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)
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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