{"product_id":"advanced-theoretical-neural-networks-9798339808039","title":"Advanced Theoretical Neural Networks","description":"\u003cp\u003e • Author(s): Jamie Flux\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Information Theory\u003c\/p\u003e\u003cp\u003eA deep dive into the theory and mathematics behind neural networks, beyond typical AI applications. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cb\u003eArea of focus: \u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e- Grasp complex statistical learning theories and their application in neural frameworks.\u003cbr\u003e- Explore universal approximation theorems to understand network capabilities.\u003cbr\u003e- Delve into the trade-offs between neural network depth and width.\u003cbr\u003e- Analyze the optimization landscapes to enhance training performance.\u003cbr\u003e- Study advanced gradient optimization methods for efficient training.\u003cbr\u003e- Investigate generalization theories applicable to deep learning models.\u003cbr\u003e- Examine regularization techniques with a strong theoretical foundation.\u003cbr\u003e- Apply the Information Bottleneck principle for better learning insights.\u003cbr\u003e- Understand the role of stochasticity and its impact on neural networks.\u003cbr\u003e- Master Bayesian techniques for uncertainty quantification and posterior inference.\u003cbr\u003e- Model neural networks using dynamical systems theory for stability analysis.\u003cbr\u003e- Learn representation learning and the geometry of feature spaces for transfer learning.\u003cbr\u003e- Explore theoretical insights into Convolutional Neural Networks (CNNs).\u003cbr\u003e- Analyze Recurrent Neural Networks (RNNs) for sequence data and temporal predictions.\u003cbr\u003e- Discover the theoretical underpinnings of attention mechanisms and transformers.\u003cbr\u003e- Study generative models like VAEs and GANs for creating new data.\u003cbr\u003e- Dive into energy-based models and Boltzmann machines for unsupervised learning.\u003cbr\u003e- Understand neural tangent kernel frameworks and infinite width networks.\u003cbr\u003e- Examine symmetries and invariances in neural network design.\u003cbr\u003e- Explore optimization methodologies beyond traditional gradient descent.\u003cbr\u003e- Enhance model robustness by learning about adversarial examples.\u003cbr\u003e- Address challenges in continual learning and overcome catastrophic forgetting.\u003cbr\u003e- Interpret sparse coding theories and design efficient, interpretable models.\u003cbr\u003e- Link neural networks with differential equations for theoretical advancements.\u003cbr\u003e- Analyze graph neural networks for relational learning on complex data structures.\u003cbr\u003e- Grasp the principles of meta-learning for quick adaptation and hypothesis search.\u003cbr\u003e- Delve into quantum neural networks for pushing the boundaries of computation.\u003cbr\u003e- Investigate neuromorphic computing models such as spiking neural networks.\u003cbr\u003e- Decode neural networks' decisions through explainability and interpretability methods.\u003cbr\u003e- Reflect on the ethical and philosophical implications of advanced AI technologies.\u003cbr\u003e- Discuss the theoretical limitations and unresolved challenges of neural networks.\u003cbr\u003e- Learn how topological data analysis informs neural network decision boundaries.","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":45560436129943,"sku":"9798339808039","price":2537.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798339808039.webp?v=1768596330","url":"https:\/\/atlanticbooks.com\/products\/advanced-theoretical-neural-networks-9798339808039","provider":"Atlantic Books","version":"1.0","type":"link"}