TensorFlow.NET

Load image data using .NET libraries.

Preprocess the image data into a TensorFlow tensor.

Load a pre-trained model (e.g., ResNet) using TensorFlow.NET.

Perform inference on the image tensor to obtain class probabilities.

Display the predicted class label.

Load text data from a file or API.

Tokenize the text using a suitable tokenizer.

Convert tokens to numerical indices using a vocabulary.

Create a TensorFlow model for sentiment classification.

Train the model using labeled text data.

Perform inference on new text to predict sentiment.

Load time series data from a database or file.

Preprocess the data by scaling or normalizing.

Create a recurrent neural network (RNN) model using TensorFlow.Keras.

Train the model using historical data.

Forecast future values by feeding the last known values into the model.

Load image/video data.

Preprocess the data to match model input requirements.

Load a pre-trained object detection model (e.g., SSD or YOLO).

Run inference to obtain bounding boxes and class labels.

Visualize the detected objects on the image/video.

Load the dataset.

Train an autoencoder or similar model on normal data.

Calculate reconstruction error for each data point.

Flag data points with high reconstruction error as anomalies.

Visualize anomalies for further inspection.

Load user-item interaction data.

Build a collaborative filtering model using TensorFlow.NET.

Train the model on user-item interactions.

Generate personalized recommendations for each user.

Evaluate the recommendation quality using metrics like precision and recall.