000			01621nam a22002057a 4500
005			20250211111741.0
008			250211b ||||| |||| 00| 0 eng d
020			_a9781484294055
040			_cAL
041			_aeng
082			_223 _a332 _bAHLR
100			_aAhlawat Samit _9198943
245			_aReinforcement learning for finance _b: solve problems in finance with CNN and RNN using the tensorflow library
260			_aNew York _bApress _c2023
300			_axv,423p _bPB _c23x15cm.
365			_2Genral _a6391 _b₹959.20 _c₹ _d₹1199.00 _e20% _f6/02/2025
520			_aThis book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN – two kinds of neural networks used as deep learning networks in reinforcement learning. Further, the book dives into reinforcement learning theory, explaining the Markov decision process, value function, policy, and policy gradients, with their mathematical formulations and learning algorithms. It covers recent reinforcement learning algorithms from double deep-Q networks to twin-delayed deep deterministic policy gradients and generative adversarial networks with examples using the TensorFlow Python library. It also serves as a quick hands-on guide to TensorFlow programming, covering concepts ranging from variables and graphs to automatic differentiation, layers, models, andloss functions.
650			_2Economics _aFinancial Economics _9198944
942			_2ddc _cBK
999			_c233747 _d233747