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At {F}Consulting.tech, we have been transforming businesses since 2019 with our cutting-edge IT software and consulting services.
Our goal is simple: to empower your business with technology-driven solutions that drive growth and innovation. Whether you\'re an IT company looking for specialized support or a business owner seeking to harness the power of technology, you\'ve come to the right place.

Custom Software Development

From idea to implementation, we craft tailored software solutions that align with your unique business needs. Our expert developers use the latest technologies to create intuitive and efficient software that boosts your operational excellence.

IT Consulting

Unlock the true potential of your IT infrastructure with our strategic consulting services. We analyze, plan, and execute strategies that optimize your technology stack, enhance security, and streamline processes

Digital Transformation

Stay ahead in the digital age. We facilitate your digital transformation journey by integrating advanced technologies like AI, IoT, and Big Data, driving innovation and delivering exceptional customer experiences

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Secure Your Network: A Guide to Safe Protocols for Invulnerable Communication

In the vast realm of digital communication, ensuring that exchanged information across the network is secure is crucial. Many network protocols transmit data in plaintext, without any form of encryption, making them vulnerable to prying eyes. In this article, we will explore the importance of using secure protocols and provide recommended alternatives to safeguard your online communication.

The Risk of Clear Text Transmission

The transmission of information in clear text poses a significant security risk online. When data travels through the network without encryption, it becomes easily accessible to those employing “network sniffing.” This tactic relies on the use of software to inspect data packets as they traverse the network, allowing the extraction of sensitive text such as usernames and passwords.

Consequences of Network Sniffing

Network sniffing extends beyond intercepting login credentials; it can also reveal the content of documents and other files if transmitted through insecure protocols. The need to protect the confidentiality of information is crucial, and this can be achieved through adopting secure protocols for data transmission.

  1. FTP (File Transfer Protocol) port 21→ SFTP (Secure File Transfer Protocol) port 22
  2. HTTP (Hypertext Transfer Protocol) port 80 → HTTPS (Hypertext Transfer Protocol Secure) port 443
  3. Telnet port 23 → SSH (Secure Shell) port 22
  4. POP3 (Post Office Protocol 3) port 143 → IMAPS (Internet Message Access Protocol Secure) port 993
  5. SMTP (Simple Mail Transfer Protocol) port 25 → SMTPS (Simple Mail Transfer Protocol Secure) port 587
  6. LDAP port 389 (Lightweight Directory Access Protocol) → LDAPS port 636

Securing your network from intrusions is essential to ensure the safety of exchanged information online. Choosing secure protocols is the first step towards invulnerable communication. Be sure to implement the recommended alternatives to minimize risks associated with network sniffing and enjoy a secure and private online connection.

ESP32 and ESP8266 with Micropython

Flash and install firmware

ESP32

esptool.py --chip esp32 --port /dev/tty.usbserial-1410 --baud 460800 write_flash -z 0x1000 ./ESP32_GENERIC-20231005-v1.21.0.bin

ESP8266

Preliminary actions

Install esptool

download the firmware

https://micropython.org/download/ESP8266_GENERIC/

flash ESP8266

esptool.py --port /dev/tty.usbserial-0001 erase_flash

Install firmware

esptool.py --port /dev/tty.usbserial-0001 --baud 460800 write_flash --flash_size=detect 0 ./ESP8266_GENERIC-20231005-v1.21.0.bin

Unlocking the Power of Neural Networks: A Deeper Look

Today, we’re diving deeper into the fascinating world of neural networks, shedding light on some critical concepts.

Label and Features:

  • Label (output): Labels are the ultimate goals for our neural networks. They’re like the answers to a challenging question. When you show your AI a picture of a cat, the label would be “cat.” It’s what the network aims to predict.
  • Features (input): Think of features as the building blocks of your data. They’re like the clues that help the network understand and make predictions. In an image, features could be the whiskers, fur, and pointy ears that scream “cat.”

Example with Label (Used for Training):

When we train a neural network, we provide real data with both features (like the cat’s fur and whiskers) and labels (the “cat” tag). The network uses these paired examples to learn how to make predictions accurately. It’s like studying with answer keys to get better at a quiz.

Example without Label (Used for Testing):

For testing, we give our network real data (with features) but keep the labels hidden. The network makes predictions, and we see how well it does. This helps us evaluate its ability to work with new, unlabeled data.

Model:

The model is the brain behind the operation. It’s a mathematical representation of how the network should process data to produce the correct output (label). The model learns by tweaking its internal parameters, often referred to as “weights and biases.”

Regression vs. Classification:

In the world of machine learning, we have two main types of tasks: regression and classification.

  • Regression: This is like predicting a number, such as the price of a house given its size. The model finds a function to map inputs (features) to a continuous range of values.
  • Classification: Here, we’re assigning labels to data. It’s like deciding whether a given image is of a cat or a dog. The model sorts data into predefined categories.

Training and Loss:

Training is the phase where our neural network learns from examples. It fine-tunes its model to make better predictions. Loss measures how far off the network’s predictions are from the real labels. It’s like a report card telling the network what it needs to improve.

Linear Regression:

Linear regression is a fundamental technique in machine learning. It’s like drawing a straight line through data points to find the best fit. The goal is to minimize the squared loss, which measures how far each prediction is from the actual label.

The famous formula is

y = mx + b

where y is the label (output), m (or sometimes w) is the wheight, x is the feature (input) and b is the bias

The linear regression model uses a loss function called squared loss (RSS), which quantifies the error between observations and predictions. This loss guides the model in adjusting its parameters (m and b) to get closer to the real labels. In essence, it helps the model become a pro at drawing those best-fit lines. Formula for squared loss is

sum of all (label - prediction(x))2 ==> sum(y - (mx+b))2

The “adjusting” is an iterative phase. At each loop the model tries to reduce the loss applying a new value for m. The new weight value is of the weight plus (or minus, it depends if the loss is negative or positive) a value that will allow us to reach the 0 loss in an efficient way, that is in less iteration than possible. This iteration represents basically the learning rate.

Understanding these concepts is like unlocking the secret language of neural networks. They’re the tools and principles that underpin AI’s incredible capabilities. Stay curious and keep exploring this exciting world!