Deep Neural Networks Mathematical

Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using Python

This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks.

Build up your intuition of the fundamental building blocks of Neural Networks.

Advanced neural networks: Master Computer Vision with Convolutional Neural Networks (CNN) and Deep Learning

Advanced neural networks: Master Computer Vision with Convolutional Neural Networks (CNN) and Deep Learning

The Best Machine Learning Techniques for Data Science in Java and Neuroph with Application in Image Recognition

The Best Machine Learning Techniques for Data Science in Java and Neuroph with Application in Image Recognition

The Best Machine Learning Techniques for Data Science in Java and Neuroph with Application in Image Recognition

Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow| Python.