Data Science & AI
intermediate
Deep Learning & Neural Networks
A comprehensive course covering Deep learning. Topics include neural network architecture, activation functions, backpropagation, CNNs for image recognition, RNNs for sequences, transfer learning, TensorFlow, and PyTorch. Designed for intermediate level learners.
14 hours
4 modules
12 lessons
1 enrolled
Dr
Dr. Priya Patel
Professor of Data Science & Artificial Intelligence
Free
Full access to all course materials
Course Content
Foundations of Neural Network Architecture
3 lessons
Neural Network Architecture
22 min
Activation Functions
28 min
Applying Neural Network Architecture
23 min
Understanding CNNs for Image Recognition
3 lessons
Backpropagation
35 min
CNNs for Image Recognition
20 min
Applying Backpropagation
32 min
Working with RNNs for Sequences
3 lessons
RNNs for Sequences
20 min
Transfer Learning
24 min
Applying RNNs for Sequences
25 min
Advanced TensorFlow
3 lessons
TensorFlow
23 min
PyTorch
25 min
Applying TensorFlow
21 min