AI Capstone Project with Deep Learning

Go to Course: https://www.coursera.org/learn/ai-deep-learning-capstone

Introduction

### Course Review: AI Capstone Project with Deep Learning on Coursera #### Overview The "AI Capstone Project with Deep Learning" is a hands-on course designed for those who wish to solidify their understanding of deep learning by applying it to a real-world problem. Through this capstone project, learners will not only utilize their theoretical knowledge but also gain practical experience by deploying deep learning models to tackle significant challenges. This course encourages learners to select their favorite libraries—PyTorch or Keras—to develop and test their deep learning models. #### Course Structure The course is structured into four comprehensive modules, each focusing on a critical aspect of deep learning and image classification. Here’s a breakdown of what to expect in each module: **Module 1: Loading Data** In this introductory module, learners are introduced to the problem they will address throughout the course. The focus will be on loading a dataset of images, manipulating them, and visualizing their properties. This foundational step is crucial for understanding the nuances of the data and preparing it for further processing. **Module 2: Image Data Processing** This module emphasizes image data processing techniques that are essential for building effective classifiers. You will explore strategies for preparing image data and leveraging pre-trained models, which significantly ease the classification task while providing higher accuracy. This module serves as a critical bridge between raw data and model training. **Module 3: Building Classifiers** In Module 3, the course splits into two paths: one focused on using PyTorch and the other on using Keras. Here, learners will dive into the nitty-gritty of building linear classifiers. Using the ResNet50 pre-trained model in Keras allows participants to discover the power of transfer learning, which can save considerable time and enhance model performance. **Module 4: Peer Review Assessment** The final module culminates in a peer review assessment where participants actively engage in building their own models. In the PyTorch section, learners will use the ResNet18 pre-trained model, while the Keras section will require the construction of an image classifier using the VGG16 model. Participants will evaluate the performance of their models and compare them, reinforcing the learning experience through peer feedback. #### Pros and Cons **Pros:** - **Hands-On Experience**: The course provides ample opportunities for learners to apply concepts in a practical setting, ensuring that theoretical knowledge is translated into skills. - **Flexibility in Libraries**: The choice between PyTorch and Keras allows learners to work with the library they are most comfortable with or wish to learn more about. - **Real-World Application**: By addressing a real-world problem, learners can design solutions that are relevant and impactful, making their learning experience fruitful. - **Community Engagement**: The peer review assessment fosters a sense of community and constructive feedback among participants, enriching their learning journey. **Cons:** - **Prerequisites**: A certain level of familiarity with deep learning concepts and programming is beneficial, which may pose initial challenges for beginners. - **Time Commitment**: As with many hands-on projects, the capstone requires a dedicated time commitment to effectively complete the various components. #### Recommendation Overall, I highly recommend the "AI Capstone Project with Deep Learning" for anyone looking to deepen their understanding of deep learning and apply it to real-world challenges. It is ideal for individuals who have basic knowledge of machine learning and wish to take their skills to the next level. By the end of the course, participants will have a solid deep learning project they can showcase, enhancing both their skill set and their professional portfolio. For those aspiring to work in AI-related fields, this course is an invaluable step toward building confidence and expertise. Whether you choose PyTorch or Keras, you will emerge with practical experience, a deeper understanding of model validation, and the ability to visualize the impact of your work on the real world. Don’t miss out on the opportunity to turn your theoretical knowledge into practical, demonstrable skills!

Syllabus

Module 1 - Loading Data

In this module, you will get introduced to the problem that we will try to solve throughout the course. You will also learn how to load the image dataset, manipulate images, and visualize them.

In this Module, you will mainly learn how to process image data and prepare it to build a classifier using pre-trained models.

In this Module, in the PyTorch part, you will learn how to build a linear classifier. In the Keras part, you will learn how to build an image classifier using the ResNet50 pre-trained model.

In this Module, in the PyTorch part, you will complete a peer review assessment where you will be asked to build an image classifier using the ResNet18 pre-trained model. In the Keras part, for the peer review assessment, you will be asked to build an image classifier using the VGG16 pre-trained model and compare its performance with the model that we built in the previous Module using the ResNet50 pre-trained model.