Embracing the Principle of Resistance in Deep Learning Optimization
Thought
A contemplation on the relationship between resistance, challenges in optimization landscapes, and personal growth in the context of deep learning.
Note
Embrace the peaks in optimization landscapes as symbols of personal resistance to conquer.
Analysis
The optimization landscape in deep learning is often fraught with peaks and valleys, corresponding to local minima and maxima that algorithms must navigate to reach an optimal solution. These peaks can be thought of as representing resistance, akin to the personal resistance one faces when aiming to attain a goal or learn something new. Engaging with this resistance, rather than seeking the path of least resistance (e.g., plateauing at a local minimum), can lead to more robust and innovative solutions.
Thinking in terms of Arthur Koestler's Bisociation, the synthesis of resisting both in the context of deep learning and personal growth can yield a creative transformation. In machine learning, techniques like simulated annealing reflect this by temporarily allowing for a worse solution which may lead to discovery of a better one long term.
In terms of personal growth, embracing challenges is echoed in the maxim 'what doesn't kill you makes you stronger'. It's about not shying away from difficulties as these are the moments where the most growth and learning happen.
Books
- “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- “The Art of Learning: An Inner Journey to Optimal Performance” by Josh Waitzkin
Papers
- “Understanding deep learning requires rethinking generalization” by Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals
- “Escaping from Saddle Points — Online Stochastic Gradient Descent for Tensor Decomposition” by Rong Ge, Furong Huang, Chi Jin, Yang Yuan
Tools
- TensorFlow and PyTorch - frameworks to implement and test deep learning models
- TensorBoard and Weights & Biases - tools to visualize and monitor the training process