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1/ Chose a career as a Data Scientist, which benefits from intense focus. 2/ All the thoughts and tasks that i get in my head, if tasks are small, i try to do some of them immediately. Otherwise, i write it down, and keep a sticky which i close at the end of the day. 3/ Am 40, I have a partner, who has slowly realized that i am different, and since the past few years she coaches me to do tasks i dont like (submit expense reports). I happily give her pocket shopping money in exchange. 4/ I take notes at every meeting, Notion is my tool. 5/ Switching off, what's that? I realize my teams think that i am a maniac, and my wife thinks am too obsessed. I help my team do things, and tell them i can come across as too hot, and they need to let me to know to backoff. I like to watch movies, and do take a break from time to time.


I have been using a consistent setup that hasn't yet failed me for the past 2 years.

1. Install Anaconda to your home user directory .

2. create environment using (conda create --name myenv python=3.6) .

3. Switch to the environment using (conda activate myenv) .

4. Use (conda install mypackage), (pip install mypackage) in that priority order .

5. Export environment using (conda env export > conda_env.yaml) .

6. Environment can be created on an other system using (conda env create -f conda_env.yaml) .

Anaconda: https://www.anaconda.com/distribution/#download-section .

Dockerized Anaconda: https://docs.anaconda.com/anaconda/user-guide/tasks/docker/ .


I can vouch for this. Anaconda is especially good for simulation/data stuff (based on the focus on which packages are included by default).

One pain point though: getting it to work with Sublime Text 3 requires you to set the `CONDA_DLL_SEARCH_MODIFICATION_ENABLE` environment variable to `1` on Windows.

Not a flaw of Anaconda: it just pays attention to how to with multiple Python installations on Windows.


* Complete API review and lots of examples for Scikit-Learn

* Machine Learning Algorithms from scratch (Random Forest, Gradient Boosted Trees, OLS, multi-layer perceptron). Thanks Jason Brownlee

* Spark API and Spark ML Lib API familiarity

* Still plugging away through Stanford CS229 Aiming towards a job change in mid 2019. Hopefully it works out.


I wish there were concrete examples of how machine learning is being used.


Pretty broad but, from one section in the article, sounds like they're using convolutional layers in their networks?


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