DEEP LEARNING (Theory and Implementation)

"DEEP LEARNING (Theory and Implementation)" is a comprehensive guide published by Zahira Media Publisher, designed to provide readers with an in-depth understanding of deep learning, covering both theoretical foundations and practical implementations. The book begins with an introduction to deep learning, exploring its history, definition, and working principles, and highlighting its applications and relevance across various fields.

The second section delves into the fundamentals of neural networks, explaining the basic concepts, neuron structures, and training strategies. It then moves on to discuss key deep learning architectures, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), and Feedforward Neural Networks (FNN).

In the following chapters, the book details the training process of deep learning models, including weight initialization, forward and backpropagation, loss computation, and model optimization. It also covers hyperparameter tuning and methods to address overfitting and underfitting issues.

The practical applications of deep learning are explored in the final sections, with a focus on image classification using CNNs and natural language processing for chatbot development. This book aims to equip readers with a solid understanding of deep learning concepts and inspire them to further explore and apply these techniques in real-world scenarios, making it an essential reference for anyone interested in this rapidly evolving field.

Stok

: 0

Kode ISBN

: -

Institusi

: Tidak ada

Bidang Ilmu

: Tidak ada

Halaman

: 257

Ukuran

: 23 x 15,5

Tahun

: 2024