000			01932nam a22002177a 4500
005			20240210180223.0
008			240210b |||||||| |||| 00| 0 eng d
020			_a9789391392574
082			_a006.31 _bGRI
100			_aGridin, Ivan _914229
245			_aTime series forecasting using deep learning: _bcombining PyTorch, RNN, TCN and deep neural network models to provide production-ready prediction solutions
260			_bBPB Publications _aNew Delhi _c2023
300			_axxiii, 289 p.
365			_aINR _b899.00
520			_aThis book is amid at teaching the readers how to apply the deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch. The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the programme has been developed. You will try to use machine learning to identify the patterns that can help us forecast the future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task. Finally by the end of the book, readers would be able to solve complex real-world prediction issues by applying the models and strategies learnt throughout the course of the book. This book also offers another great way of mastering deep learning and its various techniques. (https://in.bpbonline.com/products/time-series-forecasting-using-deep-learning?_pos=1&_sid=5a64ea01e&_ss=r&variant=41900465946811)
650			_aDeep learning _915633
650			_aTime series _915649
650			_aForecasting _915650
650			_aNeural network model _915651
942			_cBK _2ddc
999			_c5969 _d5969