Public facing notes page

斯坦福李飞飞深度学习课程的课后作业，有3个部分Assignment #1: Image Classification, kNN, SVM, Softmax, Neural NetworkAssignment #2: Fully-Connected Nets, Batch Normalization, Dropout, Convolutional NetsAssignment #3: Image Captioning with Vanilla RNNs, Image Captioning with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks官方资源（讲义、作业等）地址：http://cs231n.github.io/网易课程地址：http://study.163.com/course/courseMain.htm?courseId=1003223001

[CS231n: http://cs231n.github.io/] Programming Assignment - My Implemenatation

Deep Visualization Toolbox Github: https://github.com/yosinski/deep-visualization-toolbox Understanding Image Representations by Inverting Them Paper: https://arxiv.org/pdf/1412.0035v1.pdf Learning FRAME Models Using CNN filters Project page: http://www.stat.ucla.edu/~yang.lu/project/deepFrame/main.html Convergent Learning: Do different neural networks learn the same representations? Github: https://github.com/yixuanli/convergent_learning Torch-visbox https://github.com/Aysegul/torch-visbox Plot caffe models online http://ethereon.github.io/netscope/#/editor Grad-CAM: Gradient-weighted Class Activation Mapping https://github.com/ramprs/grad-cam/ Quiver: Interactive Feature Visualization for Keras https://github.com/jakebian/quiver CS231 Stanford notes on Visualization http://cs231n.github.io/understanding-cnn/

Solutions to Assignments of CS231n: Convolutional Neural Networks for Visual Recognition(http://cs231n.github.io/)

Some of the most successful deep learning methods involve artificial neural networks. Artificial neural networks are inspired by the 1959 biological model proposed by Nobel laureates David H. Hubel & Torsten Wiesel, who found two types of cells in the primary visual cortex: simple cells and complex cells. Deep learning (deep structured learning or hierarchical learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. cs231n.github.io, theano tutorial and ufldl.stanford.edu has a reference.

Homework for model pruning. CIFAR10 related code from Stanford CS231n homework http://cs231n.github.io/ and PASCAL VOC related code from https://github.com/xuzheyuan624/yolov3-pytorch

competition: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/overview evaluation: 6 class each binary, column-wise ROC AUC, https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge#evaluation dataset: dir data/train.csv, data/test.csv technology: deep learning CNN http://cs231n.github.io/convolutional-networks/ lstm: http://colah.github.io/posts/2015-08-Understanding-LSTMs/, 自己写的，扔了就跑 ： https://zhuanlan.zhihu.com/p/35756075 bi-lstm: word embedding: https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa GloVe: embedding/glove.pdf keras: https://keras.io/zh/preprocessing/text/ network: tokenize -> word embedding -> (bi-lstm or lstm) -> CNN (3*3) (or deeper, see lstm_keras.py) -> pooling -> dense net -> 6 binary sigmoid for classify

Assignment 2 from http://cs231n.github.io/

I am looking for opportunity and challenge where I can develop and apply my skills in machine learning and data visualization. Now I enjoy every single day studying deep learning from great courses, Stanford's CS231n and Stanford's CS224d. I blog my experience at https://mlxai.github.io

https://cs231n.github.io/assignments2021/assignment1/

Ref: http://cs231n.github.io/assignments2018/assignment2/

Its a copy of https://github.com/machinelearningblr/machinelearningblr.github.io/blob/master/tutorials/CS231n-Materials/CS231n-python-numpy-tutorial.ipynb

Understanding the basics of neural network from a 2D toy example using tutorial - http://cs231n.github.io/neural-networks-case-study/

Convolutional Neural Networks for Visual Recognition Course http://kamranisg.github.io/CS231n-Andrej_Karpathy_Stanford/

CS231n的一些练习，具体笔记见[我的博客](xieydd.github.io)

Assignment 3 from http://cs231n.github.io/

cs231n / cs231n.github.io

CS231n http://cs231n.github.io/

https://cs231n.github.io

http://cs231n.github.io/

http://cs231n.github.io/

http://cs231n.github.io/

http://cs231n.github.io/

http://cs231n.github.io/ cs231n assignments

CS231n Assignment REf @ https://cs231n.github.io/

Assignments from Stanford CS231n (http://cs231n.github.io/)

http://cs231n.github.io/assignments2017/assignment1/

http://cs231n.github.io/assignments2019/assignment2/

Following along http://cs231n.github.io/