![]() ![]() Many traditional machine learning models can be understood as special cases of neural networks. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The book is also rich in discussing different applications in order to give the practitioner a flavour of how neural architectures are designed for different types of problems. The theory and algorithms of neural networks are particularly important for understanding important concepts so that one can understand the important design concepts of neural architectures in different applications. The primary focus is on the theory and algorithms of deep learning. This book covers both classical and modern models of deep learning. Guide to Convolutional Neural Networks for Computer Visionħ+ Best Books to Learn Neural Networks to read in 2023 Neural Networks and Deep Learning: A Textbook.Efficient Processing of Deep Neural Networks.Graph Neural Networks: Foundations, Frontiers, and Applications.Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow.Java Deep Learning Cookbook: Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j.Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects.Neural Network Methods in Natural Language Processing. ![]() Neural Networks and Deep Learning: A Textbook.7+ Best Books to Learn Neural Networks to read in 2023. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |