Search Results for "deep learning with python"

The Rust语言圣经 (Rust Language Bible) is a comprehensive, Chinese-language Rust tutorial that aims to take learners from beginner concepts to advanced mastery. The course is carefully designed with a structured catalog, vivid and approachable language, and an engaging style that avoids the dry and mechanical tone of many technical books. It covers the basics of Rust, such as ownership, borrowing, lifetimes, traits, and generics, but also dives deep into advanced topics like performance...

... the original series, enhanced with detailed explanations, formula derivations, and optimized solutions. In July 2023, work on the second edition was announced, which expands the project with updated content, new problems inspired by recent big-tech interviews, and introductions to modern machine learning techniques such as XGBoost, CNNs, RNNs, and LSTMs. This collection serves both as a historical record of algorithm problem-solving and as a living resource for programmers preparing for interviews.

DeepLearning-500-questions is a comprehensive handbook that compiles 500 important questions on deep learning, curated to serve as a valuable reference for AI engineer interviews and self-study. Edited by Tan Jiyong with contributions from Guo Zizhao, Li Jian, and Dian Songyi, the book systematically covers both theoretical foundations and practical applications of deep learning. The first sections focus on essential mathematics, machine learning basics, and deep learning foundations...

CLUE (C Learning Undergraduate Environment) is a software allowing students to work on assignments in the C language while benefiting from support for peer testing, hyper-linked tutorials to help them understand compiler error messages, detection of "novice errors" with warnings hyper-linked to tutorials. Sponsored by National Science Foundation under award CCLI #0836863.

Pytest is an open source testing and evaluation framework written in Python. It is compliant with IMS Global Learning Consortium, Inc. QTI specification and can be used as a tests editor or as a server for creating a networked testing framework.

A modular web-based environment for collaborative learning. Using this system, users will be able to publish reports on their work, comment on other's report, and collaborativly edit reports.