Prepare the Environment

Setup Google Colab

For this lab we will use Google Colab. Google Colab has many advantages over using our own hardware for this lab:

• Free access to GPUs (at least sometimes)
• Zero installation overhead
• Ready-to-use scientific Python stack
• Cloud execution
• Notebook interface for interactive learning

This allows you to have a stable environment including powerfull hardware to train a machine learning model.

To use Google Colab, a Google Account is required (it doesn’t matter if this is your coporate or private one).

1. Go to https://colab.research.google.com/

2. Login to Google Colab by selecting the Sign In button at the top right corner

3. Select + New notebook in the Wizard

4. Now you should be able to access the Jupyter Notebook which allows to follow along with the lab.

Setup dependencies

The ML Software Stack we will use in this lab consists of the following components:

• TensorFlow
  Provides automatic differentiation, GPU computation, and model building tools.
• Keras
  A high-level API that makes deep learning intuitive.
• HuggingFace Datasets
  Enables easy loading and preprocessing of datasets.
• NumPy & Pandas
  Used for numerical operations and tabular views.

Use the below code snippet to install and import the required dependencies in your Colab environment.

#@title Setup dependencies
!pip install -q datasets

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

from datasets import load_dataset
from sklearn.metrics import classification_report, confusion_matrix

Validate if a GPU is available

GPUs accelerate deep learning dramatically:

graph LR
    CPU["CPU (8–16 cores)"] -->|Slow| Train[Training Time]
    GPU["GPU (1000+ cores)"] -->|Fast| Train

Neural networks = millions of matrix multiplications
This results in: GPUs = massive speed boost.

Use the below code snippet to check if a GPU is available in your Colab environment.

#@title Check GPU
device_name = tf.test.gpu_device_name()
if device_name:
    print("✅ GPU available:", device_name)
else:
    print("⚠️ No GPU detected. Training will still work, but slower.")