1. Introduction

Goal:
The primary aim of this tutorial is to help you understand and implement object detection using deep learning. We will focus on how to identify and classify objects within images or videos using Python and the TensorFlow library.

What You Will Learn:
By the end of this tutorial, you'll be able to:
1. Understand the basic concepts of deep learning related to object detection.
2. Implement object detection algorithms with TensorFlow.
3. Classify objects within images and videos.

Prerequisites:
- Basic knowledge of Python programming.
- Familiarity with deep learning concepts and TensorFlow will be beneficial, though not mandatory.

2. Step-by-Step Guide

Concepts:
Object detection involves identifying objects within images or videos and classifying them into predefined categories. Deep learning techniques, such as Convolutional Neural Networks (CNN), are used for this purpose.

Tips:
- Use large and diverse datasets for training to improve the accuracy of the model.
- Regularly evaluate and fine-tune your model.

3. Code Examples

We'll use TensorFlow's Object Detection API for our examples.

Example 1: Importing required libraries

# Importing necessary libraries
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from IPython.display import display

In the above section, we import all the necessary libraries that we'll use throughout this tutorial.

Example 2: Setting up the model

# Model preparation 
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

Here, we define the model to be used (ssd_mobilenet_v1_coco_2017_11_17), the path to the model's pre-trained weights (frozen_inference_graph.pb), and set up labels and the number of classes.

4. Summary

This tutorial provided an introduction to object detection using deep learning. We talked about the basic concepts and how they can be implemented using TensorFlow's Object Detection API. You learned how to import necessary libraries, set up the model, and prepare it for object detection.

Next Steps:
- Explore other models and how they can be used for object detection.
- Try implementing the code on different datasets to understand its versatility.

Additional Resources:
- TensorFlow's Object Detection API
- Python Documentation

5. Practice Exercises

1. Exercise 1: Modify the code to detect objects in a video stream.
2. Exercise 2: Implement the code using a different pre-trained model.
3. Exercise 3: Evaluate the accuracy of your model.

Solutions & Explanations:
These exercises are open-ended, and the solutions will depend on the exact task and the model you choose. The key is to understand how to implement and tweak the code to suit your specific requirements.

Tips for Further Practice:
- Experiment with different models and datasets.
- Try improving the accuracy of detection by tweaking the model parameters.