Deep Learning
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The book is structured around the following key topics: fundamentals of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative models, and reinforcement learning. In addition, we also cover advanced topics such as attention mechanism, transformer architecture, multimodal learning, few-shot learning, adversarial examples and defenses, hyper parameter tuning, and regularization techniques.Each chapter starts with a brief introduction to the topic and provides an intuitive and geometric understanding of the underlying concepts. We believe that geometri...
The book is structured around the following key topics: fundamentals of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative models, and reinforcement learning. In addition, we also cover advanced topics such as attention mechanism, transformer architecture, multimodal learning, few-shot learning, adversarial examples and defenses, hyper parameter tuning, and regularization techniques.Each chapter starts with a brief introduction to the topic and provides an intuitive and geometric understanding of the underlying concepts. We believe that geometric intuition is essential for understanding deep learning concepts, and we make every effort to use visualizations to help readers build a strong mental model of the concepts. The book also provides programming intuition, which helps readers understand how to implement deep learning algorithms using popular frameworks such as Tensor Flow or Py Torch. We believe that programming intuition is crucial for readers to develop practical skills and apply deep learning techniques to real-world problems.
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