithms, math, languages, and frameworks. It also includes references to learn more on each episode's topic.

Loved:

Great for trips (when traveling a ton, it was an easy listen).

The podcast makes machine learning fun with interesting and compelling analogies.

Tyler is a big fan of Andrew Ng's Coursera course and reviews concepts in Coursera course very well, such that both pair together nicely.

Covers the canonical resources for learning more on a particular topic.

Disliked:

Certain courses were more theory-based; all are interesting, yet impractical.

Due to limited funding the project is a bit slow to update and has less than 30 episodes.

Podcaster:

Tyler Renelle:

Machine learning engineer focused on time series and reinforcement

Background in full-stack JavaScript, 10 years web and mobile

Creator of HabitRPG, an app that treats habits as an RPG game

Links:

Machine Learning Guide podcast

Machine Learning Guide podcast (iTunes)

Tips on Listening:

Listen along your journey to help solidify understanding of topics.

Skip episodes 1, 3, 16, 21, and 26 (unless their topics interest and inspire you!).

Kaggle Machine Learning Track (Lesson 1) ↓

Need to Know: A simple code lab that covers the very basics of machine learning with scikit-learn and Panda through the application of the examples onto another set of data.

Loved:

A more active form of learning.

An engaging code lab that encourages participants to apply knowledge.

This track offers has a built-in Python notebook on Kaggle with all input files included. This removed any and all setup/installation issues.

Side note: It's a bit different than Jupyter notebook (e.g., have to click into a cell to add another cell).

Each lesson is short, which made the entire lesson go by very fast.

Disliked:

The writing in the first lesson didn't initially make it clear that one would need to apply the knowledge in the lesson to their workbook.

It wasn't a big deal, but when I started referencing files in the lesson, I had to dive into the files in my workbook to find they didn't exist, only to realize that the knowledge was supposed to be applied and not transcribed.

Lecturer:

Dan Becker:

Data Scientist at Kaggle

Undergrad in Computer Science, PhD in Econometrics

Supervised data science consultant for six Fortune 100 companies

Contributed to the Keras and Tensorflow libraries

Finished 2nd (out of 1353 teams) in $3 million Heritage Health Prize data mining competition

Speaks at deep learning workshops at events and conferences

Links:

https://www.kaggle.com/learn/machine-learning

Tips on Doing:

Read the exercises and apply to your dataset as you go.

Try lesson 2, which covers more complex/abstract topics (note: this second took a bit longer to work through).

Ready to commit

Fast.ai (part 1 of 2) ↓

Need to Know: Hands-down the most engaging and active form of learning ML. The source I would most recommend for anyone (although the training plan does help to build up to this course). This course is about learning through coding. This is the only course that I started to truly see the practical mechanics start to come together. It involves applying the most practical solutions to the most common problems (while also building an intuition for those solutions).

Loved:

Course Philosophy:

Active learning approach

"Go out into the world and understand underlying mechanics (of machine learning by doing)."

Counter-culture to the exclusivity of the machine learning field, focusing on inclusion.

"Let's do shit that matters to people as quickly as possible."

Highly pragmatic approach with tools that are currently being used (Jupyter Notebooks, scikit-learn, Keras, AWS, etc.).

Show an end-to-end process that you get to complete and play with in a development environment.

Math is involved, but is not prohibitive. Excel files helped to consolidate information/interact with information in a different way, and Jeremy spends a lot of time recapping confusing concepts.

Amazing set of learning resources that allow for all different styles of learning, including:

Video Lessons

Notes

Jupyter Notebooks

Assignments

Highly active forums

Resources on Stackoverflow

Readings/resources

Jeremy often references popular academic texts

Jeremy's TEDx talk in Brussels

Jeremy really pushes one to do extra and put in the effort by teaching interesting problems and engaging one in solving them.

It's a huge time commitment; however, it's worth it.

All of the course's profits are donated.

Disliked:

Overview covers their approach to learning (obviously I'm a fan!). If you're already drinking the Kool-aid, skip past.

I struggled through the AWS setup (13-minute video) for about five hours (however, it felt so good when it was up and running!).

Because of its practicality and concentration on solutions used today to solve popular problem types (image recognition, text generation, etc.), it lacks breadth of machine learning topics.

Lecturers:

Jeremy Howard:

Distinguished Research Scientist at the University of San Francisco

Faculty member at Singularity University

Young Global Leader with the World Economic Forum

Founder of Enlitic (the first company to apply deep learning to medicine)

Former President and Chief Scientist of the data science platform Kaggle

Rachel Thomas:

PhD in Math from Duke

One of Forbes' "20 Incredible Women Advancing AI Research"

Researcher-in-residence at the University of San Francisco Data Institute

Teaches in the Masters in Data Science program

Links:

http://course.fast.ai/start.html

http://wiki.fast.ai/index.php/Main_Page

https://github.com/fastai/courses/tree/master/deeplearning1/nbs

Tips on Doing:

Set expectations with yourself that installation is going to probably take a few hours.

Prepare to spend about ~70 hours for this course (it's worth it).

Don't forget to shut off your AWS instance.

Balance out machine learning knowledge with a course with more breadth.

Consider giving part two of the Fast.ai program a shot!

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems ↓

Need to Know: This book is an Amazon best seller for a reason. It covers a lot of ground quickly, empowers readers to walk through a machine learning problem by chapter two, and contains practical up-to-date machine learning skills.

Loved:

Book contains an amazing introduction to machine learning that briskly provides an overarching quick view of the machine learning ecosystem.

Chapter 2 immediately walks the reader through an end-to-end machine learning problem.

Immediately afterwards, Aurélien pushes a user to attempt to apply this solution to another problem, which was very empowering.

There are review questions at the end of each chapter to ensure on has grasped the content within the chapter and to push the reader to explore more.

Once installation was completed, it was easy to follow and all code is available on GitHub.

Chapters 11-14 were very tough reading; however, they were a great reference when working through Fast.ai.

Contains some powerful analogies.

Each chapter's introductions were very useful and put everything into context. This general-to-specifics learning was very useful.

Disliked:

Installation was a common source of issues during the beginning of my journey; the text glided over this. I felt the frustration that most people experience from installation should have been addressed with more resources.

Writer:

Aurélien Géron:

Led the YouTube video classification team from 2013 to 2016

Currently a machine Learning consultant

Founder and CTO of Wifirst and Polyconseil

Published technical books (on C++, Wi-Fi, and Internet architectures)

Links:

https://www.amazon.com/_/dp/1491962291?tag=oreilly20-20

http://shop.oreilly.com/product/0636920052289.do

https://github.com/ageron/handson-ml

Tips on Using:

Get a friend with Python experience to help with installation.

Read the introductions to each chapter thoroughly, read the chapter (pay careful attention to code), review the questions at the end (highlight any in-text answer), make a copy of Aurélien's GitHub and make sure everything works on your setup, re-type the notebooks, go to Kaggle and try on other datasets.

Broadening your horizons

Udacity: Intro to Machine Learning (Kate/Sebastian) ↓

Need to Know: A course that covers a range of machine learning topics, supports building of intuition via visualization and simple examples, offers coding challenges, and a certificate (upon completion of a final project). The biggest challenge with this course is bridging the gap between the hand-holding lectures and the coding exercises.

Loved:

Focus on developing a visual intuition on what each model is trying to accomplish.

This visual learning mathematics approach is very useful.

Cover a vast variety and breadth of models and machine learning basics.

In terms of presenting the concept, there was a lot of hand-holding (which I completely appreciated!).

Many people have done this training, so their GitHub accounts can be used as reference for the mini-projects.

Katie actively notes documentation and suggests where viewers can learn more/reference material.

Disliked:

All of the conceptual hand-holding in the lessons is a stark contrast to the challenges of installation, coding exercises, and mini-projects.

This is the first course started and the limited instructions on setting up the environment and many failed attempts caused me to break down crying at least a handful of times.

The mini-projects are intimidating.

There is extra code added to support the viewers; however, it's done so with little acknowledgement as to what it's actually doing. This made learning a bit harder.

Lecturer:

Caitlin (Katie) Malone:

Director of Data Science Research and Development at Civis Analytics

Stanford PhD in Experimental Particle Physics

Intern at Udacity in summer 2014

Graduate Researcher at the SLAC National Accelerator Laboratory

https://www6.slac.stanford.edu/

Podcaster with Ben Jaffe (currently Facebook UI Engineer and a music aficionado) on a machine learning podcast Linear Digressions (100+ episodes)

Sebastian Thrun:

CEO of the Kitty Hawk Corporation

Chairman and co-founder of Udacity

One of my favorite Sebastian quotes: "It occurred to me, I could be at Google and build a self-driving car, or I can teach 10,000 students how to build self-driving cars."

Former Google VP

Founded Google X

Led development of the robotic vehicle Stanley

Professor of Computer Science at Stanford University

Formerly a professor at Carnegie Mellon University.

Links:

https://www.udacity.com/course/intro-to-machine-learning--ud120

Udacity also offers a next step, the Machine Learning Engineer Nanodegree, which will set one back about $1K.

Tips on Watching:

Get a friend to help you set up your environment.

Print mini-project instructions to check off each step.

Andrew Ng's Coursera Machine Learning Course ↓

Need to Know: The Andrew Ng Coursera course is the most referenced online machine learning course. It covers a broad set of fundamental, evergreen topics with a strong focus in building mathematical intuition behind machine learning models. Also, one can submit assignments and earn a grade for free. If you want to earn a certificate, one can subscribe or apply for financial aid.

Loved:

This course has a high level of credibility.

Introduces all necessary machine learning terminology and jargon.

Contains a very classic machine learning education approach with a high level of math focus.

Quizzes interspersed in courses and after each lesson support understanding and overall learning.

The sessions for the course are flexible, the option to switch into a different section is always available.

Disliked:

The mathematic notation was hard to process at times.

The content felt a bit dated and non-pragmatic. For example, the main concentration was MATLAB and Octave versus more modern languages and resources.

Video quality was less than average and could use a refresh.

Lecturer:

Andrew Ng:

Adjunct Professor, Stanford University (focusing on AI, Machine Learning, and Deep Learning)

Co-founder of Coursera

Former head of Baidu AI Group

Founder and previous head of Google Brain (deep learning) project

Former Director of the Stanford AI Lab

Chairman of the board of Woebot (a machine learning bot that focuses on Cognitive Behavior Therapy)

Links:

https://www.coursera.org/learn/machine-learning/

Andrew Ng recently launched a new course (August 2017) called DeepLearning.ai, a ~15 week course containing five mini-courses ($49 USD per month to continue learning after trial period of 7 days ends).

Course: https://www.coursera.org/specializations/deep-learning

Course 1: Neural Networks and Deep Learning

Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Course 3: Structuring Machine Learning Projects

Course 4: Convolutional Neural Networks

Course 5: Sequence Models

Tips on Watching:

Be disciplined with setting aside timing (even if it's only 15 minutes a day) to help power through some of the more boring concepts.

Don't do this course first, because it's intimidating, requires a large time commitment, and isn't a very energizing experience.

Additional machine learning opportunities

iPullRank Machine Learning Guide ↓

Need to Know: A machine learning e-book targeted at marketers.

Loved:

Targeted at marketers and applied to organic search.

Covers a variety of machine learning topics.

Some good examples, including real-world blunders.

Gives some practical tools for non-data scientists (including: MonkeyLearn and Orange)

I found Orange to be a lot of fun. It struggled with larger datasets; however, it has a very visual interface that was more user-friendly and offers potential to show some pretty compelling stories.

Example: World Happiness Dataset by:

X-axis: Happiness Score

Y-axis: Economy

Color: Health

Disliked:

Potential to break up content more with relevant imagery -- the content was very dense.

Writers:

iPullRank Team (including Mike King):

Mike King has a few slide decks on the basics of machine learnings and AI

iPullRank has a few data scientists on staff

Links:

http://ipullrank.com/machine-learning-guide/

Tips on Reading:

Read chapters 1-6 and the rest depending upon personal interest.

Review Google PhD ↓

Need to Know: A two-hour presentation from Google's 2017 IO conference that walks through getting 99% accuracy on the MNIST dataset (a famous dataset containing a bunch of handwritten numbers, which the machine must learn to identify the numbers).

Loved:

This talk struck me as very modern, covering the cutting edge.

Found this to be very complementary to Fast.ai, as it covered similar topics (e.g. ReLu, CNNs, RNNs, etc.)

Amazing visuals that help to put everything into context.

Disliked:

The presentation is only a short conference solution and not a comprehensive view of machine learning.

Also, a passive form of learning.

Presenter:

Martin Görner:

Developer Relations, Google (since 2011)

Started Mobipocket, a startup that later became the software part of the Amazon Kindle and its mobile variants

Links:

Part 1 - https://www.youtube.com/watch?v=u4alGiomYP4

Part 2 - https://www.youtube.com/watch?v=fTUwdXUFfI8

Tips on Watching:

Google any concepts you're unfamiliar with.

Take your time with this one; 2 hours of screen time doesn't count all of the Googling and processing time for this one.

Caltech Machine Learning iTunes ↓

Need to Know: If math is your thing, this course does a stellar job of building the mathematic intuition behind many machine learning models. Dr. Abu-Mostafa is a raconteur, includes useful visualizations, relevant real-world examples, and compelling analogies.

Loved:

First and foremost, this is a real Caltech course, meaning it's not a watered-down version and contains fundamental concepts that are vital to understanding the mechanics of machine learning.

On iTunes, audio downloads are available, which can be useful for on-the-go learning.

Dr. Abu-Mostafa is a skilled speaker, making the 27 hours spent listening much easier!

Dr. Abu-Mostafa offers up some strong real-world examples and analogies which makes the content more relatable.

As an example, he asks students: "Why do I give you practice exams and not just give you the final exam?" as an illustration of why a testing set is useful. If he were to just give students the final, they would just memorize the answers (i.e., they would overfit to the data) and not genuinely learn the material. The final is a test to show how much students learn.

The last 1/2 hour of the class is always a Q&A, where students can ask questions. Their questions were useful to understanding the topic more in-depth.

The video and audio quality was strong throughout. There were a few times when I couldn't understand a question in the Q&A, but overall very strong.

This course is designed to build mathematical intuition of what's going on under the hood of specific machine learning models.

Caution: Dr. Abu-Mostafa uses mathematical notation, but it's different from Andrew Ng's (e.g., theta = w).

The final lecture was the most useful, as it pulled a lot of the conceptual puzzle pieces together. The course on neural networks was a close second!

Disliked:

Although it contains mostly evergreen content, being released in 2012, it could use a refresh.

Very passive form of learning, as it wasn't immediately actionable.

Lecturer:

Dr. Yaser S. Abu-Mostafa:

Professor of Electrical Engineering and Computer Science at the California Institute of Technology

Chairman of Machine Learning Consultants LLC

Serves on a number of scientific advisory boards

Has served as a technical consultant on machine learning for several companies (including Citibank).

Multiple articles in Scientific American

Links:

https://work.caltech.edu/telecourse.html

https://itunes.apple.com/us/course/machine-learning/id515364596

Tips on Watching:

Consider listening to the last lesson first, as it pulls together the course overall conceptually. The map of the course, below, was particularly useful to organizing the information taught in the courses.

Image source: http://work.caltech.edu/slides/slides18.pdf

"Pattern Recognition & Machine Learning" by Christopher Bishop ↓

Need to Know: This is a very popular college-level machine learning textbook. I've heard it likened to a bible for machine learning. However, after spending a month trying to tackle the first few chapters, I gave up. It was too much math and pre-requisites to tackle (even with a multitude of Google sessions).

Loved:

The text of choice for many major universities, so if you can make it through this text and understand all of the concepts, you're probably in a very good position.

I appreciated the history aside sections, where Bishop talked about influential people and their career accomplishments in statistics and machine learning.

Despite being a highly mathematically text, the textbook actually has some pretty visually intuitive imagery.

Disliked:

I couldn't make it through the text, which was a bit frustrating. The statistics and mathematical notation (which is probably very benign for a student in this topic) were too much for me.

The sunk cost was pretty high here (~$75).

Writer:

Christopher Bishop:

Laboratory Director at Microsoft Research Cambridge

Professor of Computer Science at the University of Edinburgh

Fellow of Darwin College, Cambridge

PhD in Theoretical Physics from the University of Edinburgh

Links:

https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_2?ie=UTF8&qid=1516839475&sr=8-2&keywords=Pattern+Recognition+%26+Machine+Learning

Tips on Reading:

Don't start your machine learning journey with this book.

Get a friend in statistics to walk you through anything complicated (my plan is to get a mentor in statistics).

Consider taking a (free) online statistics course (Khan Academy and Udacity both have some great content on statistics, calculus, math, and data analysis).

Machine Learning: Hands-on for Developers and Technical Professionals ↓

Need to Know: A fun, non-intimidating end-to-end launching pad/whistle stop for machine learning in action.

Loved:

Talks about practical issues that many other sources didn't really address (e.g. data-cleansing).

Covered the basics of machine learning in a non-intimidating way.

Offers abridged, consolidated versions of the content.

Added fun anecdotes that makes it easier to read.

Overall the writer has a great sense of humor.

Writer talks to the reader as if they're a real human being (i.e., doesn't expect you to go out and do proofs; acknowledges the challenge of certain concepts).

Covers a wide variety of topics.

Because it was well-written, I flew through the book (even though it's about ~300 pages).

Disliked:

N/A

Writer:

Jason Bell:

Technical architect, lecturer, and startup consultant

Data Engineer at MastodonC

Former section editor for Java Developer's Journal

Former writer on IBM DeveloperWorks

Links:

https://www.amazon.com/Machine-Learning-Hands-Developers-Professionals/dp/1118889061

https://www.wiley.com/en-us/Machine+Learning%3A+Hands+On+for+Developers+and+Technical+Professionals-p-9781118889060

Jason's Blog: https://dataissexy.wordpress.com/

Tips on Reading:

Download and explore Weka's interface beforehand.

Give some of the exercises a shot.

Introduction to Machine Learning with Python: A Guide for Data Scientists ↓

Need to Know: This was a was a well-written piece on machine learning, making it a quick read.

Loved:

Quick, smooth read.

Easy-to-follow code examples.

The first few chapters served as a stellar introduction to the basics of machine learning.

Contain subtle jokes that add a bit of fun.

Tip to use the Python package manager Anaconda with Jupyter Notebooks was helpful.

Disliked:

Once again, installation was a challenge.

The "mglearn" utility library threw me for a loop. I had to reread the first few chapters before I figured out it was support for the book.

Although I liked the book, I didn't love it. Overall it just missed the "empowering" mark.

Writers:

Andreas C. Müller:

PhD in Computer Science

Lecturer at the Data Science Institute at Columbia University

Worked at the NYU Center for Data Science on open source and open science

Former Machine Learning Scientist at Amazon

Speaks often on Machine Learning and scikit-learn (a popular machine learning library)

And he makes some pretty incredibly useful graphics, such as this scikit-learn cheat sheet:

Image source: http://peekaboo-vision.blogspot.com/2013/01/machin...

Sarah Guido:

Former senior data scientist at Mashable

Lead data scientist at Bitly

2018 SciPy Conference Data Science track co-chair

Links:

https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413/ref=sr_1_7?s=books&ie=UTF8&qid=1516734322&sr=1-7&keywords=python+machine+learning

http://shop.oreilly.com/product/0636920030515.do

Tips on Reading:

Type out code examples.

Beware of the "mglearn" utility library.

Udacity: Machine Learning by Georgia Tech ↓

Need to Know: A mix between an online learning experience and a university machine learning teaching approach. The lecturers are fun, but the course still fell a bit short in terms of active learning.

Loved:

This class is offered as CS7641 at Georgia Tech, where it is a part of the Online Masters Degree. Although taking this course here will not earn credit towards the OMS degree, it's still a non-watered-down college teaching philosophy approach.

Covers a wide variety of topics, many of which reminded me of the Caltech course (including: VC Dimension versus Bayesian, Occam's razor, etc.)

Discusses Markov Decision Chains, which is something that didn't really come up in many other introductory machine learning course, but they are referenced within Google patents.

The lecturers have a great dynamic, are wicked smart, and displayed a great sense of (nerd) humor, which make the topics less intimidating.

The course has quizzes, which give the course a slight amount of interaction.

Disliked:

Some videos were very long, which made the content a bit harder to digest.

The course overall was very time consuming.

Despite the quizzes, the course was a very passive form of learning with no assignments and little coding.

Many videos started with a bunch of content already written out. Having the content written out was probably a big time-saver, but it was also a bit jarring for a viewer to see so much information all at once, while also trying to listen.

It's vital to pay very close attention to notation, which compounds in complexity quickly.

Tablet version didn't function flawlessly: some was missing content (which I had to mark down and review on a desktop), the app would crash randomly on the tablet, and sometimes the audio wouldn't start.

There were no subtitles available on tablet, which I found not only to be a major accessibility blunder, but also made it harder for me to process (since I'm not an audio learner).

Lecturer:

Michael Littman:

Professor of Computer Science at Brown University.

Was granted a patent for one of the earliest systems for Cross-language information retrieval

Perhaps the most interesting man in the world:

Been in two TEDx talks

How I Learned to Stop Worrying and Be Realistic About AI

A Cooperative Path to Artificial Intelligence

During his time at Duke, he worked on an automated crossword solver (PROVERB)

Has a Family Quartet

He has appeared in a TurboTax commercial

Charles Isbell:

Professor and Executive Associate Dean at School of Interactive Computing at Georgia Tech

Focus on statistical machine learning and "interactive" artificial intelligence.

Links:

https://www.udacity.com/course/machine-learning--ud262

Tips on Watching:

Pick specific topics of interest and focusing on those lessons.

Andrew Ng's Stanford's Machine Learning iTunes ↓

Need to Know: A non-watered-down Stanford course. It's outdated (filmed in 2008), video/audio are a bit poor, and most links online now point towards the Coursera course. Although the idea of watching a Stanford course was energizing for the first few courses, it became dreadfully boring. I made it to course six before calling it.

Loved:

Designed for students, so you know you're not missing out on anything.

This course provides a deeper study into the mathematical and theoretical foundation behind machine learning to the point that the students could create their own machine learning algorithms. This isn't necessarily very practical for the everyday machine learning user.

Has some powerful real-world examples (although they're outdated).

There is something about the kinesthetic nature of watching someone write information out. The blackboard writing helped me to process certain ideas.

Disliked:

Video and audio quality were pain to watch.

Many questions asked by students were hard to hear.

On-screen visuals range from hard to impossible to see.

Found myself counting minutes.

Dr. Ng mentions TA classes, supplementary learning, but these are not available online.

Sometimes the video showed students, which I felt was invasive.

Lecturer:

Andrew Ng (see above)

Links:

https://itunes.apple.com/us/course/machine-learning/id495053006

https://www.youtube.com/watch?v=UzxYlbK2c7E

Tips on Watching:

Only watch if you're looking to gain a deeper understanding of the math presented in the Coursera course.

Skip the first half of the first lecture, since it's mostly class logistics.

Additional Resources

Fast.ai (part 2) - free access to materials, cost for AWS EC2 instance

Deeplearning.ai - $50/month

Udacity Machine Learning Engineer Nanodegree - $1K

https://machinelearningmastery.com/

Motivations and inspiration

If you're wondering why I spent a year doing this, then I'm with you. I'm genuinely not sure why I set my sights on this project, much less why I followed through with it. I saw Mike King give a session on Machine Learning. I was caught off guard, since I knew nothing on the topic. It gave me a pesky, insatiable curiosity itch. It started with one course and then spiraled out of control. Eventually it transformed into an idea: a review guide on the most affordable and popular machine learning resources on the web (through the lens of a complete beginner). Hopefully you found it useful, or at least somewhat interesting. Be sure to share your thoughts or questions in the comments!

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

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maritalblitz-blog · 7 years

Text

Week 1 Postgame Analysis & Owner Interviews

The Fighting Maria Chanders v. Father of Dragons:

I'm sure at some point Stephanie Haltiwanger has been on the receiving end of

Maria's belt, but this week it was Russell who tasted her leather as he laid

across MC's knee. After only one week, the "Father of Dragons" might consider

changing his name to "Christian Grey" because it's as if he was BEGGING to

feel the sting of punishment just one more time.

Make no mistake, this game started on shaky ground for the MC's after their

#1 and #2 picks (Mike Evans & Jay Ajayi) were sidelined by hurricane Irma.

But their shoes were easily filled by Ty Montgomery (GB, 18 pts) and DeAndre

Hopkins (Hou, 18 pts). Capped-off with surprising production from a 7th-round

replacement WR, Stefon Diggs (Min, 28 pts), and a HUGE game by The Jaguar

Defense (29 pts).

Father of Dragons simply failed to reap the reward of the owner's risky

line-up. For instance, it was a bold move to start Odell Beckham Jr.(0 pts)

when he was reportedly out for week one's match-up against Dallas. Also,

starting rookie Joe Mixon (4 pts) at the RB2 position when he is listed as

the #3 RB in Cinci below Jeremy Hill and Giovani Bernard.

After the game, The Fighting Maria Chandler's owner Beau Bailey was asked

about his teams performance without his first or second round draft picks.

Bailey commented, "Yeah, I'm really proud of this team. It shows their

'next-man-up' mentality going into this season, and now we'll start preparing

for the Whitley's."

Final Score The Fighting Maria Chandlers-126, Father of Dragons-78

===================

Jack in the Cox v. Little Red Fournette:

In Game #2, experts are saying that Little Red Fournette didn't have the

horse-power to catch up to the lead Jack in the Cox established in the very

first game of the NFL Season, and simply ran out of gas after Sunday's early

games.

Jack in the Cox jumped out to a HUGE lead when rookie RB and 4th-round draft

pick Kareem Hunt (KC) exposed the Patriot defense scoring 46 points. Turns

out, JC's owners literally slept on this sleeper telling our insiders they

didn't even find out about his performance until the next morning. Well sleep

no more on Kareem Hunt, this rookie from the University of Toledo has

officially rocketed up the ranks of RBs in the NFL.

But Hunt wasn't the only rookie RB to go off in week one of this match-up.

The namesake of Little Red Fournette, Leonard Fournette (Jax), pounced on the

board scoring 21 points against a very stout Houston defense. But the

rookie's first career TD was the only one scored by LRF's position players.

Our reporters spoke to the owners after the game. When asked about his team's

week 1, one of JC's owners tempered their expectations moving forward saying,

"It's only week one, but it's a good start." It seems as if they feel like

they have more to prove this year, and it's going to take more than one good

week to achieve their goals.

We asked one of the LRF's owners what they thought about the week and their

team going into week 2?

She simply responded, "Crapola."

Final Score: Jack in the Cox-131, Little Red Fournette-93 ===================

Team Swinderman v. Team Nichols

In the battle of First Overall Draft Pick vs. Second Overall Autodraft Pick,

the owner of Team Swinderman's frustrations have nearly reached a feverpitch.

The first pick in a draft is very valuable. A few years ago, then

commissioner Caroline Glazebrook parlayed that pick into a championship. So

to see Le'Veon Bell (Pit, RB), who has more apostrophes in his first name

than TDs this season, only score 7 points left at least one of the owners

from Team Swinderman rather frustrated. Add that to their second and third

round picks (Lamar Miller, RB, Hou & Tom Brady, QB, NE) and the top three

picks of this promising team only scored a combined 30 points.

We asked one of the owners if she had anything to say about her team's week 1

showing. She did. She said, "Yeah, they sucked. I want to redraft. This is

bulls__."

We overheard her later as she took a go the Marital Blitz commissioner

saying, "I've never had a team score that low. Even my bench sucks. This is

your fault!"

Our league insiders tell us they don't think any fines will be handed down

following the confrontation. As a matter of fact, they said the commish

simply smiled, winked at her, then slowly reeled up his middle finger as if

it were a Sunday morning fishing trip. Then coyly mouthed, "O-S-U blows" as

the crazed owner was carried away crying, "I play to win!"

If there is any silver-lining to Team Swinderman's year it comes at a cost to

Team Nichols: at least Team Swinderman's first round pick will survive to

play another week.

While this game is a big win for Team Nichols to start the season, it comes

at the cost of the second overall draft pick, David Johnson (RB, Ari).

Johnson was drafted with extremely high expectations of productivity, but

after suffering a wrist injury in the third quarter, he did not return. Team

Nichols was later informed Johnson dislocated his left wrist which will

require surgery and will be forced to miss the next 8-12 weeks of action.

After the game, the ownership of Team Nichols had an optimistic perspective

of their team simply saying, "Auto-draft. 1st round pick gets hurt. Still

drops 100."

Final Score: Team Swinderman-70, Team Nichols-100 ===================

Team Whitley v. Game of Jones

In the closest match-up of the week, Game of Jones was able to fend off a

late surge from Team Whitley in Monday Night's late game to secure a victory

in a very well fought match.

Our insiders reported one of the owners of Team Whitley was looking to make

some roster moves in the coming weeks after witnessing the poor performance

in their early games, specifically when asked if they were going to carry

three QBs into week 1, saying, "As of now...[the] struggle is real this

week."

But Team Whitley's optimism was palpable heading into the last game of the

week. Their ownership sent out a press release saying, "We like to start at

rock bottom Week 1, but we're still waiting on a Melvin Gordon Miracle

tonight."

It is that type of confidence in their players that led first round draft

pick, Melvin Gorden (RB, LAC), to such a strong week scoring 18 points.

Unfortunately, Demaryius Thomas (WR, Den) wasn't able to keep up his side of

the offense only scoring 11 points.

It was obvious that Game of Jones had a better line-up than Team Whitley, but

moving forward, depth could haunt the two owners of Game of Jones.

Looking to get DeVante Parker (WR, Mia) back from a hurricane-induced bye-

week, and waiting on Doug Martin (RB, TB) to fulfill his three-game

suspension, Game of Jones will be looking to bolster their bench with some

roster moves over the next few weeks.

For example, they had three players (McFadden, Dal RB, Rawls, Sea RB, and

Henry, Lac TE) on their bench who combined scored less points than their

kicker, Adam Vinatieri (2 pts).

As the week of games came to a close, our beat-writer in the field got a

chance to speak with Alicia Walters, one of the owners of Game of Jones,

about their week against Team Whitley and moving forward. She said, "We are

pleased with our Week 1 performance. We dropped a few balls that we wish

we could get back, but it was like we were playing the Jets this weekend, so

we didn't have to be our best. We need to continue to get better and trust the

process."

Final Score: Team Whitley-77, Game of Jones-101 ===================

***WEEK ONE GAME OF THE WEEK*** Sleeping w/the Commissioner v. That's What She Said aka "The Battle of the Bedroom"

By far the most intriguing match-up of Week 1 was a subway series between

two owners who are married to one another. The past three seasons we have

had to wait until Week 2 to get this game, but this year our appetites were

spared and these two went head-2-head to kick off the 2017 season.

TWSS won their first ever meeting back in 2014 by the score of 106-94, but

was blown away in 2015 when SwtC avenged their embarassment 92-58. In

2016, SwtC retained the title Bedroom Belt in a bloody shootout 142-127.

We have become so accustomed to this great rivalry game, but this year,

That's What She Said had too many weapons for Sleeping w/the

Commissioner to ward off.

After witnessing the superiority of TWSS's first round draft pick, Antonio

Brown (WR, Pit)(29 pts), and QB Matt Ryan (Atl)(23 pts), and SwtC's inability

to respond, there wasn't much left ponder "what ifs..."

SwtC's TE, Jimmy Graham (Sea), opened the season looking to regain his

production since leaving New Orleans, but failed only scoring 3 points.

Marshawn Lynch was believed to be healthy and able to rebrand himself as the

premiere RB in Oakland, but failed only scoring 9 points. Even his own

favorite team's defense (Atl) could not help their owner overcome a loss only

scoring 5 points against a very poor Chicago team. Which left SwtC's Stephan

Glazebrook handing over the Battle of the Bedroom Belt late Monday night.

After the game, our reporters were in the press conference and got to hear

from the owner of SwtCs, Stephan Glazebrook. Disappointment surrounded the

young executive as he took responsibility for his teams lack of production.

However, he promised change was-a-comin', saying, "I would say that, as their

coach, I've failed. However, if they don't perform better this week, I'll be

handing out pink slips to their sorry asses."

As one defeated Glazebrook left the press room and one elated Glazebrook

entered, they exchanged fist bumps and the owner of TWSS patted S

Glazebrook on the butt and told him to keep his head up.

Once TWSS owner Caroline Glazebrook took to the podium, her assistants told

the press she would only be taking one question so she could return to

celebrate with her team. A Fox Sports One reporter asked what she thought of

her team's victory.

She spoke confidently, "No surprise here." And she dropped the mic and

kicked through the doors to a raucous locker room celebration.

Final Score Sleeping w/the Commissioner-105, That's What She Said-135

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