Update markdown instructions for Penguin classification exercise (1)

exercises/01_penguin_classification.ipynb

@@ -23,20 +23,23 @@
     "### Task 1: look at the data\n",
     "In the following code block, we import the ``load_penguins`` function from the ``palmerpenguins`` package.\n",
     "\n",
-    "- Call this function, which returns a single object, and assign it to the variable ``data``.\n",
-    "  - Print ``data`` and recognise that ``load_penguins`` has returned a ``pandas.DataFrame``.\n",
-    "- Consider which features it might make sense to use in order to classify the species of the penguins.\n",
-    "  - You can print the column titles using ``pd.DataFrame.keys()``\n",
-    "  - You can also obtain useful information using ``pd.DataFrame.Series.describe()``"
+    "- Call this function, which returns a single object in the form of a ``pandas.DataFrame``, and assign it to the variable ``data``.\n",
+    "  - Print ``data`` and recognise that ``load_penguins`` has returned the dataframe.\n",
+    "- Analyse which features it might make sense to use in order to classify the species of the penguins.\n",
+    "  - You can print the column names using ``pd.DataFrame.keys()``\n",
+    "  - You can also obtain useful statical information on the dataset using ``pd.DataFrame.Series.describe()``"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from palmerpenguins import load_penguins"
+    "from palmerpenguins import load_penguins\n",
+    "\n",
+    "# Load the penguin data\n",
+    "penguins = load_penguins()\n"
    ]
   },
   {
@@ -402,7 +405,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -416,7 +419,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,

slides/slides.qmd

@@ -1,6 +1,6 @@
 ---
-title: "Introduction to Machine Learning with PyTorch"
-subtitle: "NCAS & ICCS Summer Schools 2023"
+title: "Introduction to Neural Networks with PyTorch"
+subtitle: "ICCS Summer School 2024"
 format:
   revealjs:
     embed-resources: true
@@ -12,12 +12,12 @@ format:
     theme: [dark, custom.scss]
     render-on-save: true
 authors:
-  - name: Jack Atkinson
-    orcid: 0000-0001-5001-4812
+  - name: Matt Archer
+    orcid: 0009-0002-7043-6769
     affiliations: ICCS/Cambridge
-  - name: Jim Denholm
-    affiliations: Cambridge
-    orcid: 0000-0002-2389-3134
+  - name: Surbhi Goel
+    affiliations: ICCS/Cambridge
+    orcid: 0009-0005-0237-756X
 revealjs-plugins:
   - attribution
 ---
@@ -61,6 +61,17 @@ Helping Today:
 
 # Part 1: Neural-network basics -- and fun applications.
 
+## Machine learning
+
+- Machine learns underlying patterns and relations in given data to produce an output.
+
+- Machine learning is accomplished by learning a mathematical function that can represent the data. 
+
+
+## Types of Machine learning
+
+- Learning can be supervised, unsupervised, semi-supervised, self-supervised, reinforcement etc. depending on the task in hand.   
+
 
 ## Stochastic gradient descent (SGD)
 
@@ -253,6 +264,10 @@ Image source: [3Blue1Brown](https://www.3blue1brown.com/topics/neural-networks)
 :::
 
 
+## The Learning process summarised
+
+![](ModelLearning.png)
+
 # Python and PyTorch {.smaller}
 
 - In this workshop-lecture-thing, we will implement some straightforward neural networks in PyTorch, and use them for different classification and regression problems.
@@ -261,6 +276,40 @@ Image source: [3Blue1Brown](https://www.3blue1brown.com/topics/neural-networks)
 - See the PyTorch website: [https://pytorch.org/](https://pytorch.org/)
 
 
+
+## Getting to the Exercise
+ *Github Repository Cloning*
+
+- Navigate to 
+[https://tinyurl.com/ml-iccs-24](https://tinyurl.com/ml-iccs-24)
+- Go to terminal and type in the below command
+- `git clone https://github.com/Cambridge-ICCS/practical-ml-with-pytorch`
+
+## Using online platform
+*Using Colab*
+
+`https://tinyurl.com/4arrjjt5`
+
+*Using Jupyter Notebook*
+
+- Download the repo from the [https://tinyurl.com/ml-iccs-24](https://tinyurl.com/ml-iccs-24)
+- `cd <path_to_your_repo>`
+- `jupyter notebook`
+
+## Creating virtual environement
+*Installing with venv*
+
+- `python3 -m venv venv`
+- `source venv/bin/activate`
+
+*Installing with conda*
+
+- `conda create -n ml-workshop "python>=3.9.10"`
+- `conda activate ml-workshop`
+- `cd practical-ml-with-PyTorch`
+- `pip install . `
+
+
 # Exercises