Pick one vertical, and gain breadth not the other way around, learning breadth and then specialising vertically in one field, it would always seem time pass. Hence for these next 3 months I have decided to pick "AI/Ml" as the only major vertical from scratch >agents>deployment.

Day 10

Day 8 & 9 in the pursuit of being "cracked" Continued ml specialisation...

Paired with github.com/ai-hpc/ai-hardwar… It can take you places.....

Day 7 in the pursuit of being "cracked" Finally after rest from kedarnath yatra Continued ml specialization Continued git and GitHub

I am learning these in my pursuit of being cracked , Lets see looking to get small internship by next summer and major one in next to next currently in sem 3.

True

In 1980, there were no GPUs, no ImageNet, and no billion-parameter models. GPT's were still science fiction. Yet a researcher named Kunihiko Fukushima was already working on ideas that would eventually become part of the foundation of modern computer vision. What fascinates me is that most people have never heard of him. Today, names like Ilya Sutskever, Geoffrey Hinton, Yann LeCun, and Andrej Karpathy are often associated with major advances in AI. But decades before deep learning became mainstream, Fukushima was asking a deceptively simple question: How does the brain recognize the same object when it appears in different positions? Back then, computers struggled with tasks that seem trivial today. If you showed a machine the same handwritten digit in a slightly different location, it could easily fail to recognize it. Humans do not have this problem. We can identify a face, a cat, or a handwritten number regardless of where it appears in our field of view. Fukushima became interested in how the brain solves this problem. Instead of asking, "How can I make computers smarter?", he asked a more fundamental question: "How does biological vision work in the first place?" That question led him to develop the Neocognitron, a neural network architecture inspired by the visual cortex. What made it remarkable was not just that it was a neural network, but how it was structured. Fukushima proposed a hierarchy of layers where lower layers learned simple visual features such as edges, corners, and line orientations. Higher layers combined these simple features into increasingly complex patterns and objects. The architecture consisted of what he called S-cells and C-cells. S-cells responded to specific local features in an image, while C-cells pooled information from nearby S-cells to make recognition more tolerant to small shifts in position. This was a crucial idea. A cat remains a cat whether it appears in the center of an image or slightly to the left. Humans naturally understand this, but computers struggled with it. Fukushima's architecture introduced a mechanism for achieving this kind of position invariance. When I read the original work, what struck me was how many concepts felt familiar. Local receptive fields. Hierarchical feature extraction. Alternating feature-detection and pooling stages. Translation-invariant pattern recognition. Multi-layer representation learning. Today, these ideas are considered fundamental to Convolutional Neural Networks (CNNs). In fact, long before the term "deep learning" became popular, Fukushima had already demonstrated that a network could learn increasingly abstract representations by stacking multiple layers on top of one another. The deeper I looked into it, the more it felt like reading an early blueprint for modern computer vision. The most fascinating part is that Fukushima achieved this in 1980. This was before modern GPUs. Before ImageNet. Before large-scale datasets. Before the computing infrastructure that would later make deep learning practical. The core ideas were already there. The world simply had not caught up yet. Then, more than three decades later, came one of the most important moments in AI history. In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton introduced AlexNet and achieved a breakthrough that stunned the computer vision community. Their model dramatically outperformed competing approaches in the ImageNet competition and showed the world what deep neural networks could achieve when combined with large datasets and GPU acceleration. That breakthrough did not appear out of nowhere. Many of the principles behind modern convolutional neural networks can be traced back to foundations that researchers like Fukushima had laid decades earlier. AlexNet became the spark that ignited the deep learning revolution, but pioneers like Fukushima had already spent years building the groundwork that made such a breakthrough possible. From there, progress accelerated rapidly. Computer vision became one of the most successful applications of deep learning. The same family of ideas would go on to power medical imaging systems, autonomous vehicles, robotics, scientific research, and many of the AI systems that shape our world today. Reading about Fukushima reminded me of something that applies far beyond AI. We often celebrate the people who turn an idea into a revolution. Far less attention goes to the people who first saw the possibility. Sometimes being early looks exactly the same as being wrong. The difference only becomes obvious years later when everyone else finally catches up. Kunihiko Fukushima was one of those rare researchers who saw a piece of the future long before the future arrived.

Charcter is when nobody's watching. Similar is my pursuit of being cracked which is not merely a milestone for me, but my overall goal in life to be a good person, that includes all aspects of life, which is an unending pursuit .

75 followers reached. Thank you everyone, on vacation soon will post...

True !!!!

Day 6 in the pursuit of being "cracked" Not much except Coursera ml specialisation. Now off to 7 days vacation let's see how much I can progress, finally laptop will see 7 days of complete rest too 😜.

Day 4 & 5 in the pursuit of being cracked _ did git _ python dsa started well completed basics of tc sc lists _ml specialisation ongoing

65 billion with a BBBB , series HHH , understand this absurdity : More than 110/195 countries GDP 65,000 million USD 6,50,000 hundred thousands usd, stack it to reach beyond space Earning 100k usd/yr then 6,50,000 years Million USD per day then 178 years. 1usd = 100 inr

I am looking to buy a new laptop, Asus Zephyrus G16 with following specs: 32gb non upgradeble ram 12gb vram 5070ti 1tb SSD Any suggestions advices or recommendations ? My current one works fine but has no vram, only 8gb ram and heats up like crazy with 50 min. battery life.

To declutter and maximize your focus, narrow your immediate efforts to one primary goal and support it with exactly three rotating sub-topics at a time. This allows for deep learning and problem-solving without cognitive overload.

Noted. vladfeinberg.com/2026/05/10/…

sem 2 results declared : 9/10 CGPA : 9.00 Lets gooooooo

Relentless curiosity is great but what I have realised and it took a lot of metacognition is it can lead to dreadful procrastination if not paired with focused patch of work or time period given dedicatedly to it.

I love my mother a lot, thank you mom for everything .