Embark on a journey through the intricate workings of advanced Convolutional Neural Networks (CNNs), Transfer Learning, and Recurrent Neural Networks (RNNs). This course begins with a thorough exploration of CNNs, delving into sophisticated architectures like VGG16 and practical applications through multi-part case studies. Each segment is designed to build your foundational knowledge and practical skills incrementally.
Advanced CNNs, Transfer Learning, and Recurrent Networks
This course is part of Deep Learning with Real-World Projects Specialization
Recommended experience
What you'll learn
Apply transfer learning techniques to enhance model performance.
Utilize RNNs and LSTMs for sequence prediction tasks.
Develop practical solutions for industry-specific problems.
Master the integration of advanced neural networks in real-world applications.
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September 2024
4 assignments
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There are 8 modules in this course
In this module, we will delve into the basics of CNNs, examining the VGG16 architecture, and engage in a comprehensive case study spread across multiple practical sessions. These hands-on exercises will reinforce the theoretical concepts covered.
What's included
7 videos2 readings
In this module, we will explore various pre-trained models, their architectures, and the principles of transfer learning. Through a series of detailed sessions, we will apply these concepts in practical settings, culminating in case studies and analytical discussions.
What's included
16 videos
In this module, we will apply CNN techniques to real-world natural images, specifically focusing on flower images. Through an extensive case study spread over multiple sessions, we will learn to implement, evaluate, and refine models in a practical, industry-relevant context.
What's included
15 videos1 assignment
In this module, we will tackle the challenge of identifying medical abnormalities using CNNs. Focusing on X-Ray images, we will conduct a detailed case study over several sessions, learning to interpret medical data and develop effective diagnostic models.
What's included
7 videos
In this module, we will introduce Recurrent Neural Networks, covering their basic concepts, architecture, and types. We will delve into training methods and address common challenges like the vanishing gradient problem through a series of detailed sessions.
What's included
12 videos
In this module, we will focus on Long Short-Term Memory (LSTM) networks, covering their architecture and functionality. We will compare LSTM with other RNN variants like GRU and implement these networks in practical scenarios through a series of detailed sessions.
What's included
10 videos1 assignment
In this module, we will apply RNN techniques to develop a Part-Of-Speech tagger for natural language processing tasks. Through an extended case study spread across multiple sessions, we will develop, evaluate, and refine the performance of the Part-Of-Speech tagger.
What's included
9 videos
In this module, we will delve into the practical application of RNNs for text generation by exploring a comprehensive code generator case study divided into four parts. Each part builds on the previous one, enhancing our understanding and skills in using RNNs for generating coherent text.
What's included
4 videos1 reading2 assignments
Recommended if you're interested in Machine Learning
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.