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Advanced CNNs, Transfer Learning, and Recurrent Networks
Master advanced deep learning with CNN architectures, transfer learning, and RNN techniques for image analysis and text generation using TensorFlow.
Course Cost
Free course
Advanced
Skill Level
10 Hours
Self-paced lessons
This advanced course explores sophisticated deep learning architectures and techniques, focusing on three key areas: advanced convolutional neural networks (CNNs), transfer learning, and recurrent neural networks (RNNs). Beginning with a comprehensive exploration of CNN architectures like VGG16, AlexNet, GoogleNet, and ResNet, you'll develop practical skills through extensive case studies involving real-world image data. The course then delves into transfer learning methodologies, showing how to leverage pre-trained models to enhance performance and efficiency in new applications. In the final section, you'll master recurrent neural networks and their variants, including LSTMs and GRUs, with hands-on projects in natural language processing tasks such as part-of-speech tagging and text generation. Throughout the course, you'll apply these advanced techniques to industry-relevant problems, including medical image analysis for detecting abnormalities and implementing sophisticated text processing systems, gaining practical experience that translates directly to professional applications.
English
What you'll learn
Apply advanced CNN architectures like VGG16, AlexNet, and ResNet to complex image classification tasks
Implement transfer learning techniques to leverage pre-trained models for new applications
Develop medical image analysis systems for identifying abnormalities in X-rays
Build and train recurrent neural networks for sequence prediction tasks
Create LSTM and GRU models to address the vanishing gradient problem in sequential data
Design part-of-speech taggers using RNNs for natural language processing
Implement text generation systems with recurrent architectures
Evaluate and optimize deep learning models for industry-specific applications
Skills you'll gain
This course includes:
8.5 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 8 modules in this course
This course provides advanced training in three crucial deep learning architectures: Convolutional Neural Networks (CNNs), Transfer Learning, and Recurrent Neural Networks (RNNs). The curriculum begins with a comprehensive exploration of advanced CNN implementations, focusing on sophisticated architectures like VGG16 and their practical applications. Students engage with multiple hands-on case studies using real-world datasets, including natural images and medical X-rays, learning to develop models that solve industry-relevant problems. The Transfer Learning section examines influential pre-trained models such as AlexNet, GoogleNet, and ResNet, teaching students how to leverage these architectures to accelerate learning and improve model performance on limited datasets. The final section covers Recurrent Neural Networks in detail, exploring standard RNN architectures, Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs). Students implement these sequential models in practical applications like part-of-speech tagging and text generation, gaining expertise in handling time-series and text data.
CNN-Keras
Module 1 · 1 Hours to complete
CNN-Transfer Learning
Module 2 · 1 Hours to complete
CNN-Industry Live Project: Playing with Real-World Natural Images
Module 3 · 1 Hours to complete
CNN-Industry Live Project: Find Medical Abnormalities and Save a Life
Module 4 · 29 Minutes to complete
Recurrent Neural Networks: Introduction
Module 5 · 1 Hours to complete
Recurrent Neural Networks: LSTM
Module 6 · 1 Hours to complete
Recurrent Neutral Networks: Part-Of-Speech Tagger
Module 7 · 1 Hours to complete
Text Generation Using RNN
Module 8 · 1 Hours to complete
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Faculties
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