# Tag: machine-learning

- Matryoshka Representation Learning (23 Feb 2024)

- An Intuitive Introduction to the Vision Transformer (15 Mar 2023)

- Representation Learning Through Self-Prediction Task Optimization (05 Apr 2022)

- A Few Words on Representation Learning (15 Apr 2021)

- Understanding Linear Regression using the Singular Value Decomposition (12 Oct 2020)

- Exploring SimCLR: A Simple Framework for Contrastive Learning of Visual Representations (23 Feb 2020)

- Self-Supervised Learning and the Quest for Reducing Labeled Data in Deep Learning (20 Jan 2020)

- Practical Deep Learning Audio Denoising (18 Dec 2019)

- How to Add Regularization to Keras Pre-trained Models the Right Way (26 Nov 2019)

- A Visual Guide to Time Series Decomposition Analysis (08 Aug 2019)

- Logistic Regression: The good parts (16 Feb 2019)

- An illustrative introduction to Fisher's Linear Discriminant (03 Jan 2019)

- Advanced GANs - Exploring Normalization Techniques for GAN training: Self-Attention and Spectral Norm (11 Aug 2018)

- How to deploy TensorFlow models to production using TF Serving (18 Jun 2018)

- Deeplab Image Semantic Segmentation Network (29 Jan 2018)

- Densely Connected Convolutional Networks in Tensorflow (12 Dec 2017)

- Semi-supervised Learning with GANs (31 Jul 2017)

- A Short Introduction to Generative Adversarial Networks (07 Jun 2017)

## Archive

machine-learning

deep-learning

representation-learning

tensorflow

python

self-supervised-learning

generative-models

gans

self-supervised

pytorch

keras

contrastive-learning

unsupervised-learning

torchvision

timeseries-decomposition

timeseries-analysis

timeseries

svd

singular-value-decomposition

simclr

serving

semi-supervised-learning

semantic-segmentation

regularization

production

pca

openai

logistic-regression

linear-regression

linear-models

gradient-descent

embeddings

dimensionality-reduction

densenets

deepLab_v3

deep-learning-audio

contrastive-loss

computer-vision

classification

audio-machine-learning

audio-denoising

attention