Deep Learning Fundamentals
Neural networks, backpropagation, CNNs, RNNs, and training workflows.
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Machine Learning• intermediate
Deep Learning Fundamentals
Neural networks, backpropagation, CNNs, RNNs, and training workflows.
4.3(78 reviews)
189 students
Last updated 2024-01-18
Prof. Michael Johnson
Deep Learning Researcher
0:00 / 0:00
Deep Learning Fundamentals - Preview
What you'll learn
Understand neural network architectures
Master backpropagation and gradient descent
Build and train CNNs for image recognition
Implement RNNs and LSTMs for sequence data
Deploy deep learning models to production
Course content
3 sections • 60 minutes
1
Neural Networks 101
Perceptrons to multilayer nets.
20 min
2
Convolutional Nets
Vision models and convolutions.
22 min
3
Recurrent Nets
Sequences with RNN/LSTM/GRU.
18 min
Requirements
- Python programming experience
- Basic understanding of linear algebra
- Familiarity with machine learning concepts
- Access to GPU (optional but recommended)
About this course
This course covers the fundamentals of deep learning with hands-on projects. You'll build neural networks from scratch and learn to use popular frameworks like TensorFlow and PyTorch.
$149$249
40% OFF
One-time payment • Lifetime access
4 hours of video content
15 downloadable resources
Certificate of completion
Lifetime access
Source code included
Community support
30-day guarantee
Full refund if you're not satisfied
Course Info
English
60 hours total
3 lessons
Certificate of completion