Все публикации

4.1 Sequence Model

4.1 Sequence Model Basics

2.2 Data Preprocessing

2.2 Data Preprocessing (with Pandas)

3.2 Object Detection

3.2 Object Detection Basics

3.1 Preprocessing and

3.1 Preprocessing and Transfer Learning

2.3 Modern Convnets

2.3 Modern Convnets (ResNet, NiN, Inception, ShuffleNet, etc.)

2.2 LeNet and

2.2 LeNet and AlexNet

2.1 Convolutions (Padding,

2.1 Convolutions (Padding, Stride, Pooling)

1.3 Multilayer Perceptron,

1.3 Multilayer Perceptron, Dropout, Residual Connections, Batch Norm

2.1 Data Manipulation

2.1 Data Manipulation

1.2 SGD and

1.2 SGD and Backprop

1.1 Linear and

1.1 Linear and Logistic Regression

AutoGluon Overview at

AutoGluon Overview at WAIC'20

Dive into Deep

Dive into Deep Learning D2L at WAIC'20

ML Confidential or

ML Confidential or The case for AutoML

AutoGluon Overview ICML'20

AutoGluon Overview ICML'20 Workshop

AWS Machine Learning

AWS Machine Learning and Open Source

L26/1 Momentum, Adagrad,

L26/1 Momentum, Adagrad, RMSProp, Adam

L26/2 Momentum, Adagrad,

L26/2 Momentum, Adagrad, RMPProp in Python

L26/3 Course Summary

L26/3 Course Summary

L25/4 Minibatch SGD

L25/4 Minibatch SGD in Python

L25/3 Gradient descent

L25/3 Gradient descent in Python

L25/2 Gradient Descent

L25/2 Gradient Descent and Convergence Rate

L25/1 Convex Optimization

L25/1 Convex Optimization

L24/6 Transformer in

L24/6 Transformer in Python