Ayush
@TensorThrottleX
Crafting objective proof from vast and ambiguous datasets. Validating a chosen path to enable confident, decisive action. #100DaysOfML
Day 0 – The 100-Day Raw Grind Begins- Starting tomorrow, @TensorThrottleX , @BinaryBlaze16, & @_n1nj07 are going all in n out . DSA, development, and daily wins. No excuses. No shortcuts. Every line of code, every problem solved, every day counts.
Day 80: Log your grind. Share the Gains. Prove your sweat counts. #100DaysRawGrind
Day 195 : DataScience Journey VGG16 Run Concludes Strong : 99.12% Train Accuracy & 98.78% Validation Moving Forward with ResNet Arch : ResNet paper 2015 (He et al.) : Deep Residual Learning for Image Recognition;) Training concluded with exceptional results ; 99.12% training…
Day 79: Log your grind. Share the Gains. Prove your sweat counts. #100DaysRawGrind
Day 194 : DataScience Journey Decoupled MLOps Architecture Validation: VGG16 Trained from Scratch Achieves 98.78% Validation Accuracy The hybrid Controller-Compute-Warehouse architecture is now fully operational and has been rigorously validated through end-to-end execution of a…
Day 194 : DataScience Journey Decoupled MLOps Architecture Validation: VGG16 Trained from Scratch Achieves 98.78% Validation Accuracy The hybrid Controller-Compute-Warehouse architecture is now fully operational and has been rigorously validated through end-to-end execution of a…
100DaysRawGrind : Re-introducing : A structure for people tired of their own excuses. Not motivation. Not inspiration. Execution. Daily. Public. How it Works: The idea is simple. -Choose ONE skill or domain you will transform in 100 days -Post raw proof of work every day, no…
Day 78: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 193 : DataScience Journey The Controller -Compute -Warehouse Setup Is Now Fully Live (and it’s stupidly fast): -After last week’s bloodbath of dead runs and $2k+ in wasted GPU time, the new hybrid architecture is finally battle-hardened and running like clockwork. 1)…
Day 193 : DataScience Journey The Controller -Compute -Warehouse Setup Is Now Fully Live (and it’s stupidly fast): -After last week’s bloodbath of dead runs and $2k+ in wasted GPU time, the new hybrid architecture is finally battle-hardened and running like clockwork. 1)…
Day 77: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 192: DataScience Journey Remodifying Training Environment and Architecture: -Today was focused on continuing the automation reliably connect and use the remote GPU so runs don’t fail silently. The main fix addressed unstable remote startup and connection failures by 1.…
Day 192: DataScience Journey Remodifying Training Environment and Architecture: -Today was focused on continuing the automation reliably connect and use the remote GPU so runs don’t fail silently. The main fix addressed unstable remote startup and connection failures by 1.…
Day 76: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 191 : DataScience Journey Remodifying Training Environment Architecture: Today was focused on tightening the foundation of the entire training ecosystem from directory hygiene to error-handling pathways. The Training & Testing environment is no longer just a folder; it’s
Day 191 : DataScience Journey Remodifying Training Environment Architecture: Today was focused on tightening the foundation of the entire training ecosystem from directory hygiene to error-handling pathways. The Training & Testing environment is no longer just a folder; it’s
Day 75: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 190 : DataScience Journey Today’s Incremental Evolution: Why the need: ML workflows were scaling aggressively, but manual environment setups kept breaking pipelines every time dependencies changed or models switched. Today’s evolution fixes that by automating environment
Day 190 : DataScience Journey Today’s Incremental Evolution: Why the need: ML workflows were scaling aggressively, but manual environment setups kept breaking pipelines every time dependencies changed or models switched. Today’s evolution fixes that by automating environment
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