TensorFlow

Load and preprocess image data using tf.data.

Define a convolutional neural network (CNN) model using tf.keras.

Train the model on a labeled dataset of images.

Evaluate the model's accuracy on a held-out test set.

Deploy the trained model for real-time image classification.

Preprocess text data by tokenizing and creating embeddings.

Define a recurrent neural network (RNN) or transformer model using tf.keras.

Train the model on a large corpus of text data.

Evaluate the model's performance on a benchmark dataset.

Deploy the trained model for NLP tasks.

Load and preprocess time series data.

Define a recurrent neural network (RNN) or temporal convolutional network (TCN) model.

Train the model on historical time series data.

Evaluate the model's forecasting accuracy.

Deploy the trained model for real-time forecasting.

Collect user interaction data (e.g., purchases, clicks).

Build a collaborative filtering or content-based model.

Train the model on user interaction data.

Evaluate the model's recommendation accuracy.

Deploy the trained model to provide personalized recommendations.

Collect transaction data with labeled fraud indicators.

Build a classification model to predict fraudulent transactions.

Train the model on historical transaction data.

Evaluate the model's fraud detection accuracy.

Deploy the trained model for real-time fraud detection.