A Day in the Life of a Future Software Engineer: Learning, Challenges, and Growth

Hi,

Hope you’re doing well. My story is still missing and will be discussed in upcoming articles, but today’s priority is what I do today. Unfortunately, I lost two projects—not a huge loss, but I did lose some time, and you know how important time is. "Time is the key to success." Thanks to colleges that are working on that project but not completed due to some unexpected reasons. May be will have more work in future.

So, When I reached the university, I had a Sociology class. The topic was "How people show their identity." It was a good discussion, with many feedbacks from other colleges and group discussions on various things. The main focus was language as a marker of identity. For example, in Islamabad, the capital of Pakistan, we meet people from many traditions. We often recognize people through their clothes, but this isn’t always reliable since not everyone wears traditional clothes. However, language is a strong indicator. If someone speaks Punjabi or any other language, we can often determine their cultural background. It’s harder to distinguish between Pashtoon Pathans and Afghanis, though, as I’m not familiar with Pashto or Afghan languages, but I'm curious to learn.

After Sociology, I went outside on campus, but I don’t enjoy spending too much time just sitting around; it feels unproductive. Then, I headed to the library, but I wasn’t sure where my friends went. My next class was Mobile Application Development. This wasn’t the first class; we’ve already covered Android Studio basics. Today’s topic was Explicit and Implicit Intents. At first, I didn’t understand, so I asked the teacher to explain further. Here’s what I learned:

1. Explicit Intents: These specify the exact component (activity, service, or broadcast receiver) that should handle the intent by explicitly stating the class name of the component.
2. Implicit Intents: These don’t specify a particular component but declare an action to be performed, like opening a web page or sharing content, letting Android choose the appropriate app to handle it.

The teacher provided examples, gave us an assignment, and discussed our semester project requirements.

After class, I met with friends briefly before returning to the library. My friends jokingly call me "pagal" (crazy) because I spend most of my time in the library from the first to the sixth semester. They spend their time differently, which I respect. I try to convince them to learn programming and collaborate with me, but even after long conversations, they don’t seem to join in. One friend always delays collaborating on projects, saying they'll learn something else so we can combine skills, but it doesn’t work out. I realize I need to do things independently, so here I am, learning daily.

Later, I started working on my website—it's not complete yet, but I’ll show it to you once it is. I also continued learning about Machine Learning, specifically reinforcement learning and how models learn based on feedback. This concept has great potential for the future. I also reviewed supervised and unsupervised learning, and my research didn’t stop there. This week’s focus is Machine learning. So this topic is new for me to learn but I always add some thing new So here the overview.

What is Deep Learning?

Deep learning is a type of machine learning where computers learn and make decisions by imitating the human brain using layers of connected "neurons." These layers help the computer recognize patterns in complex data, like images, sounds, or text, without explicit programming for each task.

History of Deep Learning

The concept of "neurons" in deep learning was inspired by biological neurons in the human brain. In 1943, Warren McCulloch and Walter Pitts created a mathematical model of neurons to mimic brain function. The field of deep learning took shape in 1986 when Geoffrey Hinton, David Rumelhart, and Ronald Williams developed the backpropagation algorithm, allowing neural networks to learn more effectively. Hinton is often called one of the "fathers of deep learning" for his foundational work.

However, in the early stages, many scientists failed to build functional neural networks due to low computational power, limited data, and inefficient algorithms. Advances in technology, data collection, and social media now provide the resources to build and train deep learning models. So, I’m learning bit by bit and will continue exploring in depth tomorrow. I think I will work on some portfolio projects tomorrow, but it's the hardest part of life for me to build a portfolio. Well, no worries—I will, because I consider myself a software engineer, or rather, a future software engineer, you could say.

Have a good day!