When I first learned about deep learning, the concept of epochs fascinated me. In machine learning, an epoch is one complete pass through the training data; after each epoch a model's weights are updated using backpropagation, and over time it improves.
One day I asked myself: what if life works the same way? What if each lifetime is like an epoch — not training a neural network, but training a soul?
Drawing the Parallel
In deep learning terms:
Model → the system being trained (our soul)
Weights → stored knowledge and habits
Dataset → life experiences, struggles, joys
Epoch → one complete lifetime
Loss function→ distance from our ideal self
Backprop → reflecting on mistakes and adjusting behavior
If reincarnation is real, then maybe each life is a training round. We carry lessons, instincts, and tendencies from earlier lives — just like a model carries learned weights between epochs.
The Mystery of Innate Talents
Some children are born with extraordinary abilities: a natural musician, a mechanical tinkerer, a mathematical prodigy. While genetics and environment explain much, sometimes talent appears without an obvious source.
My Story
My father is a farmer. My mother is a housewife. In my entire family, there wasn’t a single person who thought about engineering or robotics when they were children. Yet I was building things long before I understood the words “engineering” or “design.”
As a child I:
- Built toy cars from scrap
- Designed motorized boats and tried to make them float
- Attempted a drone (failed — motor too heavy)
- Tried a homemade submarine (it sank, but I learned)
- Sketched robot schematics like Iron Man — dreaming of a future build
None of this came from family instruction. It felt like I had pre-trained weights — instincts and patterns carried forward into this life.
Failure as Feedback
In deep learning, a wrong prediction gives feedback. The model updates, tries again, and improves. Life supplies feedback the same way: mistakes, pain, and failure are data. If we reflect and respond, our inner parameters shift.
When my drone wouldn’t lift or my submarine sank, I wasn’t just failing — I was collecting training data for my future projects.
Genetics vs. Soul Memory
Think of genetics as hardware. The soul’s tendencies would be like pre-trained software. Some people are naturally primed for certain paths; others must train from scratch. Both are valid — the difference is in efficiency.
The Bigger Picture
Across cultures, this idea has echoes: karma and reincarnation in Hinduism, samsara in Buddhism, the mystics’ talk of repeated lives for refinement. Whether you interpret this literally or metaphorically, the concept changes how we view failure, talent, and purpose.
Why This View Matters
Framing life as an epoch alters your relationship with learning and loss. It turns setbacks into curriculum. It makes talent a resource to be used, not a label to protect. It invites patience — a recognition that growth often needs many passes through life’s data.
Conclusion
Deep learning has taught me one practical thing: powerful systems aren’t born in a single pass. They train. They fail. They iterate. Maybe souls do the same. Maybe each lifetime is one epoch in a very long training loop.
So when you feel behind, remember: you’re not in a race. You are training.