add presentations

.gitignore

@@ -1,190 +0,0 @@
-# ---> Python
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
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-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
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-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# UV
-#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#uv.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
-.pdm.toml
-.pdm-python
-.pdm-build/
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
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-# Celery stuff
-celerybeat-schedule
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-# SageMath parsed files
-*.sage.py
-
-# Environments
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-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
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-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
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-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
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-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
-
-# Ruff stuff:
-.ruff_cache/
-
-# PyPI configuration file
-.pypirc
-
-# ---> JupyterNotebooks
-# gitignore template for Jupyter Notebooks
-# website: http://jupyter.org/
-
-.ipynb_checkpoints
-*/.ipynb_checkpoints/*
-
-# IPython
-profile_default/
-ipython_config.py
-
-# Remove previous ipynb_checkpoints
-#   git rm -r .ipynb_checkpoints/
-

README.md

@@ -1,3 +1,13 @@
-# Ipsofacto
+# IpsofactoModel
 
-Ipsofacto Model
+## توضیحات
+هوش‌مصنوعی توضیح‌پذیر بالفعل
+
+یکی از اهداف این دستاورد افزایش اختیار و شناخت انسان در استفاده از ابزار هوش‌مصنوعی است.
+
+هدف ما تولید ابزار هوش‌مصنوعی است که بتواند داده‌ی تاثیر گذار در فرایند استنتاج را دیده و آنها را بدون نیاز به آموزش مجدد هوش‌مصنوعی عوض کند.
+
+نمونه‌ی اولیه که ما ساختیم با ترکیبی از یادگیری عمیق و یادگیری تنبل به ما اجازه‌ی دخالت در داده‌های تاثیرگذار را میدهد. میتوانید نمونه‌ی کد آن را در این صفحه ببینید و آزمایش کنید.
+
+- `ipsofactomodel.ipynb` : کد مربوط به پیاده سازی هوش مصنوعی توضیح پذیر بالفعل
+- ‍`presentation.pptx` : محتوای مربوط به ارایه کارکرد هوش مصنوعی توضیح پذیر بالفعل