Free Download Advanced Theoretical Neural Networks (Mastering Machine Learning) by Jamie Flux
English | September 19, 2024 | ISBN: N/A | ASIN: B0DHJ69Z6T | 195 pages | PDF | 3.92 Mb
A deep dive into the theory and mathematics behind neural networks, beyond typical AI applications.
Area of focus:
– Grasp complex statistical learning theories and their application in neural frameworks.
– Explore universal approximation theorems to understand network capabilities.
– Delve into the trade-offs between neural network depth and width.
– Analyze the optimization landscapes to enhance training performance.
– Study advanced gradient optimization methods for efficient training.
– Investigate generalization theories applicable to deep learning models.
– Examine regularization techniques with a strong theoretical foundation.
– Apply the Information Bottleneck principle for better learning insights.
– Understand the role of stochasticity and its impact on neural networks.
– Master Bayesian techniques for uncertainty quantification and posterior inference.
– Model neural networks using dynamical systems theory for stability analysis.
– Learn representation learning and the geometry of feature spaces for transfer learning.
– Explore theoretical insights into Convolutional Neural Networks (CNNs).
– Analyze Recurrent Neural Networks (RNNs) for sequence data and temporal predictions.
– Discover the theoretical underpinnings of attention mechanisms and transformers.
– Study generative models like VAEs and GANs for creating new data.
– Dive into energy-based models and Boltzmann machines for unsupervised learning.
– Understand neural tangent kernel frameworks and infinite width networks.
– Examine symmetries and invariances in neural network design.
– Explore optimization methodologies beyond traditional gradient descent.
– Enhance model robustness by learning about adversarial examples.
– Address challenges in continual learning and overcome catastrophic forgetting.
– Interpret sparse coding theories and design efficient, interpretable models.
– Link neural networks with differential equations for theoretical advancements.
– Analyze graph neural networks for relational learning on complex data structures.
– Grasp the principles of meta-learning for quick adaptation and hypothesis search.
– Delve into quantum neural networks for pushing the boundaries of computation.
– Investigate neuromorphic computing models such as spiking neural networks.
– Decode neural networks’ decisions through explainability and interpretability methods.
– Reflect on the ethical and philosophical implications of advanced AI technologies.
– Discuss the theoretical limitations and unresolved challenges of neural networks.
– Learn how topological data analysis informs neural network decision boundaries.