Hi! This repo contains my implementation of Notebook's from Deep learning Specialization from deeplearning.ai by Andrew Ng.
https://www.coursera.org/learn/neural-networks-deep-learning Paper's/Notes Referred :
Week2 -
- Implementing a Neural Network from Scratch in Python – An Introduction (Denny Britz, 2015)
- Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning? (Stack Exchange)
Week3 -
- Demystifying Deep Convolutional Neural Networks (Adam Harley)
- CS231n: Convolutional Neural Networks for Visual Recognition (Stanford University)
Week4 -
- Autoreload of modules in IPython (Stack Overflow)
https://www.coursera.org/learn/deep-neural-network Paper's/Note's Referred:
- Introduction to gradients and automatic differentiation (TensorFlow Documentation)
- tf.GradientTape (TensorFlow Documentation)
https://www.coursera.org/learn/machine-learning-projects/home/welcome Paper's/Note's Refered :
- None.
https://www.coursera.org/learn/convolutional-neural-networks Paper's/Note's Referred:
Week1-
- The Sequential model (TensorFlow Documentation)
- The Functional API (TensorFlow Documentation)
Week2-
- Deep Residual Learning for Image Recognition (He, Zhang, Ren & Sun, 2015)
- deep-learning-models/resnet50.py/ (GitHub: fchollet)
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (Howard, Zhu, Chen, Kalenichenko, Wang, Weyand, Andreetto, & Adam, 2017)
- MobileNetV2: Inverted Residuals and Linear Bottlenecks (Sandler, Howard, Zhu, Zhmoginov &Chen, 2018)
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (Tan & Le, 2019)
Week3-
- You Only Look Once: Unified, Real-Time Object Detection (Redmon, Divvala, Girshick & Farhadi, 2015)
- YOLO9000: Better, Faster, Stronger (Redmon & Farhadi, 2016)
- YAD2K (GitHub: allanzelener)
- YOLO: Real-Time Object Detection
- Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs (Novikov, Lenis, Major, Hladůvka, Wimmer & Bühler, 2017)
- Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks (Dong, Yang, Liu, Mo & Guo, 2017)
- U-Net: Convolutional Networks for Biomedical Image Segmentation (Ronneberger, Fischer & Brox, 2015)
Week4-
- FaceNet: A Unified Embedding for Face Recognition and Clustering (Schroff, Kalenichenko & Philbin, 2015)
- DeepFace: Closing the Gap to Human-Level Performance in Face Verification (Taigman, Yang, Ranzato & Wolf)
- facenet (GitHub: davidsandberg)
- How to Develop a Face Recognition System Using FaceNet in Keras (Jason Brownlee, 2019)
- keras-facenet/notebook/tf_to_keras.ipynb (GitHub: nyoki-mtl)
- A Neural Algorithm of Artistic Style (Gatys, Ecker & Bethge, 2015)
- Convolutional neural networks for artistic style transfer
- TensorFlow Implementation of "A Neural Algorithm of Artistic Style"
- Very Deep Convolutional Networks For Large-Scale Image Recognition (Simonyan & Zisserman, 2015)
- Pretrained models (MatConvNet)
https://www.coursera.org/learn/nlp-sequence-models/home/welcome Paper's/Note's Referred:
Week 1:
- Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy (GitHub: karpathy)
- The Unreasonable Effectiveness of Recurrent Neural Networks (Andrej Karpathy blog, 2015)
- deepjazz (GitHub: jisungk)
- Learning Jazz Grammars (Gillick, Tang & Keller, 2010)
- A Grammatical Approach to Automatic Improvisation (Keller & Morrison, 2007)
- Surprising Harmonies (Pachet, 1999)
Week 2:
- Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (Bolukbasi, Chang, Zou, Saligrama & Kalai, 2016)
- GloVe: Global Vectors for Word Representation (Pennington, Socher & Manning, 2014)
- Woebot.
Week 4:
- Natural Language Processing Specialization (by DeepLearning.AI)
- Attention Is All You Need (Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser & Polosukhin, 2017)