Deep Learning News #3, Feb 13 2021 @SebastianRaschka
Deep Learning News #3, Feb 13 2021  @SebastianRaschka
Uploaded February 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books

Link to resources mentioned in the video:

(2) High-Performance Large-Scale Image Recognition Without Normalization: arxiv.org/abs/2102.06171

(3.1) Deep Learning Theory: old.reddit.com/r/MachineLearning/comments/lgsgz8/d_deep_learning_theory

(3.2) Double Descent: openai.com/blog/deep-double-descent

(4) Removing biased data to improve fairness and accuracy: arxiv.org/abs/2102.03054

(5) TracIn — A Simple Method to Estimate Training Data Influence
ai.googleblog.com/2021/02/tracin-simple-method-to-estimate.html

(6.1) This human genome does not exist: Researchers taught an AI to generate fake DNA: thenextweb.com/neural/2021/02/08/this-human-genome-does-not-exist-researchers-taught-an-ai-to-generate-fake-dna

(6.2) Creating artificial human genomes using generative neural networks: journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1009303

(7) vogue.com/article/rebag-launches-clair-ai-image-recognition-tool
Deep Learning News #3, Feb 13 2021Twitter Posts Political Ideology Classification (Student Presentation, Group 15)Build an LLM from Scratch 2: Working with text dataL16.4 A Convolutional Autoencoder in PyTorch   Code ExampleL11.2 How BatchNorm WorksL4.0 Linear Algebra for Deep Learning   Lecture OverviewL4.3 Vectors, Matrices, and BroadcastingL5.2 Relation Between Perceptron and Linear RegressionL10.5.3 (Optional) Dropout Ensemble InterpretationL8.7.1 OneHot Encoding and Multi-category Cross EntropyL4.4 Notational Conventions for Neural NetworksBuild an LLM from Scratch 7: Instruction Finetuning
Sebastian Raschka |

Deep Learning News #3, Feb 13 2021

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