Radhe Radhe, Hare Krishna π
Day 2: goals achieved β
π The day started with a 6.5 km run in the morning.
π In ML, I learned feature scaling through five different techniques: standardization, min-max scaling, robust scaler, max-abs scaler, and L1/L2 normalization. It was interesting to see how each one behaves differently, especially when the data has outliers. On that note, I also learned outlier detection using Isolation Forest.
πͺ In the evening, I kept it light with an easy leg workout.
That's it for today. What have you all been working on? Let me know below π
See you tomorrow for Day 3.
Radhe Radhe, Hare Hare Krishna Guys π
Day 2 of Being Consistent Forever
π Started the day with a 6.5 km run, and that's already done.
π Next, I'll be learning feature scaling and outlier detection in ML. These are two steps that look small but quietly decide how well a model actually performs.
π§ After that, I'll be finishing the last few parts of the RL lectures from the NPTEL course. Still tough, but getting there.
πͺ In the evening, I'll be hitting legs and adding some eccentric training to finish the day strong.
Would love to connect with people in tech. What are you currently focusing on? Drop it below π
Will update tonight.
Radhe Radhe, Hare Hare Krishna Guys π
Day 2 of Being Consistent Forever
π Started the day with a 6.5 km run, and that's already done.
π Next, I'll be learning feature scaling and outlier detection in ML. These are two steps that look small but quietly decide how well a model actually performs.
π§ After that, I'll be finishing the last few parts of the RL lectures from the NPTEL course. Still tough, but getting there.
πͺ In the evening, I'll be hitting legs and adding some eccentric training to finish the day strong.
Would love to connect with people in tech. What are you currently focusing on? Drop it below π
Will update tonight.
Radhe Radhe, Hare Hare Krishna Guys π
Day 1: goals achieved β
π 6.5 km morning jog
π ML: Learned how to handle missing values. The two main approaches are removing the data or imputing it, for both univariate and multivariate cases
π§ RL: Partially watched the NPTEL lectures
πͺ 100 weighted pull-ups + 100 weighted dips
RL is still going over my head. If you know any simple, beginner-friendly RL resources, please drop them below π
Would love to connect with people in Tech, and anyone into sports and fitness too.
π Strava: https://t.co/GpAAMvZd81
ποΈ Hevy: https://t.co/9MsW9irwZF
Day 2 tomorrow. See you π
Radhe Radhe, Hare Krishna π
Day 1 of Being Consistent Forever
I'm Yash. After a lot of overthinking, I've finally decided to share my progress publicly, every single day.
Starting with a 45-day consistency challenge, and the plan is to keep it going forever.
Today's targets:
β 6.5 km jog at 6 AM (done)
π ML: Learning how to handle missing values in a dataset (following CampusX's ML playlist)
π§ RL: Covering 5 lectures of Prof. Balaraman Ravindran's NPTEL Reinforcement Learning course. It's tough and I'm barely getting it right now, but I'm showing up anyway
πͺ Evening: 100 weighted pull-ups + 100 dips
Will update tonight on how it went.
Take care, see you tomorrow.
Radhe Radhe, Hare Krishna π
Day 1 of Being Consistent Forever
I'm Yash. After a lot of overthinking, I've finally decided to share my progress publicly, every single day.
Starting with a 45-day consistency challenge, and the plan is to keep it going forever.
Today's targets:
β 6.5 km jog at 6 AM (done)
π ML: Learning how to handle missing values in a dataset (following CampusX's ML playlist)
π§ RL: Covering 5 lectures of Prof. Balaraman Ravindran's NPTEL Reinforcement Learning course. It's tough and I'm barely getting it right now, but I'm showing up anyway
πͺ Evening: 100 weighted pull-ups + 100 dips
Will update tonight on how it went.
Take care, see you tomorrow.
@bhupesh_ku36215 Hey, Bhupesh I think your resume and experience are really appreciable.
Can you please tell me how you cover these many technologies and build projects on it. Do you cover each one fully and then start building, also what resources you refer to.