Carnegie Mellon University Deep Learning
“Deep Learning” systems, typified by deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. As a result, expertise in deep learning is fast changing from an esoteric desirable to a mandatory prerequisite in many advanced academic settings, and a large advantage in the industrial job market. In this course we will learn about the basic...
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1,013 episodes
Who and what this show talks about
The people, companies, books and ideas that recur across 5 analyzed episodes.
- Mean Squared Error (Concept, 2 episodes)
- Kaggle (Company, 2 episodes)
- Cross Entropy Loss (Concept, 2 episodes)
- AutoLab (Company, 2 episodes)
- Neural Network (Concept, 2 episodes)
- MLP (Concept)
- Stochastic Gradient Descent (Concept)
- Anaconda (Product)
- Python 3.13 (Product)
- Batch Normalization (Concept)
- Weights and Biases (Product)
- Patza (Company)
- Ahmed Isisa (Person)
- Mchun Jang (Person)
- PyTorch (Product)
- Neurons (Concept)
- Google Translate (Product)
- Multi-layer Perceptron (Concept)
- Boolean Functions (Concept)
- Activation Function (Concept)
Mentioned together
- Kaggle and AutoLab: competes with
- Mean Squared Error and Cross Entropy Loss: together in 2 episodes
- Multi-layer Perceptron and Neurons: part of
- MLP and Mean Squared Error: part of
- MLP and Cross Entropy Loss: part of
- MLP and Stochastic Gradient Descent: part of
- Universal Approximation Theorem and Multi-layer Perceptron: part of
- Multi-layer Perceptron and Activation Function: part of
- Neurons and Activation Function: part of
- Activation Function and Sigmoid: part of