From Theory to Training: Building My First Neural Networks
The author shares their journey of learning machine learning, moving from shallow algorithms to building and training neural networks for handwritten digit recognition and image denoising.
Why it matters
This article provides a valuable firsthand account of the practical challenges and learnings involved in transitioning from theoretical ML concepts to building and training neural networks.
Key Points
- 1Shifted from learning what models predict to how they learn
- 2Implemented a neural network for handwritten digit recognition with 97% accuracy
- 3Experimented with different layer architectures like Dropout, Convolution, and BatchNorm
- 4Explored autoencoders for image denoising, finding the right bottleneck size is key
- 5Faced challenges with hyperparameter tuning and debugging, learned the importance of optimizer choice
Details
The article covers the author's progress in their machine learning journey, moving from shallow algorithms like linear and logistic regression to building and training neural networks. They implemented a neural network for handwritten digit recognition using the MNIST dataset, achieving 97% accuracy. The author also experimented with different layer architectures, including Dropout layers for fault tolerance, Convolutional layers for respecting spatial structure, and BatchNorm for stabilizing training. For image denoising, they explored autoencoders, finding that the size of the bottleneck layer is crucial - too large leads to memorizing noise, while too small loses important details. The author also faced challenges with hyperparameter tuning and debugging, learning the importance of starting with known-good defaults and changing one thing at a time, as well as the impact of the optimizer choice (SGD vs. Adam).
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