Deep Learning Understanding

Deep learning, a subset of machine learning, is behind many advancements in AI. Knowledge of neural networks, CNNs, RNNs, and reinforcement learning, as well as frameworks like TensorFlow and PyTorch, is essential for cutting-edge development.

Deep learning, a subset of machine learning, is behind many advancements in AI. Knowledge of neural networks, CNNs, RNNs, and reinforcement learning, as well as frameworks like TensorFlow and PyTorch, is essential for cutting-edge development.

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Rutika Bhoir
Grad Student at Umass Amherst

Deep learning can feel like a black box when you’re starting out. Neural networks, CNNs, RNNs, reinforcement learning—it’s a lot. But once you get into it, it’s also kind of magical. I took a Reinforcement Learning course last semester, and it absolutely stretched my brain. One of the things that stuck with me while learning Markov Decision Processes was this idea: “The future is independent of the past, given the present.” It sounds like a life mantra, honestly—and in RL, it actually is. (Also, existential crises mid-homework? Highly likely.) If you’re diving into deep learning, I’d say start with Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow—it makes the concepts practical and less terrifying. And don’t underestimate the power of lurking in the r/MachineLearning or r/DeepLearning subreddits. Some of the discussions there are chaotic, brilliant, and weirdly comforting. You realize everyone’s kind of lost sometimes. Also, real talk: understanding how to use frameworks like TensorFlow or PyTorch is important—but understanding why things work matters more in the long run. Play with models. Break them. Make a CNN that does nonsense. Build an RNN that writes poetry. Let yourself experiment. It’s okay to not “get it” all at once. Deep learning is deep. That’s the point. Dig anyway.

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