BMCARDONA/Deep-Learning-Specialization-Research-Papers

A curated list of research papers from the Deep Learning Specialization by Andrew Ng.

Deep-Learning-Specialization-Research-Papers

A curated list of research papers/articles from the Deep Learning Specialization by Andrew Ng.

Table of Contents

• Convolutional Neural Networks
  • Week 2
    • Classic Networks
    • Residual Nets (ResNets)
    • Networks in Networks, 1x1 Convolutions
    • Inception Networks
    • MobileNets
  • Week 3
    • Convolutional Implementation of Sliding Windows
    • YOLO algorithm
    • Region Proposals
    • U-Nets
  • Week 4
    • Siamese Network
    • Triplet Loss
    • What are deep ConvNets learning?
    • Neural Style transfer
    • Image Recognition
• NLP Sequence Models
  • Week 1
    • Gated Recurrent Units (GRUs)
    • Long Short-Term Memory (LSTM)
  • Week 2
    • Skip-Grams, Hierarchical Softmax
    • Word Embeddings
    • Negative Sampling
    • GloVe
    • Debiasing Word Embeddings
  • Week 3
    • Sequence to Sequence Model
    • Image Captioning
    • Bleu Score
    • Attention based intuation
    • Speech Recognition
  • Week 4
    • Transformers

Convolutional Neural Networks

Week 2

Classic Networks

• Gradient-based learning applied to document recognition
• ImageNet classification with deep convolutional neural networks
• Very deep convolutional networks for large scale image recognition

Residual Nets (ResNets)

• Deep residual networks for image recognition

Networks in Networks, 1x1 Convolutions

• Going deeper with convolutions
• Network In Network

Inception Networks

• Going Deeper with Convolutions

MobileNets

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• MobileNetV2: Inverted Residuals and Linear Bottlenecks

Week 3

Convolutional Implementation of Sliding Windows

• OverFeat: Integrated recognition, localization and detection using convolutional networks

YOLO algorithm

• You Only Look Once: Unified real-time object detection
• YOLO9000: Better, Faster, Stronger

Region Proposals

• Rich feature hierarchies for accurate object detection and semantic segmentation
• Fast R-CNN
• Faster R-CNN: Toward real-time object detection with region proposal networks

U-Nets

• U-Net: Convolutional Networks for Biomedical Image Segmentation

Week 4

Siamese Network

• DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Triplet Loss

• FaceNet: A Unified Embedding for Face Recognition and Clustering

What are deep ConvNets learning?

• Visualizing and understanding convolutional networks

Neural Style transfer

• A neural algorithm of artistic style
• Convolutional neural networks for artistic style transfer
• TensorFlow Implementation of "A Neural Algorithm of Artistic Style"

Image Recognition

• Very deep convolutional networks for large-scale image recognition

NLP Sequence Models

Week 1

Gated Recurrent Units (GRUs)

• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Long Short-Term Memory (LSTM)

• Long Short-Term Memory

Week 2

Skip-Grams, Hierarchical Softmax

• Efficient Estimation of Word Representations in Vector Space

Word Embeddings

• Linguistic Regularities in Continuous Space Word Representations
• A Neural Probabilistic Language Model

Negative Sampling

• Distributed Representations of Words and Phrases and their Compositionality

GloVe

• GloVe: Global Vectors for Word Representation

Debiasing Word Embeddings

• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Week 3

Sequence to Sequence Model

• Sequence to Sequence Learning with Neural Networks
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Image Captioning

• Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
• Show and Tell: A Neural Image Caption Generator
• Deep Visual-Semantic Alignments for Generating Image Descriptions

Bleu Score

• BLEU: a Method for Automatic Evaluation of Machine Translation

Attention based intuation

• Neural Machine Translation by Jointly Learning to Align and Translate
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Speech Recognition

• Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks

Week 4

Transformers

• Attention Is All You Need

Further Reading List

• http://deeplearning.net/reading-list/