Il documento discute la classificazione nel NLP, affrontando la differenza tra dati strutturati e non strutturati, introducendo il modello Bag of Words per la rappresentazione delle caratteristiche, e descrivendo vari esempi di classificazione. Viene esaminato il processo di etichettatura dei dati, la classificazione bayesiana, la regressione logistica binaria e multinomiale, e l'importanza dell'ingegneria delle caratteristiche e della normalizzazione delle probabilità. La conclusione sottolinea come la regressione logistica apprenda parametri che pesano l'importanza delle caratteristiche.

Addresses the challenges of structured vs. unstructured data, and introduces the Bag of Words model for feature representation. Explain why each document can be represented as a point in a d-dimensional feature space.

NLP - Classification

The document discusses supervised learning in machine learning, where models are trained on labeled datasets to predict outputs. It explains neural networks, their structure, including input, hidden, and output layers, and the process of weight adjustment using gradient descent. Additionally, it covers deep learning as a subset of machine learning that utilizes multi-layer neural networks for complex tasks, with a focus on PyTorch for implementation and includes a code example for a simple neural network model.

Discusses neural networks, their structure, including input, hidden, and output layers, and the process of weight adjustment using gradient descent. Additionally, this post covers deep learning as a subset of machine learning that utilizes multi-layer neural networks for complex tasks, with a focus on PyTorch for implementation and includes a code example for a simple neural network model.

NLP - Neural Networks

Explores the Bayes' theorem, which calculates the posterior probability of a hypothesis given evidence. Discusses how the generalized Bayesian model simplifies calculations under the Naive Bayes assumption of independent effects.

NLP - Generalized Bayes’ Rule

The document reviews key concepts in probability, including random variables (discrete and continuous), probability distributions (PMF and PDF), expectation and variance, joint distributions, conditional expectation, and complex queries involving random variables. It discusses applications such as order statistics and the expectation of products, along with conditional probabilities and normalization constants.

Reviews key concepts in probability, including random variables (discrete and continuous), probability distributions (PMF and PDF), expectation and variance, joint distributions, conditional expectation, and complex queries involving random variables.

NLP - Probability Review

In projects involving the ESP32, internal flash storage may be insufficient, necessitating the use of a Micro SD card. However, many people encounter SD card instability even when following a guide. This post provides steps to troubleshoot SD card failures, including following instructions, trying different SD cards, checking voltage requirements, measuring voltage with a multimeter, and optimizing power supply connection.

ESP32 - How to Troubleshoot Micro SD Card Failures

The ESP32 microcontroller has an internal Real-Time Clock (RTC) with limitations in maintaining time during power loss. To overcome this, an external RTC module, such as DS1307, DS3231, or PCF8563, can be integrated with the ESP32. This requires wiring the module to the ESP32, using I2C or other interfaces, and optionally connecting a backup battery. Example code is provided for setting up the external RTC and synchronizing with NTP servers for accurate timekeeping in IoT applications.

The ESP32 microcontroller has an internal Real-Time Clock (RTC) with limitations in maintaining time during power loss. To overcome this, an external RTC module, such as DS1307, DS3231, can be integrated with the ESP32. This requires wiring the module to the ESP32, using I2C or other interfaces, and optionally connecting a backup battery. Example code is provided for setting up the external RTC and synchronizing with NTP servers for accurate timekeeping in IoT applications.

ESP32 - Time and External RTC

Qiwei Mao published their first YouTube video explaining the system design for their ESP32 data logger project and demoing the React front end. They identified areas for improvement, such as getting straight to the main content, using bullet points for summaries, changing the camera view shape, demoing with more data, and explaining how to add different sensors. They also shared their plan for the next video on ESP32 RTC Clock, aiming to keep it under 2 minutes and providing a brief introduction, quick overview, step-by-step setup, and conclusion. Qiwei Mao is a geotechnical engineer passionate about IoT systems and exploring low-power microcontrollers and LoRa communication systems.

Today, I published my first YouTube video explaining the system design for my ESP32 data logger project and demoing the React front end for the ESP32. While the video was generally well done, I identified several areas for improvement. Qiwei Mao, Atlanta GA.