1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.
More from Machine learning
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
With hard work and determination, anyone can learn to code.
Here’s a list of my favorites resources if you’re learning to code in 2021.
👇
1. freeCodeCamp.
I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things
2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:
1) You can see the most upvoted solutions so you can read really good code
2) You can ask questions in the discussion section if you're stuck, and people often answer. Free
3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE
On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.
4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.
Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
Here’s a list of my favorites resources if you’re learning to code in 2021.
👇
1. freeCodeCamp.
I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things
2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:
1) You can see the most upvoted solutions so you can read really good code
2) You can ask questions in the discussion section if you're stuck, and people often answer. Free
3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE
On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.
4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.
Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.