sjchoi86 / Dl_tutorials_10weeks
Licence: mit
Deep Learning Tutorials for 10 Weeks
Stars: ✭ 406
Labels
Projects that are alternatives of or similar to Dl tutorials 10weeks
sklearn-audio-classification
An in-depth analysis of audio classification on the RAVDESS dataset. Feature engineering, hyperparameter optimization, model evaluation, and cross-validation with a variety of ML techniques and MLP
Stars: ✭ 31 (-92.36%)
Mutual labels: deep-learning-tutorial
Deeplearning.ai Notes
These are my notes which I prepared during deep learning specialization taught by AI guru Andrew NG. I have used diagrams and code snippets from the code whenever needed but following The Honor Code.
Stars: ✭ 262 (-35.47%)
Mutual labels: deep-learning-tutorial
Awesome Deep Learning
A curated list of awesome Deep Learning tutorials, projects and communities.
Stars: ✭ 18,071 (+4350.99%)
Mutual labels: deep-learning-tutorial
Neo
Deep learning library in python from scratch
Stars: ✭ 36 (-91.13%)
Mutual labels: deep-learning-tutorial
cresset
Template repository to build PyTorch projects from source on any version of PyTorch/CUDA/cuDNN.
Stars: ✭ 573 (+41.13%)
Mutual labels: deep-learning-tutorial
Pytorch Gans
My implementation of various GAN (generative adversarial networks) architectures like vanilla GAN (Goodfellow et al.), cGAN (Mirza et al.), DCGAN (Radford et al.), etc.
Stars: ✭ 271 (-33.25%)
Mutual labels: deep-learning-tutorial
tutorials
Introduction to Deep Learning: Chainer Tutorials
Stars: ✭ 68 (-83.25%)
Mutual labels: deep-learning-tutorial
First Steps Towards Deep Learning
This is an open sourced book on deep learning.
Stars: ✭ 376 (-7.39%)
Mutual labels: deep-learning-tutorial
RACLAB
No description or website provided.
Stars: ✭ 33 (-91.87%)
Mutual labels: deep-learning-tutorial
Tensorflow Course
📡 Simple and ready-to-use tutorials for TensorFlow
Stars: ✭ 15,931 (+3823.89%)
Mutual labels: deep-learning-tutorial
Personalised-aesthetic-assessment-using-residual-adapters
Jupyter notebooks used as supporting material for an msc thesis about personalised aesthetic assessment using residual adapters.
Stars: ✭ 19 (-95.32%)
Mutual labels: deep-learning-tutorial
Adaptnlp
An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.
Stars: ✭ 278 (-31.53%)
Mutual labels: deep-learning-tutorial
nlp-notebooks
A collection of natural language processing notebooks.
Stars: ✭ 19 (-95.32%)
Mutual labels: deep-learning-tutorial
Tensorflow Project Template
A best practice for tensorflow project template architecture.
Stars: ✭ 3,466 (+753.69%)
Mutual labels: deep-learning-tutorial
deep-learning-topics
No description or website provided.
Stars: ✭ 12 (-97.04%)
Mutual labels: deep-learning-tutorial
Dlpython course
Примеры для курса "Программирование глубоких нейронных сетей на Python"
Stars: ✭ 266 (-34.48%)
Mutual labels: deep-learning-tutorial
Trainyourownyolo
Train a state-of-the-art yolov3 object detector from scratch!
Stars: ✭ 399 (-1.72%)
Mutual labels: deep-learning-tutorial
Pytorch Tutorials Examples And Books
PyTorch1.x tutorials, examples and some books I found 【不定期更新】整理的PyTorch 1.x 最新版教程、例子和书籍
Stars: ✭ 346 (-14.78%)
Mutual labels: deep-learning-tutorial
Machine Learning Curriculum
Complete path for a beginner to become a Machine Learning Scientist!
Stars: ✭ 279 (-31.28%)
Mutual labels: deep-learning-tutorial
Deep Learning Tutorial
45 Papers + TF implementations
Topics (papers)
Modern CNNs
- Alex Krizhevsky, et al. "ImageNet Classification with Deep Convolutional Neural Networks", NIPS, 2012
- Christian Szegedy, et al. "Going Deeper with Convolutions", CVPR, 2015
- Christian Szegedy, et al. "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning", ArXiv, 2016
- Kaiming He, et al. "Deep Residual Learning for Image Recognition", CVPR, 2016
- Andreas Veit, et al. "Residual Networks are Exponential Ensembles of Relatively Shallow Networks", ArXiv, 2016
- Sergey Zagoruyko and Nikos Komodakis "Wide Residual Networks", ArXiv, 2016
Regularization
- Nitish Srivastava, et al. "Dropout- A Simple Way to Prevent Neural Networks from Overfitting", JMLR, 2014
- Sergey Ioffe and Christian Szegedy "Batch Normalization- Accelerating Deep Network Training by Reducing Internal Covariate Shift, ArXiv, 2015
Algorithms behind AlphaGo
- David Silver et al. "Mastering the game of Go with deep neural networks and tree search", Nature, 2016
Optimization Methods
- Momentum, NAG, AdaGrad, AdaDelta, RMSprop, ADAM
- Diederik Kingma and Jimmy Bam "ADAM: A Method For Stochastic Optimization", ICLR, 2015
Restricted Boltzmann Machine
- Geoffrey Hinton, "A Practical Guide to Training Restricted Boltzmann Machines", 2010
Semantic Segmentation
- Jonathan Long et al. "Fully Convolutional Networks for Semantic Segmentation", CVPR, 2015
- Liang-Chieh Chen et al. "Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs", CVPR, 2015
- Hyeonwoo Noh et al. "Learning Deconvolution Network for Semantic Segmentation", ICCV, 2015
- Liang-Chieh Chen et al. "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs", ArXiv, 2016
Weakly Supervised Localization
- Maxime Oquab et al. "Is object localization for free? – Weakly-supervised learning with convolutional neural networks", CVPR, 2015
- Bolei Zhou et al. "Learning Deep Features for Discriminative Localization", CVPR, 2016
Image detection methods
- Ross Girshick et al. "Rich feature hierarchies for accurate object detection and semantic segmentation", CVPR, 2014
- Kaiming He et al. "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition", CVPR, 2015
- Ross Girshick, "Fast R-CNN", ICCV, 2015
- Shaoqing Ren et al. "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", NIPS, 2015
- Joseph Redmon et al. "You Only Look Once: Unified, Real-Time Object Detection", CVPR, 2016
- Donggeun Yoo et al. "AttentionNet: Aggregating Weak Directions for Accurate Object Detection", ICCV, 2015
- Wei Liu et al. "SSD: Single Shot MultiBox Detector", ECCV, 2016
- Joseph Redmon, Ali Farhadi, "YOLO9000: Better, Faster, Stronger", ArXiv, 2017
Visual Q&A
- Hyeonwoo Noh et al. "Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction", CVPR, 2015
- Akira Fukui et al. "Multimodal Compact Bilinear Pooling for VQA", CVPR, 2016
Deep reinforcement learning
- Volodymyr Mnih et al. "Playing Atari with Deep Reinforcement Learning", NIPS, 2013
- Hado van Hasselt et al. "Deep Reinforcement Learning with Double Q-learning", AAAI, 2016
Recurrent Neural Networks
- Alex Graves, "Generating Sequences With Recurrent Neural Networks", ArXiv, 2013
Word embedding
- Tomas Mikolov et al. "Distributed Representations of Words and Phrases and their Compositionality", NIPS, 2013
Image captioning
- Oriol Vinyals et al. "Show and Tell: A Neural Image Caption Generator", CVPR, 2015
- Kelvin Xu et al. "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention", ICML, 2015
- Justin Johnson et al. "DenseCap: Fully Convolutional Localization Networks for Dense Captioning", CVPR, 2016
Neural Styles
- Leon A. Gatys et al. "Texture Synthesis Using Convolutional Neural Networks", NIPS, 2015
- Aravindh Mahendran and Andrea Vedaldi, "Understanding Deep Image Representations by Inverting Them", CVPR, 2015
- Leon A. Gatys et al. "A Neural Algorithm of Artistic Style", ArXiv, 2015
Generative adversarial networks
- Ian J. Goodfellow et al. "Generative Adversarial Networks", NIPS, 2015
- Alec Radford et al. "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR, 2016
- Scott Reed et al. "Generative Adversarial Text to Image Synthesis", ICML, 2016
- Donggeun Yoo et al. "Pixel Level Domain Transfer", ECCV, 2016
- Phillip Isola et al, "Image-to-Image Translation with Conditional Adversarial Networks", ArXiv, 2016
- Anh Nguyen et al. "Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space", ArXiv, 2016
- Scott Reed et al. "Learning What and Where to Draw", NIPS, 2016
TF-101)
and implementations (which can be found in- Basic Python usage (numpy, matplotlib, ..)
- Handling MNIST
- Logistic regression
- Multilayer Perceptron
- Convolutional Neural Network
- Denoising Autoencoders (+Convolutional)
- Class Activation Map
- Semantic Segmentation
- Using Custom Dataset
- Recurrent Neural Network
- Char-RNN
- Word2Vec
- Neural Style
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].