It's a library of how I learn AI technology
- Neural Networks and Deep Learning (By Michael Nielsen)
It is an easy-to-understand introduction of deep leanrning, including Algebra, figures and code explanation of neural neworks.
- Machine Learning (By Stanford Andrew Ng)
A good course to learn machine from zero. Learning another couse will be easier after finishing this one.
- Deep Learning (By deeplearning.ai Andrew Ng et al.)
Deep Learning Specialization on Coursera. Including , <Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization>, , , .
- Deep Learning(By Google)
Google's deep learning course on Udacity
- A guide to convolution arithmetic for deep learning(By Vincent Dumoulin1 and Francesco Visin)
This guide’s objective is:
- Explain the relationship between convolutional layers and transposed convolutional layers.
- Provideanintuitiveunderstandingoftherelationshipbetweeninputshape, kernel shape, zero padding, strides and output shape in convolutional, pooling and transposed convolutional layers.
- 2006 Category Theory
- 2012 OLOGS: A CATEGORICAL FRAMEWORK FOR KNOWLEDGE REPRESENTATION
- 2012 Ontology Testing - Methodology and Tool Support
- 2016 Question Answering over Knowledge Base using Factual Memory Networks
- 2018 MolGAN: An implicit generative model for small molecular graphs
- 2018 Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings
- 2017 A Knowledge-Grounded Neural Conversation Model
- 2018 Deep Graphs
- 2014 Modeling interestingness with deep neural networks
- 2014 Convolutional Neural Networks for Sentence Classification
- 2015 Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation
- 2016 Smart Reply: Automated Response Suggestion for Email
- 2017 No Need to Pay Attention:Simple Recurrent Neural Networks Work! (for Answering “Simple” Questions)
- 2017 Convolutional sequence to sequence learning
- 2017 Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions
- 2003 Best practices for convolutional neural networks applied to visual document analysis
- 2009 Tour the world: building a web-scale landmark recognition engine
- 2016 Ssd: Single shot multibox detector
- 2017 MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- 2017 A Neural Representation of Sketch Drawings
- 2017 MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation
- 2017 Image-to-image translation with conditional adversarial networks
- 2009 AI and HCI: Two Fields Divided by a Common Focus
- 2014 Generative Adversarial Nets
- 2016 One Hundred Year Study on Artificial Intelligence (AI100)
- 2016 “Why Should I Trust You?” Explaining the Predictions of Any Classifier
- 2017 Machine Learning: The Power and Promise of Computers That Learn by Example