Luxury Marketing Strategies

When it comes to marketing luxury products and services, there are certain “anti-laws” that should be considered. These so called anti-laws go against conventional marketing wisdom, and require a unique approach to attract and retain high-end clients. In this article, we’ll explore a few anti-laws of luxury marketing.

Luxury is not comparative, forget about ‘positioning’

When it comes to luxury, being unique is what counts. Luxury brands do not compare themselves with a competitor.

Luxury is the expression of a taste. Luxury items are aloof – they have a unique identity (“this is what I am”). Luxury brands have an indifference to comparison (“this is how I compare to others” is regarded as vulgar). In essence they lead with integrity.

Contrast this with mass market brands which messaging focuses on comparison to others. Mass market brands seek to establish a unique selling proposition, and communicate a competitive advantage associated with their brand, products, or services. Mass market brands attempt to offer a unique selling proposition by positioning its brand against others: (it is the most popular, it is the fastest, it is made locally, etc.).

Seek a small clientele

Mass marketing brands focus on the number of customers they have, and pay less attention to the quality of customers. Luxury brands strive for the exact opposite: less customers but the highest quality.

Luxury brands do not attempt to satisfy the common customer. Luxury brands prefer to keep individuals that are not qualified to own the brand at a distance.

Protect clients from non-clients

Luxury brands work on the open–close principle. Too much ‘open’ is harmful to the brand’s social function.

Category : Uncategorized

Large language models(LLM)

Large language models(LLM), also known as deep learning models or neural language models, are state-of-the-art AI systems designed to generate human-like text. They are trained on vast amounts of text data, typically billions of words, and are designed to capture patterns and relationships between words, phrases, and sentences. The goal of these models is to generate text that is natural, coherent, and contextually appropriate.

Large language models are typically built using deep learning techniques, specifically neural networks, and are based on a variant of the Transformer architecture. This architecture was introduced in a 2017 paper and has since become the dominant approach for building language models. The Transformer architecture is designed to process sequential data, such as text, efficiently and to capture long-range dependencies between elements in a sequence. This makes it well-suited for natural language processing tasks, such as text generation and language translation.

Large language models are trained on massive datasets that consist of a diverse range of text data, including books, news articles, websites, and social media posts. During training, the model learns to predict the next word in a sequence given the preceding words. Over time, the model becomes better at this task and starts to generate more coherent and contextually appropriate text.

The success of large language models has been remarkable and has led to many exciting applications. For example, they can be used to generate news articles, answer questions, translate languages, and even write poetry. They can also be fine-tuned for specific tasks, such as sentiment analysis or named entity recognition, by training the model on a smaller dataset that is relevant to the task.

Here are some examples of popular large language models:

  • GPT-3 (Generative Pretrained Transformer 3) by OpenAI
  • BERT (Bidirectional Encoder Representations from Transformers) by Google
  • Transformer-XL by Google
  • XLNet by Google and Carnegie Mellon University
  • RoBERTa (Robustly Optimized BERT Approach) by Facebook AI
  • CTRL (Conditional Transformer Language Model) by Salesforce Research
  • T5 (Text-to-Text Transfer Transformer) by Google
  • ALBERT (A Lite BERT) by Google
  • ERNIE (Enhance Representation through kNowledge IntEgration) by Baidu
  • GPT-2 (Generative Pretrained Transformer 2) by OpenAI

These models have been trained on massive datasets and have achieved state-of-the-art results on a variety of natural language processing tasks, such as text generation, language translation, question answering, and sentiment analysis. They have also been fine-tuned for specific tasks and have been used to build a range of language-based applications, from chatbots to language translators.

Large language models are powerful AI systems that have the ability to generate human-like text. They are trained on vast amounts of text data to capture patterns and relationships in language and generate text that is both coherent and contextually appropriate. The success of these models has led to many exciting applications and has paved the way for further advancements in AI and natural language processing.

Category : Lexicon


In Python, pip (short for “Pip Installs Packages”) is a package management system that allows users to easily install and manage libraries and dependencies for Python projects.

With pip, you can install packages from the Python Package Index (PyPI) or from local package files. PyPI is a repository of Python packages that can be installed with pip. It contains thousands of open-source packages that can be used for various purposes, such as data analysis, machine learning, web development, and more.

A wheel in Python is a package format for distributing Python libraries. It is a built distribution format, which means that it contains pre-built and pre-compiled versions of the library, making installation faster and more efficient.

A wheel file has the file extension .whl, and it contains the library code, as well as metadata such as version and dependencies. When you install a wheel package, pip will look for a wheel that is compatible with your system and install it directly, instead of building the package from source.

This is particularly useful for large libraries or libraries with many dependencies, as building them from source can take a long time and require additional dependencies to be installed.

Wheel files are useful when the user wants to share a package with others, or when you want to distribute a package to other users, because it makes the installation process faster and easier.

Here is a temporary collection of useful tips for Python

$: pip3 install –upgrade pip

$: pip3 cache purge

$: pip3 install –upgrade numpy

$: pip3 install scikit-learn

$: pip3 uninstall scipy

$: pip3 install –upgrade scipy

$: pip3 install –upgrade scikit-learn

$: pip3 install pandas

$: pip3 install nltk

Python Pandas

Pandas is a Python library that provides data structures and data analysis tools. The two main data structures in pandas are the Series and DataFrame. A Series is a one-dimensional array-like object that can hold any data type, while a DataFrame is a two-dimensional table of data with rows and columns. Pandas provides a variety of functions and methods for manipulating and analyzing data, including reading and writing data to/from various file formats (such as CSV, Excel, and JSON), filtering, aggregation, and more. It is a very powerful and widely used library for data manipulation and analysis.


Scikit-learn, also known as sklearn, is a Python library for machine learning. It provides a wide range of tools for tasks such as classification, regression, clustering, and dimensionality reduction. It is built on top of other popular Python libraries such as NumPy and pandas, and is designed to be easy to use and consistent across different algorithms.

The library includes a wide range of supervised and unsupervised learning algorithms, including popular ones such as linear regression, k-means, decision trees, and Random Forest. It also includes tools for model evaluation and selection, such as cross-validation and metrics for classification and regression.

Scikit-learn is a widely used library in the data science and machine learning community and is considered to be one of the most comprehensive libraries for machine learning in Python.

Tf-Idf Vectorizer

In scikit-learn, a Tf-Idf Vectorizer is a class that can be used to convert a collection of raw documents (i.e., a list of strings) into a numerical representation, called a Tf-Idf matrix. This matrix can then be used as input to a machine learning model.

Tf-Idf stands for “term frequency-inverse document frequency”. It is a numerical statistic that is intended to reflect how important a word is to a document in a collection of documents.

The term frequency (tf) is the number of times a word appears in a document. The inverse document frequency (idf) is a measure of how rare a word is across all documents. The product of these two values is the Tf-Idf value for a given word in a given document.

The Tf-Idf Vectorizer in scikit-learn converts a collection of raw documents into a Tf-Idf matrix by:

Tokenizing the documents (i.e., splitting them into individual words)
Building a vocabulary of all the words in the documents
Counting the number of occurrences of each word in each document
Computing the Tf-Idf values for each word in each document
Representing each document as a vector of Tf-Idf values
The resulting matrix has one row for each document and one column for each word in the vocabulary. The value at the intersection of a row and a column is the Tf-Idf value for the corresponding word in the corresponding document.

The Tf-Idf Vectorizer can also be used in text classification, clustering, and information retrieval tasks, as it provides a way to convert text into numerical features that can be used as input to machine learning algorithms.

Category : Knowledge Base

Crontab Examples

A crontab (cron tables) is a configuration file that specifies shell commands to run periodically on a given schedule. The commands in a crontab file (also known as a “cron job”) are executed by the cron daemon, a built-in Linux utility that runs processes on your system at a scheduled time.

Each line in a crontab file represents a separate cron job and follows a specific format, consisting of six fields separated by spaces:

* * * * * command

The fields represent the following:

  1. Minute (0-59)
  2. Hour (0-23)
  3. Day of the month (1-31)
  4. Month (1-12)
  5. Day of the week (0-6, with 0 being Sunday)
  6. The command to be run

The asterisks in the first five fields indicate that the command should be run every minute, every hour, every day of the month, every month, and every day of the week, respectively.

To edit your crontab file, you can use the crontab -e command, which will open the file in a text editor. When you are finished editing the file, save and exit the editor to activate the changes.

Crontab Examples

To run a command at 4:00am every day, the entry in the crontab file would be:

0 4 * * * command

To run a command every Monday at 6:00am, the entry would be:

0 6 * * 1 command

To run a command every 15 minutes using a crontab, you can use the following entry:

*/15 * * * * command

To run a command every 4 hours using a crontab, you can use the following entry:

0 */4 * * * command

To run a command at 3:15am every day using a crontab, you can use the following entry:

15 3 * * * command

Category : Knowledge Base

Linux Disk Usage

To display the amount of disk used in Linux in megabytes, you can use the df command with the -h option. This will display a list of all mounted filesystems on the system, along with the total size of the filesystem, the amount of space used, and the available space. The -h option stands for “human-readable”, and it causes the df command to display the sizes in a more readable format (e.g. “1G” for 1 gigabyte).

You can also use the –total option to display a grand total of all used and available space on all filesystems.

df -h --total

Category : Knowledge Base

Linux Memory Usage

To display the memory used by applications in Linux, you can use the ps command with the aux option. To sort the list by memory usage, you can use the –sort option and specify the rss (resident set size) field. The resident set size is the amount of memory that is currently being used by a process, in kilobytes.

Here is an example of the ps command with the –sort option:

ps aux --sort -rss

This will display a list of processes sorted in descending order by memory usage.

You can also use the top command to display a real-time view of the memory usage of processes on the system. The top command displays a list of processes, along with their CPU and memory usage, and updates the list periodically.

To sort the list by memory usage, you can press the M key.

Category : Knowledge Base


MariaDB is a fork of the MySQL database management system. It was created as a community-driven alternative to MySQL, after concerns arose over its acquisition by Oracle Corporation.

MySQL Configuration Settings

MySQL Configuration settings are located at: /etc/mysql/my.cnf

If MariaDB is installed, the my.cnf file likely references: /etc/mysql/mariadb.conf.d/50-server.cnf

MySQL Memory Configuration

To increase the amount of memory available to MySQL on Linux, you can use the –innodb_buffer_pool_size option in the MySQL configuration file (e.g. /etc/my.cnf or /etc/mysql/mariadb.conf.d/50-server.cnf) and then restart the MySQL server to apply the change.


MySQL Tuner

MySQLTuner is a script written in Perl that allows you to review a MySQL installation quickly and make adjustments to increase performance and stability.

To install: sudo apt-get install mysqltuner

To run: sudo mysqltuner

MariaDB [(none)]> SHOW VARIABLES;

MariaDB [(none)]> select information_schema.system_variables.variable_name, information_schema.system_variables.default_value, global_variables.variable_value from information_schema.system_variables,information_schema.global_variables where system_variables.variable_name=global_variables.variable_name and system_variables.default_value <> global_variables.variable_value and system_variables.default_value <> 0;

Category : Knowledge Base

Linux Error Logs

To view the error log file on Linux and filter for errors related to a specific website, you can use the tail command in combination with the grep command.

You can also use the -i option with grep to perform a case-insensitive search, so that lines containing “”, “WWW.EXAMPLE.COM”, and “WwW.ExAmPlE.CoM” would all be displayed.

Here is an example of the command with the -i option:

tail -f /path/to/error.log | grep -i ""

On Linux systems, there are several common error logs that you might want to check when troubleshooting issues. Here are some examples:

  • /var/log/syslog: This is the general system log file, where various system messages are logged.

  • /var/log/auth.log: This log file contains messages related to authentication and authorization, such as login and logout messages.

  • /var/log/kern.log: This log file contains messages related to the Linux kernel, such as system startup and shutdown messages, as well as hardware and driver-related messages.

  • /var/log/cron.log: This log file contains messages related to the execution of cron jobs.

  • /var/log/messages: This log file contains miscellaneous messages that are not logged elsewhere.

  • /var/log/apache2/access_log: If you are running the Apache web server, this log file contains the web server’s access log, which records incoming HTTP requests.

  • /var/log/apache2/error_log: If you are running the Apache web server, this log file contains the web server’s error log, which records errors and problems encountered by the web server.

Keep in mind that these log files are just examples, and the actual log files on your system may be named differently or may be located in a different directory.

Category : Knowledge Base

Popular Platforms for Artificial Intelligence (AI) Development

There are many platforms that are popular for artificial intelligence (AI) development. These platforms provide tools and libraries for tasks such as data preprocessing, model training, evaluation, and deployment. They can be used to develop a wide range of artificial intelligence applications, including image and speech recognition, natural language processing, and predictive analytics.

Some of the most widely used platforms include:

  • TensorFlow: an open-source platform for machine learning and artificial intelligence developed by Google.

  • PyTorch: an open-source machine learning platform developed by Facebook’s AI Research lab.

  • Keras: an open-source neural network library written in Python that can run on top of TensorFlow.

  • scikit-learn: an open-source machine learning library for Python.

  • Azure Machine Learning: a cloud-based platform for developing and deploying machine learning models.

  • AWS Machine Learning: a cloud-based platform for developing and deploying machine learning models.

  • IBM Watson: a cloud-based platform for developing and deploying artificial intelligence and machine learning models.

  • Apple Core ML: a framework for integrating machine learning models into iOS, iPadOS, macOS, watchOS, and tvOS applications.

  • Google Cloud AI Platform: a cloud-based platform for developing and deploying machine learning models.

  • Amazon SageMaker: a cloud-based platform for developing and deploying machine learning models.

  • Deeplearning4j: an open-source deep learning platform for the Java Virtual Machine (JVM).

  • H2O: an open-source platform for machine learning and predictive analytics.

  • Theano: an open-source platform for developing and evaluating machine learning models.

  • MXNet: an open-source deep learning platform that supports multiple programming languages.

  • Caffe: an open-source deep learning framework developed by the Berkeley Vision and Learning Center (BVLC).

  • Microsoft Cognitive Toolkit (formerly known as CNTK): an open-source deep learning platform developed by Microsoft Research.

  • Neural Designer: a commercial platform for developing and deploying machine learning models.

  • RapidMiner: a commercial platform for developing and deploying machine learning models.

  • KNIME: a commercial platform for developing and deploying machine learning models.

  • DataRobot: a commercial platform for developing and deploying machine learning models.

Category : Uncategorized

Generative Pre-training Transformer (GPT)

A Generative Pre-training Transformer, or GPT, is a type of language model developed by OpenAI. Language models are machine learning models that are trained to predict the likelihood of a sequence of words. They are used in a variety of natural language processing tasks, such as language translation, text generation, and question answering.

GPT is a type of transformer, which is a neural network architecture that processes input data using attention mechanisms to weigh different input elements differently. This allows the model to effectively process sequences of data, such as sentences in a language. GPT is “generative” because it can generate text that is similar to human-written text. It does this by learning the statistical patterns of a large dataset of text, and then using this knowledge to generate new, similar text.

GPT is also “pre-trained,” which means that it is trained on a large dataset in an unsupervised manner before being fine-tuned on a smaller dataset for a specific task. This allows the model to learn general language patterns that can be useful for a variety of tasks, rather than just one specific task. GPT has been used to generate human-like text, translate languages, and answer questions, among other things. It has also been used as the basis for more advanced language models, such as GPT-2 and GPT-3, which have achieved state-of-the-art results on a number of natural language processing tasks.

Category : Lexicon