minor updates to dependencies and link to mybinder

README.md

@@ -1,6 +1,6 @@
 # Machine Learning Exercises
 
-This repository contains the Python programming exercises accompanying the theory from my [machine learning book](https://franziskahorn.de/mlbook/). They are part of the curriculum of the [ML for Data Scientists Workshop](https://franziskahorn.de/mlws_scientist.html).
+This repository contains the Python programming exercises accompanying the theory from my [machine learning book](https://franziskahorn.de/mlbook/). They are part of the curriculum of the [ML for Data Scientists](https://franziskahorn.de/mlws_scientist.html) and [ML in Practice](https://franziskahorn.de/mlws_practice.html) Workshops.
 
 If you have any questions, please send me an [email](mailto:hey@franziskahorn.de).
 
@@ -8,13 +8,23 @@ Have fun!
 
 ### Using Python
 
-The programming exercises are written in Python. If you're unfamiliar with Python, you should work through [this tutorial](https://github.com/cod3licious/python_tutorial).
+The programming exercises are written in Python. If you're unfamiliar with Python, you should work through [this tutorial](https://github.com/cod3licious/python_tutorial) first.
 
-##### Using Python on your own computer
+#### Working on your own computer
 The [Python tutorial](https://github.com/cod3licious/python_tutorial) includes some notes on how to install Python and Jupyter Notebook on your own computer. <br>
 Please make sure you're using Python 3 and all libraries listed in the [`requirements.txt`](/requirements.txt) file are installed and up to date. You can verify this with the [`test_installation.ipynb`](/test_installation.ipynb) notebook.
 
+#### Working in the cloud
+
 ##### Using Google Colab
+
 If you have a Google account, you can also run the code in the cloud using Google Colab:
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cod3licious/ml_exercises) <br>
-While Google Colab already includes most packages that we need, should you require an additional library (e.g., `skorch` for training PyTorch neural networks in notebook 5), you can install a package by executing `!pip install package` in a notebook cell. With Colab, it is also possible to run code on a GPU, but this has to be manually selected.
+While Google Colab already includes most packages that we need, should you require an additional library (e.g., `skorch` for training PyTorch neural networks in notebook 6), you can install a package by executing `!pip install package` in a notebook cell. With Colab, it is also possible to run code on a GPU, but this has to be manually selected.
+
+
+##### Using MyBinder
+
+If you don't have a Google account, you can also use MyBinder, which does not require you to log in:
+[![Open in Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/cod3licious/ml_exercises/main) <br>
+However, this will take a while to load and might be very slow or even crash due to insufficient RAM for some of the exercises.

notebooks/1_visualize_text.ipynb

@@ -369,7 +369,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

notebooks/2_image_quantization.ipynb

@@ -199,7 +199,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

notebooks/3_supervised_comparison.ipynb

@@ -521,7 +521,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

notebooks/4_analyze_toydata.ipynb

@@ -898,7 +898,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

notebooks/5a_quality_prediction.ipynb

@@ -490,7 +490,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

notebooks/5b_quality_prediction_backend.ipynb

@@ -202,7 +202,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

notebooks/6_mnist_torch.ipynb

@@ -681,7 +681,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

notebooks/7_information_retrieval.ipynb

@@ -422,7 +422,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

notebooks/8_rl_gridmove.ipynb

@@ -349,7 +349,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

pyproject.toml

@@ -0,0 +1,25 @@
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+package-mode = false
+
+[tool.poetry.dependencies]
+python = "^3.8.1,<3.13"
+fastapi = {version = "^0.109.2", extras = ["all"]}
+ipython = ">=8.0.0"
+joblib = "^1.2.0"
+matplotlib = "^3.7.2"
+notebook = "^6.5.0"
+numpy = "^1.20.3"
+pandas = ">=1.3.5,<3.0.0"
+pillow = ">=9.1.0"
+plotly = ">=5.7.0"
+requests = ">=2.27.1"
+scipy = "^1.7.3"
+scikit-learn = "^1.2.0"
+skorch = "^0.15.0"
+torch = "^2.2.1"
+torchvision = "^0.17.1"
+xlrd = "^2.0.1"

requirements.txt

@@ -1,10 +1,11 @@
 ipython>=8.2.0
 notebook>=6.4.10
 numpy>=1.22.3
+matplotlib>=3.5.1
 pandas>=1.4.2
 scipy>=1.8.0
 scikit-learn>=1.2.0
-matplotlib>=3.5.1
+joblib>=1.2.0
 pillow>=9.1.0
 plotly>=5.7.0
 xlrd>=2.0.1

test_installation.ipynb

@@ -17,14 +17,16 @@
     "# they should not be too much behind the ones in the comments...\n",
     "import numpy\n",
     "print(\"numpy\", numpy.__version__)        # >= 1.22.3\n",
+    "import matplotlib\n",
+    "print(\"matplotlib\", matplotlib.__version__)  # >= 3.5.1\n",
     "import pandas\n",
     "print(\"pandas\", pandas.__version__)      # >= 1.4.2\n",
     "import scipy\n",
     "print(\"scipy\", scipy.__version__)        # >= 1.8.0\n",
     "import sklearn\n",
     "print(\"sklearn\", sklearn.__version__)    # >= 1.2.0\n",
-    "import matplotlib\n",
-    "print(\"matplotlib\", matplotlib.__version__)  # >= 3.5.1\n",
+    "import joblib\n",
+    "print(\"joblib\", joblib.__version__)    # >= 1.2.0\n",
     "import PIL\n",
     "print(\"pillow\", PIL.__version__)         # >= 9.1.0\n",
     "import plotly\n",
@@ -76,7 +78,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,