Update README.md

Author: jialuechen

README.md

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-**TorchQuant** is a high-performance, differentiable quantitative finance library built on top of PyTorch's automatic differentiation and GPU acceleration. It provides comprehensive tools for derivatives pricing, risk management, and stochastic model calibration. The core innovation in this pytorch extension library is to map various exotic characteristics into corresponding deep learing mechanisms. More kinds of derivatives will be supported as the library grows.
+**TorchQuant** is a high-performance, differentiable quantitative finance library built on top of PyTorch's automatic differentiation and GPU acceleration. It provides comprehensive tools for derivatives pricing, risk management, and stochastic model calibration. The core innovation in this pytorch extension library is to map various exotic characteristics into corresponding deep learing mechanisms (see the below table). More kinds of derivatives will be supported as the library grows.
 
-## Financial Instruments and Deep  Mechanisms Analogy (Partial)
+## Financial Instruments and Deep  Mechanisms Analogy (Partial Examples)
 
 This table outlines the analogy between financial instruments and neural network components, reflecting how structural and functional characteristics of financial derivatives can inspire the design of all the pricing components in TorchQuant.