Deep Learning Fundamentals - Intro to Neural Networks
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What you'll learn
This course includes
- 4.3 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 37 lessons • 4.3 hours of video
Deep Learning Fundamentals - Intro to Neural Networks
37 lessons
• 4.3 hours
Deep Learning Fundamentals - Intro to Neural Networks
37 lessons
• 4.3 hours
- Deep Learning playlist overview & Machine Learning intro 04:28
- Deep Learning explained 03:31
- Artificial Neural Networks explained 04:45
- Layers in a Neural Network explained 06:16
- Activation Functions in a Neural Network explained 05:01
- Training a Neural Network explained 03:47
- How a Neural Network Learns explained 07:00
- Loss in a Neural Network explained 04:13
- Learning Rate in a Neural Network explained 04:26
- Train, Test, & Validation Sets explained 06:58
- Predicting with a Neural Network explained 05:06
- Overfitting in a Neural Network explained 04:16
- Underfitting in a Neural Network explained 03:31
- Supervised Learning explained 04:46
- Unsupervised Learning explained 05:23
- Semi-supervised Learning explained 03:46
- Data Augmentation explained 03:20
- One-hot Encoding explained 06:00
- Convolutional Neural Networks (CNNs) explained 08:37
- Convolutions in Deep Learning - Interactive Demo App 13:27
- Visualizing Convolutional Filters from a CNN 06:09
- Zero Padding in Convolutional Neural Networks explained 13:48
- Max Pooling in Convolutional Neural Networks explained 10:50
- Max Pooling in Deep Learning - Interactive Demo App 07:08
- Backpropagation explained | Part 1 - The intuition 10:56
- Backpropagation explained | Part 2 - The mathematical notation 11:03
- Backpropagation explained | Part 3 - Mathematical observations 11:26
- Backpropagation explained | Part 4 - Calculating the gradient 14:26
- Backpropagation explained | Part 5 - What puts the "back" in backprop? 14:10
- Weight Initialization explained | A way to reduce the vanishing gradient problem 10:12
- Bias in an Artificial Neural Network explained | How bias impacts training 07:12
- Learnable Parameters in an Artificial Neural Network explained 06:34
- Learnable Parameters in a Convolutional Neural Network (CNN) explained 07:32
- Regularization in a Neural Network explained 05:55
- Batch Size in a Neural Network explained 03:54
- Fine-tuning a Neural Network explained 04:49
- Batch Normalization (“batch norm”) explained 07:32
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