Add new notebooks and code for object detection example using TF and …

…TFRecord

introduction_to_applying_machine_learning/README.md

@@ -22,3 +22,4 @@ These examples provide a gentle introduction to machine learning concepts as the
 - [Fraud Detection Using Graph Neural Networks](fraud_detection_using_graph_neural_networks) is an example to identify fraudulent transactions from transaction and user identity datasets.
 - [Identify key insights from textual document](identify_key_insights_from_textual_document) contains comphrensive notebooks for five natural language processing tasks Document Summarization, Text Classification, Question and Answering, Name Entity Recognition, and Semantic Relation Extracion, and zero-shot prompt engineering to solve various NLP tasks using the state-of-the-art Flan T5 XL model.
 - [Churn Prediction Multimodality of Text and Tabular](churn_prediction_multimodality_of_text_and_tabular) is an example notebook to train and deploy a churn prediction model that uses state-of-the-art natural language processing model to find useful signals in text. In addition to textual inputs, this model uses traditional structured data inputs such as numerical and categorical fields.
+- [Object Detection with TensorFlow and TF Records](object_detection_with_tensorflow_and_tfrecords) demonstrate how to build a data preprocessing pipeline to generate TFRecords, which are utilized for training a TensorFlow model using Keras CV and the SageMaker SDK. These notebooks cover object detection with TensorFlow and the creation of TFRecords.

introduction_to_applying_machine_learning/index.rst

@@ -10,3 +10,4 @@ Applying ML Algorithms
    introduction_to_applying_machine_learning/huggingface_sentiment_classification/huggingface_sentiment
    introduction_to_applying_machine_learning/mixtral_tune_and_deploy/mixtral-8x7b
    sagemaker_pytorch_model_zoo/sagemaker_pytorch_model_zoo
+   introduction_to_applying_machine_learning/object_detection_with_tensorflow_and_tfrecords