dpmehta02

SEEKING WORK | Back-end Engineer | SF Bay Area or Remote

Experience: 5+ years working with startups as a Senior Backend Engineer (OpenGov) and as a freelancer (building APIs, data pipelines, full-stack MVPs and Machine Learning systems).

Languages: Ruby/Rails, Python

Skills: APIs, data modeling, data pipelines, Natural Language Processing, OOP

Linkedin: https://www.linkedin.com/in/devmehta

Github: https://github.com/dpmehta02

Here's a short essay I recently wrote about eating healthy: http://dpmehta.com/posts/eating-trick.html

Contact: dpmehta02[at]gmail[dot]com

Thanks for the feedback. I thought it would be easier for users to simply slide a scale rather than manually input numbers (which I feel is error prone and more work), especially on a mobile device.

In the next iteration I'm considering allowing users to use either option, sliding scale or direct input.

Thanks for the feedback warrenm. Heuristics can be useful, but I built this calculator to help freelancers account for opportunity costs and unexpected expenses in their rates. For example, someone who makes a $100,000 salary would charge 100,000/2080 = $48/hour by the measure you described. However, I don't believe that rate accounts for PTO, National Holidays, Health Insurance, Business Development time, self-employment taxes and many other things. A more realistic equivalency in my experience (and according to the calculator) would be $100-$125/hour.

Hope that's helpful.

SEEKING WORK | Back-end Engineer | SF Bay Area or Remote

Experience: 5+ years working with startups as a Senior Platform Engineer (OpenGov) and as a freelancer (building APIs, data pipelines, full-stack MVPs and Machine Learning systems).

Skills: APIs, data modeling, data pipelines, Natural Language Processing, OOP

Languages: Ruby/Rails, Python

Linkedin: https://www.linkedin.com/in/devmehta

Github: https://github.com/dpmehta02

Here's a short essay I recently wrote about eating healthy: http://dpmehta.com/eating-trick.html

Contact: dpmehta02[at]gmail[dot]com

SEEKING WORK | Back-end Engineer | San Francisco Bay Area or Remote

Experience: 5+ years working with startups as a Senior Platform Engineer (OpenGov) and as a freelancer (building APIs, data pipelines, full-stack MVPs and Machine Learning systems).

Skills: APIs, data modeling, data pipelines, Natural Language Processing, OOP

Languages: Ruby/Rails, Python

Linkedin: https://www.linkedin.com/in/devmehta

Github: https://github.com/dpmehta02

Here's a short essay I recently wrote about eating healthy: http://dpmehta.com/eating-trick.html

Contact: dpmehta02[at]gmail[dot]com

SEEKING WORK | Back-end Engineer | San Francisco Bay Area or Remote

Experience: 5+ years working with startups as a Senior Platform Engineer (OpenGov) and as a freelancer (building APIs, data pipelines, full-stack MVPs and Machine Learning systems).

Skills: APIs, data modeling, data pipelines, Natural Language Processing, OOP

Languages: Ruby/Rails, Python

Linkedin: https://www.linkedin.com/in/devmehta Github: https://github.com/dpmehta02

Here's a short essay I recently wrote about eating healthy: http://dpmehta.com/eating-trick.html

Contact: dpmehta02[at]gmail[dot]com

Great story.

My first MOOC was also David Malan's CS50x on edX; what an incredible teacher! He brings such a unique enthusiasm and joy to the study of Computer Science. In case you're reading this, thank you Professor Malan!

SEEKING WORK - San Francisco/Remote

Back-end focused, full-stack capable. 2+ years of experience, mostly at startups in San Francisco (including YC companies). I particularly enjoy building APIs, data pipelines and Machine Learning systems, mostly in Python.

Skills: APIs, Python, Flask, MySQL, Postgres, MongoDB, Redis, Ruby/Rails, *nix, Git, Heroku, AWS, Machine Learning, NLP, Analytics, Angular, Google Maps, SOA

Linkedin: https://www.linkedin.com/in/devmehta Github: https://github.com/dpmehta02

Contact: dpmehta02[at]gmail[dot]com

SEEKING WORK - San Francisco - Remote or local (Travel possible) - Web Dev/Machine Learning

Hello fellow HNer.

I have two years of production experience building Web Apps and Machine Learning systems. I also spent three years as a Data Analyst. My goal is to solve problems and create value for my clients.

Skills: Python/Flask, Ruby/Rails, Heroku/AWS, MySQL/Postgres/MongoDB/Redis, NLP, Scikit/Numpy, APIs, Financial modeling, Project Management, the usual suspects (HTML/CSS/jQuery, Git, *nix, bash, etc.)

Most of my code is hidden in client repos, so I built a simple demo API: Code: https://github.com/dpmehta02/demo_flask_api API: https://demo-flask-api.herokuapp.com/v1/users

Website: http://dpmehta02.github.io/

Contact: dpmehta02[at]gmail[dot]com

Location: San Francisco

Remote: Open

Willing to relocate: For the right job

Technologies: Ruby/Rails (full stack), Python/Machine Learning (Scikit-learn, Numpy)

Resume: https://www.linkedin.com/in/devmehta (email for more details)

Email: dpmehta02[at]gmail[dot]com

I've been freelancing for the past year, but now I'm looking for a full-time role. I spent the past few months building a Machine Learning user classification system for a startup.

SEEKING WORK - SF Bay Area - Remote or Local

1+ years experience with Web Development and Machine Learning.

Web: Full stack Ruby/Rails, Git, Linux/Unix, Flask, Heroku, AWS, MySQL/Postgres, MongoDB, APIs, jQuery, Bootstrap, Haml, Redis

Machine Learning: Python, Natural Language Processing, Web crawlers, Scikit-Learn, Numpy, Pandas, R

dpmehta02[at]gmail[dot]com

https://github.com/dpmehta02

SEEKING WORK - SF Bay Area - Remote or Local

Web Developer + Machine Learner (1+ years Rails, 1+ years Machine Learning) freelancing while I build my own company.

If you need work done on a Rails app (from MVPs to legacy apps), you need to build a predictive algorithm or you need to crawl/scrape data, contact me.

Web: Full stack Ruby/Rails, Git, Linux/Unix, Flask, Heroku, AWS, MySQL/Postgres, MongoDB, APIs, jQuery, Bootstrap, Haml, Redis

Machine Learning: Python, Natural Language Processing, Web crawlers, Scikit-Learn, Numpy, Pandas, R

dpmehta02[at]gmail[dot]com

https://github.com/dpmehta02

http://www.kaggle.com/users/30845/dpmehta02

SEEKING WORK - SF Bay Area - Remote or Local

Web Developer (Rails) freelancing while I build my startup. In a past life I spent three years as a Data Analyst at PricewaterhouseCoopers. I also play around with Machine Learning in my spare time.

I'm open to contract work at a big company, but I particularly enjoy helping startups and founders get their products off the ground. I'm currently working on that problem myself, so I'd like to think I know the pain points and best practices.

Production experience: Ruby/Rails, TDD, Heroku, MySQL/Postgres, jQuery, Bootstrap, Haml, Crawlers, Redis, ElasticSearch, Python, Git, Unix, AWS

Side projects: MEAN (MongoDB, Express, AngularJS, Node), Node/Meteor, R, C, Octave/Matlab

dpmehta02[at]gmail[dot]com https://github.com/dpmehta02

SEEKING WORK - SF Bay Area - Remote or Local

Software Developer specializing in Web and Data Engineering, freelancing while I build my startup. I spent three years as a Data Analyst, then quit and taught myself to code. I've only been freelancing for six months, so I'm willing to work at a discount while I build up my portfolio. I'm also open to bartering.

Skills: Ruby/Rails, TDD, SQL, Redis, ElasticSearch, Python, R, Machine Learning, Project Management, Git, Linux/Unix, AWS, Heroku

Production experience: Everything associated with large Rails projects, web crawling, data pipelines, APIs, data analysis, product management

Side projects: I've built some apps in Node (Express, Meteor), and I compete in Kaggle Data Science competitions when I have time (http://www.kaggle.com/users/30845/dpmehta02). I am particularly interested in NLP.

dpmehta02[at]gmail[dot]com https://github.com/dpmehta02

This article would have been much more persuasive had it specified the actual decisions made by Google that illustrate exactly how that company has "lost its way" (i.e., chosen profit over solving important problems).

I would work through a short Meteor tutorial before buying the book. The following is an excellent working intro: http://www.smashingmagazine.com/2013/06/13/build-app-45-minu...

Here's some more short and sweet sleep info, straight from the man who founded the world's first sleep research laboratory: http://www.stanford.edu/~dement/sleepless.html

Google Trends info: http://www.google.com/trends/explore?q=kickstarter#q=kicksta...

Because it wastes water, which is something a lot of areas cannot afford: http://www.nytimes.com/2013/08/12/us/to-save-water-parched-s...

All of these terms are used interchangeably, so the following definitions probably wont be too helpful in the real world, but in my experience:

Artificial Intelligence is an umbrella academic term which encapsulates the study and design of intelligent machines. It's not well defined because AI is evolving so rapidly.

Machine Learning is a branch of AI that is concerned specifically with learning from data; the results of learning are usually used to predict future events. (Think linear regressions, random forests, etc.)

Though not specifically a part of AI, Statistics is the field that formed many of the algorithms used in ML. Stats informs ML research design (e.g., how large of a sample size do I need), generates mathematical solutions from proofs and equations, etc. With the rise of big data, it's slowly merging with ML.

Data Mining is a mix of ML, Stats and Data Engineering. It's more concerned with structuring and extracting patterns from data than necessarily learning from it. It is often a task within an ML project.

The 99% of people who use linear regression but would not call it machine learning probably aren't very well versed in regularization, cross-validation, non-linear transformations, feature engineering, data snooping, bagging, boosting, generalization, etc.

A finely tuned linear regression is a devastating machine learning algorithm.

This looks great, thanks for sharing.

Any thoughts or relevant benchmarks you would like to share about its speed?

Another: http://pythex.org/

Hiring is also a consideration. Young, talented engineers often pick the latest "trendy" language/framework as their first, so if you're starting a company and want to hire young talent, you will be at a disadvantage if you don't use Node.js, Rails, Go, etc.

Good info, thanks for sharing. Are you knowledgeable about how much time to spend on cardio and strength training per week for optimal heart health? It's difficult to find reliable information about this subject.

Some claim that elevating one's heart rate (170+ BPM for late 20s individual) for 20-30 minutes three times a week is a good 80/20 solution for aerobic exercise. My current routine is to lift for 50 minutes MWF, jog for 45 minutes Tuesday, interval sprint for 25 minutes Thursday, and take a long hike Saturday. I also bike to work MWF (11 miles round trip) and walk 12k steps a day, so I figure that cancels the need for a third weekly intense aerobic exercise session. Curious to know if you have any heart health improvement suggestions.

Deadlifts, one of the core exercises of SS, is famous for improving posture when performed correctly.

Lifting weights in general has been shown to improve cardiovascular health; obviously not as much as pure cardiovascular training, but the science is pretty conclusive that it has a positive impact on your heart.

Lifting weights prevents injuries by improving bone density, strengthening connective tissue around troublesome joints (knees, rotator cuff, hips, etc.) and preventing the use of poor posture to accomplish everyday tasks (e.g., instead of hunching your back and stressing your spine to carry a heavy ice chest, you can use the muscles of your upper back). It also adds muscle, which increases your BMR (AKA metabolism).

If your main interest is in gaining muscle and looking better, there may be better routines. But Starting Strength is about one thing ... strength. Pure and simple. It never claims otherwise.

Starting Strength has been used as a fundamental building block by athletic trainers for years; it is often modified to be sport specific, but its fundamental lifts have proven effective over and over. After one has reached strength standards, it is typical (and recommended) to move on to a more advanced strength routine, which may explain why many of the big guys you have met are no longer doing a stripped down strength routine.

Yes, those with serious pre-existing injuries may want to opt for a different program (probably machine based), but that will be true of almost any strength training program. Those with only minor injuries can just start SS at a lower weight and improve more slowly.

Hope that helps address your concerns.

Getting enough sleep is important as well. Anecdotally, I have experienced subpar lifting performance when I average less than 8.5 hours/night for a few days.

I also plateaued on Squats during SS after about 4 months, so I lowered my work weight to 80% during my middle squat day (Wednesday in a MWF routine), as Rippetoe suggests, and I immediately broke out of it.

Entitlement is a problem amongst many people who experience success, regardless of class or nationality.

Daniel Kahneman & Amos Tversky have shown this in numerous studies, but here's an easily relatable example of theirs: Financial advisors.[1]

Predicting markets is an inherently random game. We often point to those who have been successful for a long period of time as an example that it is possible to beat the system, but that analysis fails to account for the other end of the probability distribution: those who have failed for a long period of time (or failed so hard early that they had to get out of the game).

In other words, for every big winner, there is a big loser. The fact that some people win or lose can (mostly) be explained by randomness (or cheating).

But Kahneman & Tversky's stunning finding with Financial Advisors, and humans in general, is that those who are successful attribute their success mostly to skill, hard work, etc, while failing to adequately acknowledge how large a role chance played. For the humble, this is not a problem, but for the arrogant or uninformed, this can easily turn into entitlement.

(BTW I definitely believe that, in most fields, a person needs to meet certain thresholds of hard work, energy, intelligence, etc. to be successful, but the level of success after reaching those thresholds is largely a function of chance.)

We humans are very good at drawing false conclusions from random data. Have you watched a basketball game recently? If a good shooter misses a couple free throws, commentators seem obligated to explain that the reason for this is fatigue, or poor form, or this, or that. How about the fact that it just randomly happens sometimes?

Applying this thinking to Indian elites: they have a lot of money, probably through their families or their own success. They believe their status is well deserved and earned, either because of superior genetics or superior skills (or any other number of reasons), and as a result, the uninformed feel entitled (and act accordingly). The author's anecdotal examples aside, this is no different than how many (but not all!) people behave on Wall Street, in athletics, at the high levels of corporations, etc. This is not an Indian problem, or an elite problem; it's a human problem.

Fooled by Randomness[2]. Again.

[1]http://www.businessinsider.com/daniel-kahneman-on-wealth-man... [2]http://www.amazon.com/Fooled-Randomness-Hidden-Chance-Market...

Strongly agree with all of michaelochurch's points, especially #1. Frankly, it baffles me that "career tracks" weren't (and still aren't) a core component of the MOOC product offering. (I actually applied to YC S13 with a solution to points 1-3.)

Duke researchers who actually taught a course on Coursera put together a comprehensive report on their experiences. In it, they specifically address the drop-out issue. I highly recommend it: http://dukespace.lib.duke.edu/dspace/bitstream/handle/10161/...