Get your hands dirty on PyTorch quickly.
2020-09-07
Structure A perceptron is a single-layer neural network used for supervised learning of binary classifiers Perceptron $$ g(x) = \underbrace{\sum\_{i=0}^n w\_i x\_i}\_{\text{linear separator}} + \underbrace{w\_0}\_{\text{offset/bias}} $$ Decision for classification $$ \hat{y} = \begin{cases} 1 &\text{if } g(x) > 0 \\\\ -1 &\text{else}\end{cases} $$ Update Rule $w=w+y x$ if prediction is wrong
2020-09-01
TL;DR Transformer High-Level Look Let’s begin by looking at the model as a single black box. In a machine translation application, it would take a sentence in one language, and output its translation in another.
2020-08-23
For detailed explanation and summary see: Motivation Memory cell Inputs are “commited” into memory. Later inputs “erase” early inputs An additional memory “cell” for long term memory Also being read and write from the current step, but less affected like 𝐻 LSTM Operations Forget gate Input Gate Candidate Content Output Gate Forget Forget: remove information from cell $C$
2020-08-21
Computer Vision (CV) Tasks Classification Classification + Localization Object Detection Instance Segmentation Object Localization: Coordinate prediction Sliding Window Object Localization Classification & Localization Detection Sliding Window + Classification: Regioning Sliding Window Problem: Need to test many positions and scales, and use a computationally demanding classifier
2020-08-20
LeNet (1998) Image followed by multiple convolutional / pooling layers Build up hierarchical filter structures Subsampling / pooling increases robustness Fully connected layers towards the end Brings all information together, combines it once more Output layer of 10 units, one for each digit class
2020-08-20
Tutorials How do Convolutional Neural Networks work? An Intuitive Explanation of Convolutional Neural Networks Visualization CNN Explainer MINST playground Plotting NN-SVG Papers Overview The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)
2020-08-19
Intuition A CNN model can be thought as a combination of two components: feature extraction part The convolution + pooling layers perform feature extraction. For example given an image, the convolution layer detects features such as two eyes, long ears, four legs, a short tail and so on.
2020-08-19
Architecture Overview All CNN models follow a similar architecture Input Convolutional layer (Cons-layer) + ReLU Pooling layer (Pool-layer) Fully Connected layer (FC-layer) Output Input The input layer represents the input image into the CNN.
2020-08-19
2020-08-19