Lately, I've been diving deep into some fascinating areas of AI and computing, and I wanted to share what I’ve been learning! From neural networks to GPU computing, it's been an exciting journey, and I feel like I’m only scratching the surface.
One of the key topics I explored was DeepSeek, an AI research project focused on deep learning. Neural networks have always intrigued me, but learning more about reinforcement learning, matrix multiplication, and vectors has given me a clearer picture of how they actually function. Understanding these mathematical foundations has helped me appreciate how models learn and optimize their performance over time.
Reinforcement Learning: Teaching AI to Make Decisions
Reinforcement learning (RL) is another concept that caught my attention. Unlike traditional supervised learning, where models learn from labeled data, RL involves training an agent to make decisions by interacting with an environment. The AI receives rewards or penalties based on its actions, gradually improving its decision-making abilities. This is the kind of technique used in game-playing AIs and robotics.
The World of GPU Computing and CUDA Programming
Another huge revelation for me was the importance of GPU computing in AI. Unlike traditional CPUs, GPUs handle computations in parallel, making them ideal for deep learning tasks. I also learned about CUDA, a parallel computing platform by NVIDIA that allows developers to write programs that run efficiently on GPUs. Writing in CUDA opens up new possibilities for optimizing AI workloads and accelerating complex computations.
Shoutout to Computerphile!
A big thanks to the YouTube channel Computerphile for making these advanced topics easier to understand! Their videos break down complex concepts in a way that’s accessible and engaging. If you’re curious about AI, computing, or just tech in general, I highly recommend checking them out.
I’m excited to continue this learning journey and explore more about deep learning, AI optimization, and parallel computing. If you have any recommendations or resources, feel free to share!