this post was submitted on 25 Nov 2023
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Machine Learning
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Keras broke the ice for me. The design of NNs used to take me a while to understand. It felt mechanic and meaningless. I was struggling hard to understand why adding or subtracting layers would help or hurt my models. I was trudging through tf documentation and honestly… I was very close to giving up.
I built my first ANN, got better with keras, graduated to tf, built my first U-net and got more confidence. I think anyone that really criticizes keras doesn’t understand that it is like criticizing training wheels for a bike.
You gotta learn to walk before you can run. You gotta learn baby nets before you are building monster segmentation models on recurrent convolutional neural nets. It takes time to understand the concepts and data flow.
Yes, same story. Keras allowed me to understand the basics. Personally, my journey has been as Keras for architecture, Pytorch/TensorFlow for implicit gradient differentiation, JAX for explicit gradient optimization, and then creating a library on JAX to understand how these libraries work.