Deep-Learning-Specialization-Coursera and deeplearning-notes

Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Stars: 462
Forks: 380
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: Apache-2.0
Stars: 553
Forks: 168
Downloads:
Commits (30d): 0
Language:
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About Deep-Learning-Specialization-Coursera

abdur75648/Deep-Learning-Specialization-Coursera

This repo contains the updated version of all the assignments/labs (done by me) of Deep Learning Specialization on Coursera by Andrew Ng. It includes building various deep learning models from scratch and implementing them for object detection, facial recognition, autonomous driving, neural machine translation, trigger word detection, etc.

This collection of assignments provides practical examples for understanding and building advanced artificial intelligence models. It offers ready-to-use code for tasks like recognizing objects in images, identifying faces, and translating languages. Anyone learning or teaching deep learning concepts would find these practical solutions helpful.

deep-learning-education computer-vision natural-language-processing machine-learning-training

About deeplearning-notes

lijqhs/deeplearning-notes

Notes for Deep Learning Specialization Courses led by Andrew Ng.

These notes summarize the Deep Learning Specialization from Coursera, helping you grasp the core concepts of building neural networks and managing machine learning projects. They take the complex information from the course videos and present it as digestible text, outlining topics like convolutional networks and recurrent neural networks. This resource is for anyone studying or interested in deep learning, from students to professionals looking to quickly review key concepts.

deep-learning-education machine-learning-training neural-network-concepts data-science-learning ai-curriculum

Scores updated daily from GitHub, PyPI, and npm data. How scores work