Day 266 of #300daysofcode👨💻#DataScience#DataAnalytics
Back on GCP, completed 2 Qwiklabs
Got a better understanding of VPN setup and the use of Cloud NAT
Complete the 30DaysOfDS Microsoft Learn, and plan to start the webinar tomorrow
Started Azure AI fundamentals
Day 265 of #300daysofcode👨💻#DataScience#DataAnalytics
Back on GCP, completed 4 Qwiklabs
Worked with Terraform and loved the flexibility when using VMs
On 30dayofDS by @TheOyinbooke, I got to understand virtual environment better
and the use of pip freeze and requiment.txt file
Day 264 of #300daysofcode👨💻#DataScience#DataAnalytics
I spent the majority of my evening (4hrs) on GCP. Worked on 3 Quiklab projects.
The project name and lesson learned are in the pic
I got a better understanding of SL shown in the next pic
Finally, had a long nap😪
Day 263 of #300daysofcode👨💻#DataScience#DataAnalytics
Started my GCP project phase
Deployed a LAMP stack
Learned how to carry out batch inferencing on Azure ML when working with large volume of data
And hyperparameters turning in Azure ML
Day 262 of #300daysofcode👨💻#DataScience#DataAnalytics
Finally installed my binomial package on a virtual environment and saved it to PyPI.
Learned how to create, publish and schedule Azure Machine learning Pipeline.
Learned Firestore
Learning GCP monitoring resources
Day 261 of #300daysofcode👨💻#DataScience#DataAnalytics
Completed working with compute in azure matching learning module on Microsoft Learn
Learned how to:
- Create environment
- Compute
Using ML studio and SDK
Revising network on GCP
Fixed all errors on my Binomial package
Day 260 of #300daysofcode👨💻#DataScience#DataAnalytics
Learned how to train, save & register ML models on Azure using scripts
Built all methods on my binomial class.
Setup next
Learning Load balancing on GCP
It's interesting, making me wish I read computer for first degree🥺
Day 259 of #300daysofcode👨💻#DataScience#DataAnalytics
Finally integrated VAE in my CNN classier🎉.
Got lower accuracy.
Research on how to improve the VAE or alternate generative AI.
Built and deploy a Gaussian distribution package.
Pass AWS ML Udacity Certification.🎉
Day 258 of #300daysofcode👨💻#DataScience#DataAnalytics
Happy Sunday!
Working on my kidney stone project. Finding it difficult to combine my encode and prediction models because I built them with different libraries {Keras and TensorFlow}
All can say is omor
Somebody help me🥲
Day 257 of #300daysofcode👨💻#DataScience#DataAnalytics
Build class for Gaussian distribution.
Implemented magic methods like add and reps
On GCP, learning interconnection to an on-premises database.
Day 256 of #300daysofcode👨💻#DataScience#DataAnalytics
Continued improving my code
Build some classes for items sold in a shop and track salesperson performance to practice OOP
Currently building a bot to scrap job posting for a career recommendation AI.
Day 255 of #300daysofcode👨💻#DataScience#DataAnalytics
Learn testing, logging, and code review practice.
For code testing, I observed that the prefix 'test' used for naming functions to be tested must be followed by an underscore _, not a hyphen- for #pytest to detect them.
Day 254 of #300daysofcode👨💻#DataScience#DataAnalytics
Tried my first deep racer model in the competition and came out 2757 out of 3486😅 about 4 mins slower than the first position
Learning software engineering practice in DS to improve my coding. Current seeing the use of DSA
Day 253 of #300daysofcode👨💻#DataScience#DataAnalytics
Finally, back to AWS🎉
Training a deepRacer to learn how to implement reinforcement learning.
Learning how to interpret different reward graphs and identify poor models or overfitting.
Currently learning Generative AI