PinnedNicola BerniniUniswap V3 — Deep Dive — Part 1Bridging the gap between the White Paper and the Solidity CodeJan 9, 2022Jan 9, 2022
PinnedNicola BerniniinTowards Data ScienceWhy VAE are likelihood-based generative modelsGenerative Models are powerful tools to learn to generate realistic data samples from existing dataOct 25, 20203Oct 25, 20203
Nicola BerniniinEthereum Virtual Machine WalkthroughA walkthrough of EVM — Part 1 of NIn this series of posts I am going to present some code snippets in EVM Assembly and to explain how they workAug 14, 2021Aug 14, 2021
Nicola BerniniinDiscussing Deep LearningPaper Exaplained — Discovering Symbolic Models from Deep Learning with Inductive BiasesOriginal PaperJul 10, 2020Jul 10, 2020
Nicola BerniniFactorVAE ExplainedReading through the paper, understanding the key aspects while catching the detailsJun 6, 2020Jun 6, 2020
Nicola BerniniinDiscussing Deep LearningA Paper in 5 mins — FactorVAELearning a disentangled representation is more or less like learning a “code” where each digit represents a specific “factor of variance”…May 29, 2020May 29, 2020
Nicola BerniniNavigating a Paper — VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized RepresentationUnderstanding and Summarizing the followingMay 25, 2020May 25, 2020
Nicola BerniniPaper Anatomy — FactorVAE (Part 1)Explaining this paper from ICML 2018May 23, 2020May 23, 2020
Nicola BerniniinDiscussing Deep LearningKeras in depth tutorial — CNN and MNISTThis is the Part 1 of a series of in depth tutorials about Keras Framework: it is not just focused on the how-to but more on the why and…Jun 28, 2019Jun 28, 2019