I can share A LOT of code with it. Ex: Everytime i start a new project, i just need to import an Auth lib, that i created, and all Auth related stuff is set up.
Starting a new project using #Angular? Here is a list of all the stuff i use to launch my projects the fastest i can.
A THREAD 👇
I can share A LOT of code with it. Ex: Everytime i start a new project, i just need to import an Auth lib, that i created, and all Auth related stuff is set up.
There's a lot of boilerplate, but you can solve this with the built-in schematics and/or with your own schematics
https://t.co/iLrIaHVafm
https://t.co/3382Tn2k7C
You can automate all the boilerplate with hundreds of files associates with creating a new feature.
You can find a lot of solutions for i18n (internacionalization) for Angular, but the best one is the "native". All you need to do is mark all traslatable text with the "i18n" attribute and translate the .xlf files created by the Angular CLI.
I recommend ngx-dy-i18n for translations during execution and xliffmerge (ngx-i18nsupport) to control the translations files. You can use NGINX to redirect the user to it's language version.
https://t.co/g6Omudp7RZ
https://t.co/JXpoGlRh6l
https://t.co/4u9tXL8A7w
https://t.co/0NUJOED4xX
https://t.co/jEB10XXWJL
Sustainable Angular Architectures. A three parts article by @ManfredSteyer
https://t.co/Pr9aH42rua
More from Machine learning
Happy 2⃣0⃣2⃣1⃣ to all.🎇
For any Learning machines out there, here are a list of my fav online investing resources. Feel free to add yours.
Let's dive in.
⬇️⬇️⬇️
Investing Services
✔️ @themotleyfool - @TMFStockAdvisor & @TMFRuleBreakers services
✔️ @7investing
✔️ @investing_city
https://t.co/9aUK1Tclw4
✔️ @MorningstarInc Premium
✔️ @SeekingAlpha Marketplaces (Check your area of interest, Free trials, Quality, track record...)
General Finance/Investing
✔️ @morganhousel
https://t.co/f1joTRaG55
✔️ @dollarsanddata
https://t.co/Mj1owkzRc8
✔️ @awealthofcs
https://t.co/y81KHfh8cn
✔️ @iancassel
https://t.co/KEMTBHa8Qk
✔️ @InvestorAmnesia
https://t.co/zFL3H2dk6s
✔️
Tech focused
✔️ @stratechery
https://t.co/VsNwRStY9C
✔️ @bgurley
https://t.co/NKXGtaB6HQ
✔️ @CBinsights
https://t.co/H77hNp2X5R
✔️ @benedictevans
https://t.co/nyOlasCY1o
✔️
Tech Deep dives
✔️ @StackInvesting
https://t.co/WQ1yBYzT2m
✔️ @hhhypergrowth
https://t.co/kcLKITRLz1
✔️ @Beth_Kindig
https://t.co/CjhLRdP7Rh
✔️ @SeifelCapital
https://t.co/CXXG5PY0xX
✔️ @borrowed_ideas
For any Learning machines out there, here are a list of my fav online investing resources. Feel free to add yours.
Let's dive in.
⬇️⬇️⬇️
Investing Services
✔️ @themotleyfool - @TMFStockAdvisor & @TMFRuleBreakers services
✔️ @7investing
✔️ @investing_city
https://t.co/9aUK1Tclw4
✔️ @MorningstarInc Premium
✔️ @SeekingAlpha Marketplaces (Check your area of interest, Free trials, Quality, track record...)
General Finance/Investing
✔️ @morganhousel
https://t.co/f1joTRaG55
✔️ @dollarsanddata
https://t.co/Mj1owkzRc8
✔️ @awealthofcs
https://t.co/y81KHfh8cn
✔️ @iancassel
https://t.co/KEMTBHa8Qk
✔️ @InvestorAmnesia
https://t.co/zFL3H2dk6s
✔️
Tech focused
✔️ @stratechery
https://t.co/VsNwRStY9C
✔️ @bgurley
https://t.co/NKXGtaB6HQ
✔️ @CBinsights
https://t.co/H77hNp2X5R
✔️ @benedictevans
https://t.co/nyOlasCY1o
✔️
Tech Deep dives
✔️ @StackInvesting
https://t.co/WQ1yBYzT2m
✔️ @hhhypergrowth
https://t.co/kcLKITRLz1
✔️ @Beth_Kindig
https://t.co/CjhLRdP7Rh
✔️ @SeifelCapital
https://t.co/CXXG5PY0xX
✔️ @borrowed_ideas
Really enjoyed digging into recent innovations in the football analytics industry.
>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇♂️ https://t.co/9YOSrl8TdN
For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.
This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method https://t.co/Hx8XTUMpJ5
Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.
Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN
So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?
>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇♂️ https://t.co/9YOSrl8TdN
For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.
This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method https://t.co/Hx8XTUMpJ5
Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.
Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN
So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?
10 PYTHON 🐍 libraries for machine learning.
Retweets are appreciated.
[ Thread ]
1. NumPy (Numerical Python)
- The most powerful feature of NumPy is the n-dimensional array.
- It contains basic linear algebra functions, Fourier transforms, and tools for integration with other low-level languages.
Ref: https://t.co/XY13ILXwSN
2. SciPy (Scientific Python)
- SciPy is built on NumPy.
- It is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization, and Sparse matrices.
Ref: https://t.co/ALTFqM2VUo
3. Matplotlib
- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
- You can also use Latex commands to add math to your plot.
- Matplotlib makes hard things possible.
Ref: https://t.co/zodOo2WzGx
4. Pandas
- Pandas is for structured data operations and manipulations.
- It is extensively used for data munging and preparation.
- Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage.
Ref: https://t.co/IFzikVHht4
Retweets are appreciated.
[ Thread ]
1. NumPy (Numerical Python)
- The most powerful feature of NumPy is the n-dimensional array.
- It contains basic linear algebra functions, Fourier transforms, and tools for integration with other low-level languages.
Ref: https://t.co/XY13ILXwSN
2. SciPy (Scientific Python)
- SciPy is built on NumPy.
- It is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization, and Sparse matrices.
Ref: https://t.co/ALTFqM2VUo
3. Matplotlib
- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
- You can also use Latex commands to add math to your plot.
- Matplotlib makes hard things possible.
Ref: https://t.co/zodOo2WzGx
4. Pandas
- Pandas is for structured data operations and manipulations.
- It is extensively used for data munging and preparation.
- Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage.
Ref: https://t.co/IFzikVHht4
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Stan Lee, who died Monday at 95, was born in Manhattan and graduated from DeWitt Clinton High School in the Bronx. His pulp-fiction heroes have come to define much of popular culture in the early 21st century.
Tying Marvel’s stable of pulp-fiction heroes to a real place — New York — served a counterbalance to the sometimes gravity-challenged action and the improbability of the stories. That was just what Stan Lee wanted. https://t.co/rDosqzpP8i
The New York universe hooked readers. And the artists drew what they were familiar with, which made the Marvel universe authentic-looking, down to the water towers atop many of the buildings. https://t.co/rDosqzpP8i
The Avengers Mansion was a Beaux-Arts palace. Fans know it as 890 Fifth Avenue. The Frick Collection, which now occupies the place, uses the address of the front door: 1 East 70th Street.