#deep learning Cheatsheets

A comprehensive cheat sheet covering essential Artificial Intelligence concepts, algorithms, and techniques. This guide is designed to provide a quick reference for AI practitioners and students.

A quick reference guide for Apache MXNet, covering essential concepts, modules, and operations for building and training neural networks.

A comprehensive cheat sheet for PyTorch, covering essential concepts, modules, and functions for building and training neural networks.

A concise cheat sheet for Keras, covering fundamental concepts, common layers, model building, training, and evaluation techniques for deep learning.

A quick reference guide to TensorFlow, covering its core concepts, common operations, and essential functions for building and training machine learning models.

A concise reference for deep learning practitioners using PyTorch, covering core concepts, modules, and common operations.

A quick reference guide for Caffe, covering its architecture, layers, tools, and usage for deep learning tasks.

A comprehensive cheat sheet covering core machine learning algorithms, evaluation metrics, and essential concepts for interview preparation. Includes supervised, unsupervised learning, deep learning and NLP.

A comprehensive cheat sheet covering various machine learning algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning, along with deep learning architectures.