Neural Networks and Deep Learning (Course 1 of the Deep Learning Specialization)
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40 learners
What you'll learn
This course includes
- 5.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 43 lessons • 5.5 hours of video
Neural Networks and Deep Learning (Course 1 of the Deep Learning Specialization)
43 lessons
• 5.5 hours
Neural Networks and Deep Learning (Course 1 of the Deep Learning Specialization)
43 lessons
• 5.5 hours
- Welcome (Deep Learning Specialization C1W1L01) 05:32
- What is a Neural Network? (C1W1L02) 07:17
- Supervised Learning with a Neural Network (C1W1L03) 08:29
- Why is deep learning taking off? (C1W1L04) 10:22
- About This Course (C1W1L05) 02:28
- Course Resources (C1W1L06) 01:56
- Binary Classification (C1W2L01) 08:24
- Logistic Regression (C1W2L02) 06:00
- Logistic Regression Cost Function (C1W2L03) 08:12
- Gradient Descent (C1W2L04) 11:24
- Derivatives (C1W2L05) 07:11
- More Derivative Examples (C1W2L06) 10:28
- Computation Graph (C1W2L07) 03:34
- Derivatives With Computation Graphs (C1W2L08) 14:34
- Logistic Regression Gradient Descent (C1W2L09) 06:43
- Gradient Descent on m Examples (C1W2L10) 08:01
- Vectorization (C1W2L11) 08:05
- More Vectorization Examples (C1W2L12) 06:20
- Vectorizing Logistic Regression (C1W2L13) 07:33
- Vectorizing Logistic Regression's Gradient Computation (C1W2L14) 09:38
- Broadcasting in Python (C1W2L15) 11:06
- A Note on Python/Numpy Vectors (C1W2L16) 06:50
- Quick Tour of Jupyter/iPython Notebooks (C1W2L17) 03:44
- Explanation of Logistic Regression's Cost Function (C1W2L18) 07:15
- Neural Network Overview (C1W3L01) 04:27
- Neural Network Representations (C1W3L02) 05:15
- Computing Neural Network Output (C1W3L03) 09:58
- Vectorizing Across Multiple Examples (C1W3L04) 09:06
- Explanation For Vectorized Implementation (C1W3L05) 07:38
- Activation Functions (C1W3L06) 10:57
- Why Non-linear Activation Functions (C1W3L07) 05:36
- Derivatives Of Activation Functions (C1W3L08) 07:58
- Gradient Descent For Neural Networks (C1W3L09) 09:58
- Backpropagation Intuition (C1W3L10) 15:49
- Random Initialization (C1W3L11) 07:58
- Deep L-Layer Neural Network (C1W4L01) 05:51
- Forward Propagation in a Deep Network (C1W4L02) 07:16
- Getting Matrix Dimensions Right (C1W4L03) 11:10
- Why Deep Representations? (C1W4L04) 10:34
- Building Blocks of a Deep Neural Network (C1W4L05) 08:34
- Forward and Backward Propagation (C1W4L06) 10:30
- Parameters vs Hyperparameters (C1W4L07) 07:17
- What does this have to do with the brain? (C1W4L08) 03:18
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