Life is a journey where each of us seeks our global minimum—our most balanced and fulfilling state—in the middle of a fog of uncertainty.”
Every step, every experience is the sum of small steps taken towards the global minimum of life – our most fulfilling, balanced state. Even though you can’t see exactly where you are going or what the future will bring you in the fog of uncertainty, you are just trying to find your own path. By observing the world and taking steps in the direction that feels most like downward, you are eventually trying to find the global minimum of your life. Even when the future is unclear and the path seems hidden, we keep moving forward, learning along the way.
In technical terms, our journey resembles an algorithm called gradient descent —a process that seeks the lowest point on a cost function, which measures the gap between our current state and our ideal self.In life, every experience acts as a data point, steadily reducing the difference between who we are and who we wish to become.
So how did this story begin?
You waited for 9 months in a dark window to step into this world and take your first breath on Earth. You were fed, you lived, you grew.
Then, the first ray of light illuminated that dark environment and here it was, “Your first breath on Earth!” Congratulations,now you are a human child.From that moment on, every experience started to shape your identity.
At the very start of your life, your family and the culture you were born into provided you with some “labeled data”—simple guidelines about what is right and wrong. These included messages such as:
You must be polite!
Don’t lie !
Dress properly!
You are a Christian.
You are a Muslim.
You are a Jewish.
This should be your profession!
These early instructions formed the foundation of your behavior, defining what is acceptable and what is not.
These early lessons start to form the connections in our brain, like a network of simple rules. Over time, these connections shape our thoughts, actions, and feelings.
Much like the training data in a neural network, these guidelines began wiring your brain into a network of simple rules. Over time, as new experiences add more data points, your internal model is continuously adjusted—much like updating weights and biases—through the gradient descent process. This ongoing adjustment measures the difference between your predictions and your reality, guiding you ever closer to your personal state of fulfillment.
This labeled data will form the basis of the big and small decisions you will make in the future—both big and small decisions. Sometimes, the guidelines you receive from family, culture, or society may not perfectly match who you truly are. In these cases, you update them with your own experiences.
Even if you sometimes get angry at these labels, these are actually prejudices that will lead you to the global minimum, the most fulfilling life. Remember that prejudices are not always negative; they can also be elements that help you survive and move forward.
Think of each New Year’s resolution as a “batch” update. Just as a machine learning model processes a batch of data to refine its predictions, you collect all the lessons from the past year and, after careful evaluation, update your “New Year model.”
Ask yourself:
-Who do you want to be?
-What do you want to do?
-What have you learned from last year?
While processing an entire year’s worth of experiences requires handling a large dataset—demanding time and memory—it ultimately refines your personal model, preparing you for a new cycle of growth.
There's a lot to think about, right?
So it's easy to evaluate the year in terms of calculation, but it needs a long processing time. So we've got large training datasets and it needs to store all of that data in memory and process it.
Each experience makes updates in your brain one by one. Every moment you laugh, cry, get scared; It is like the simplest stochastic gradient descent steps of life. With these small, sometimes even random steps, our identity is slowly built. Stochastic gradient descent, on the other hand, is easy to store in memory and get individual responses much faster according to batch. You evaluate after each event, that is, with one training example, just like we think about an event every hour and every day.
School years are like a period of mini-batch updates.
Every class, every friendship, and every challenge contributes a mini-batch of data that refines our internal model. Exams, lessons, and social interactions provide immediate feedback on our mistakes and successes, just as mini-batch updates in machine learning adjust a model using several data points at once. In other words, different areas of our lives are divided into small batch sizes and perform updates on each of those batches. Since we divide our lives in this way, our calculations become faster, but since we are dealing with many areas, this multitasking state is not easy for us in terms of calculation.
As we move into adolescence, sometimes it is not clear which way we should go; we experience saddle points where the slope is almost zero, where neither full progress nor step back is felt. These moments of uncertainty allow us to find the necessary balance for more solid steps to be taken in the future.
Sometimes we take small steps in life, such as going to work or school every day. Sometimes we take big steps with big decisions, such as starting university or starting a new life in another country.The common element in all these decisions is what we call the learning rate. Every class you attend, every book you read, and every person you meet can adjust this rate, determining the size of the steps you take.
Imagine that every decision you make in life is like a step in an algorithm. At any point, you can look at your current situation—like checking the slope of a function—to decide which way to go.
If you feel that things are worsening (a positive slope), it signals that you should change direction—take a "step left" to lower your cost. Conversely, if you sense that things are improving in a different direction (a negative slope), you’d "step right" to further decrease your cost. Each decision, whether a small tweak or a big change, reduces your overall 'cost' (or unhappiness), much like gradient descent minimizes error.
By repeatedly checking the slope and adjusting your direction, you gradually move toward a state of balance—a local minimum. However, just as starting from different points in an algorithm can lead you into different valleys, the path you take might only bring you to a good, stable state rather than the absolute best outcome. The key is learning from every step you take and fine-tuning your path toward true fulfillment.
As we grow older, we often make big decisions that seem like leaps forward—choosing a new job, relocating to a different city, or even changing our appearance. These choices represent large steps in our personal learning process. We might feel that we’ve reached perfection—finding the love of our life or landing our dream job—but sometimes these milestones are only temporary stops, not our final destination. They signal that further adjustments are needed as we continue our journey.
There comes a moment when you realize you haven’t reached your global minimum, but instead find yourself stuck in what we call a local minimum. Think of a local minimum as a small, comfortable valley in the landscape of your life—a state that seems stable and satisfactory, yet isn’t the deepest, most fulfilling point possible. It’s like settling into a routine or situation that feels “good enough,” even though a richer, more rewarding path might exist just beyond your current view. In our gradient descent journey, recognizing that we’re in a local minimum reminds us that even when progress seems to stall, a few well-measured adjustments can help us break free and continue our search for true fulfillment.
Every event we experience—the falls we recover from, the mistakes we make, even the F grades we receive—determines our step size. In gradient descent terms, this step size represents the amount of knowledge we gain from each experience and how much we adjust our path based on the feedback we receive.
If this step size is too small, you will slowly learn from the experiences and have difficulty making changes. Although small and cautious steps protect you from overshooting, sometimes they cause you to miss opportunities due to fear and slow down your development. These steps are so small that it is not possible for you to pass the global minimum.
Sometimes, you decide on big steps and big changes. Sometimes this is quitting a job you don't like, sometimes big steps like moving to a country you don't even speak the language.
In other words, very fast and big changes.
Decisions beyond the optimum.
Such bold decisions can accelerate our progress dramatically, but if taken too rapidly, they might cause us to overshoot our optimum or delay our convergence to our ideal state."
Each person seeks their own global minimum—a unique state of true happiness.
For some, that might mean having a golden retriever and a loving family with five kids.
For others, it could be the excitement of working as a software developer in Silicon Valley.
Some spend their lives learning and studying, finding meaning in the hard work of science and research.
And some dream of life on the open road, traveling across continents and exploring new places.
Every day, every small experience is like a new mini-batch of lessons. Each moment gives us a chance to learn, change, and adjust our path. With every experience, we grow and improve, and our journey is always evolving. We write our story step by step, and we can always change our plans as we learn more.
In the end, reaching your global minimum isn’t about following a fixed rule—it’s about finding your own best self. Each person has a story full of endless possibilities, built on the many lessons learned along the way. Whether through quiet reflection or bold moves into the unknown, our lives are a journey of growth, guided by our choices and dreams.