PinnedNicola BerniniUniswap V3 — Deep Dive — Part 1Bridging the gap between the White Paper and the Solidity Code3 min read·Jan 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 data6 min read·Oct 25, 2020--3--3
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 work3 min read·Aug 14, 2021----
Nicola BerniniinDiscussing Deep LearningPaper Exaplained — Discovering Symbolic Models from Deep Learning with Inductive BiasesOriginal Paper3 min read·Jul 10, 2020----
Nicola BerniniFactorVAE ExplainedReading through the paper, understanding the key aspects while catching the details2 min read·Jun 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”…4 min read·May 29, 2020----
Nicola BerniniNavigating a Paper — VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized RepresentationUnderstanding and Summarizing the following1 min read·May 25, 2020----
Nicola BerniniPaper Anatomy — FactorVAE (Part 1)Explaining this paper from ICML 20185 min read·May 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…1 min read·Jun 28, 2019----