Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)
Understanding Deep Learning guides readers through building machine learning systems using PyTorch and TensorFlow. It explores key neural networks such as CNN, DNN, GNN, RNN, ANN, LSTM, and GAN, while delving into natural language processing techniques. Ideal for those seeking a solid foundation in AI and deep learning applications.
About This Book
This book provides a thorough introduction to deep learning, focusing on constructing machine learning systems with popular frameworks like PyTorch and TensorFlow.
It covers a range of neural network architectures, including convolutional neural networks (CNN), deep neural networks (DNN), graph neural networks (GNN), recurrent neural networks (RNN), artificial neural networks (ANN), long short-term memory (LSTM), and generative adversarial networks (GAN).
The content extends to natural language processing (NLP), offering insights into applying these technologies in real-world scenarios.
Designed for learners and professionals, it emphasizes practical building blocks from basic neural networks to advanced applications.
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