gobbezlearningtoolbox 0.5.0

My learning toolbox, with every code that i have studied

PYPI PYTHON STUDY RESOURCES

This is a simple idea of grouping my notes and study material in a single space, thus creating a PyPi module where everyone can access these. Also, the print-text is colored!

Installation and Use

Install the module from PIP:

pip install gobbezlearningtoolbox

Check all the materials in the module:

from gobbezlearningtoolbox.listall import ListAll
print(ListAll())

Use the material you want to study and find all the contents (example code):

from gobbezlearningtoolbox.datascience import DataScience

ds = DataScience()
print(ds.help())

Pick a content and start studying (example code):

from gobbezlearningtoolbox.datascience import DataScience

ds = DataScience()
print(ds.machinelearning())

List of materials:

DataScience:

Methods and most common functions for Data Science, DataFrame manipulation and Machine Learning.

• imports_dataframe():

• imports_machinelearning():

• eda():

• operations():

DeepLearning:

Most common Keras Deep Learning models.

• imports(): Common imports for Keras

• common_path(): Simple step-by-step schema when dealing with Deep Learning

• dense(): A densely connected network is a pile of Dense layers to elaborate vector data.

• convnet(): Convolutions layers search local patterns, applying the same transformation to different (patch).

• rnn(): Recurrent Neural Network elaborates a single timestep input at time while keeping a state.

• pre_elaboration(): List the most used data pre-elaboration techniques

• callbacks(): Explain how to configure Keras Callbacks for different advanced techniques

• dropout(): Explain how to set a simple DropOut

• learning_rate_scheduler(): Explain how to modify learning rate based on epochs

• mixed_precision(): Explain how to setup a mixed_precision training

• gradient_accumulation(): Explain how to use gradient accumulation to increase batch size

• TensorBoard(): Show how to use Tensorflow TensorBoard to gain insights of your neural network

• plot_model(): Show how to use Keras plot_model to create an image of the layers of your neural network

• compile(): Explain how to compile a model

• train(): Explain how to train a model

• train_with_gradient_accumulation(): Explain advanced techniques for training a model

• predict_regression(): Explain how to make regression predictions with the model

• predict_classification(): Explain how to make classification predictions with the model

• evaluate(): Explain how to evaluate the model

• plot(): Explain how to plot results of the model

TelegramBot:

Simple quick-start guide for the python-telegram-bot module to create your own Telegram Bot.

• imports(): Find the most common imports to start

• quick_start(): Quick setup explanations

Django:

Simple quick-start guide to configure and create your Django backend server and database.

• imports_project(): Most common imports for your main project

• imports_application(): Find the most common imports to use in your applications

• example_main_urls(): Example of Urls of the main project

• example_model(): Example of Model for your application

• example_view(): Example of View for your application

• example_urls(): Example of Urls for your application

• settings(): Django settings file explanations

• commands(): Most important Django commands

Cookiecutter

Simple quick-start to build a pre-configured Django project.

• commands(): Cookiecutter commands

CreatePyPiModule

Simple commands to create your own PyPi Python module.

• details(): Get step-by-step details
• commands(): Commands to create your Python module

Ollama

Chat with an AI LLM model from Ollama directly in your Python code.

• imports(): Ollama imports
• commands(): Ollama shell commands to install a model
• chat(): Example code to configure and use a LLM model

Contribute

You can contribute to the GitHub Repository or use it locally:

git clone https://github.com/gobbez/gobbezlearningtoolbox.git

Notes

Please note that this module isn't complete, and it only gives a basic entry-level to start. For more information contact me. You can find the PyPi module here: https://pypi.org/project/gobbezlearningtoolbox

Updates

• improved deeplearning (05/04/2025)
• added ollama (18/03/2025)
• added createpypimodule (18/03/2025)
• added cookiecutter (18/03/2025)
• added django (18/03/2025)
• added telegrambot (18/03/2025)
• added listall (15/03/2025)
• added deeplearning (15/03/2025)
• added datascience (15/03/2025)