Deep Learning
Unlock the Mysteries of AI: Master Deep Learning from Neurons to Neural Networks with Professor Bryce! Dive into cutting-edge topics like Neural Network Training, Transformers, and GANs. Your journey from basics to breakthroughs starts here!
5.0
(1)
25 learners
What you'll learn
- Understand the fundamentals and applications of deep learning.
- Identify the components and processes involved in neural network training.
- Apply techniques to enhance model performance and prevent overfitting.
- Implement advanced neural network architectures and optimization strategies.
This course includes
- 4.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 26 lessons • 4.5 hours of video
Deep Learning Essentials: From Neurons to Transformers
26 lessons
• 6.3 hours
Deep Learning Essentials: From Neurons to Transformers
26 lessons
• 6.3 hours
- What is Deep Learning? (DL 01) 10:13
- Deep Learning Prerequisites (DL 02) 05:05
- What can a single neuron compute? (DL 03) 11:46
- How to train your neuron (DL 04) 20:10
- The Data Analysis Pipeline (DL 05) 14:18
- Out-of-Sample Validation (DL 06) 14:56
- Feed-Forward Neural Networks (DL 07) 16:20
- Neural Network Backpropagation (DL 08) 18:02
- Better Activation & Loss for Classification: Softmax & Categorical Crossentropy (DL 09) 14:50
- Making Neural Networks Fast with Vectorization (DL 10) 19:44
- Vanishing (or Exploding) Gradients (DL 11) 12:07
- Avoiding Neural Network Overfitting (DL 12) 13:17
- Convolutional Layers (DL 13) 23:45
- Training large networks with little data: transfer learning and data augmentation (DL 14) 12:01
- Residual Networks and Skip Connections (DL 15) 17:00
- Word Embeddings (DL 16) 11:31
- Recurrent Neural Networks (DL 17) 09:55
- LSTMs (DL 18) 16:46
- Transformers and Self-Attention (DL 19) 17:33
- Other Metrics and the ROC Curve (DL 20) 09:02
- The Adam Optimizer (DL 21) 09:13
- Auto-Encoders (DL 22) 11:06
- Generative Adversarial Networks (DL 23) 16:25
- AlphaGo & AlphaGo Zero (DL 24) 22:42
- Computation Graphs (DL 25) 27:09
- Automatic Differentiation (DL 26) 11:24
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