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-001. Chapter 1. What is deep learning.mp4286711152023-09-06 18:48:39
-002. Chapter 1. Learning representations from data.mp4330219432023-09-06 18:48:43
-003. Chapter 1. Understanding how deep learning works, in three figures.mp4126167462023-09-06 18:48:46
-004. Chapter 1. Don’t believe the short-term hype.mp4292665602023-09-06 18:48:49
-005. Chapter 1. Before deep learning a brief history of machine learning.mp4273310282023-09-06 18:48:52
-006. Chapter 1. Decision trees, random forests, and gradient boosting machines.mp4380337192023-09-06 18:48:57
-007. Chapter 1. Why deep learning Why now.mp4273096272023-09-06 18:49:01
-008. Chapter 1. A new wave of investment.mp4236672192023-09-06 18:49:04
-009. Chapter 2. Before we begin the mathematical building blocks of neural networks.mp4217348622023-09-06 18:49:07
-010. Chapter 2. Data representations for neural networks.mp4128368112023-09-06 18:49:09
-011. Chapter 2. Real-world examples of data tensors.mp4191059222023-09-06 18:49:12
-012. Chapter 2. The gears of neural networks tensor operations.mp4142979232023-09-06 18:49:14
-013. Chapter 2. Tensor dot.mp4109706262023-09-06 18:49:16
-014. Chapter 2. The engine of neural networks gradient-based optimization.mp4227599572023-09-06 18:49:19
-015. Chapter 2. Stochastic gradient descent.mp4216897892023-09-06 18:49:23
-016. Chapter 2. Looking back at our first example.mp4101856302023-09-06 18:49:25
-017. Chapter 3. Getting started with neural networks.mp4279176202023-09-06 18:49:28
-018. Chapter 3. Introduction to Keras.mp4181836752023-09-06 18:49:31
-019. Chapter 3. Setting up a deep-learning workstation.mp4242493532023-09-06 18:49:34
-020. Chapter 3. Classifying movie reviews a binary classification example.mp4250762132023-09-06 18:49:37
-021. Chapter 3. Validating your approach.mp4162186452023-09-06 18:49:39
-022. Chapter 3. Classifying newswires a multiclass classification example.mp4291019202023-09-06 18:49:43
-023. Chapter 3. Predicting house prices a regression example.mp4276756582023-09-06 18:49:46
-024. Chapter 4. Fundamentals of machine learning.mp4342474912023-09-06 18:49:50
-025. Chapter 4. Evaluating machine-learning models.mp4280218492023-09-06 18:49:54
-026. Chapter 4. Data preprocessing, feature engineering, and feature learning.mp4253667622023-09-06 18:49:57
-027. Chapter 4. Overfitting and underfitting.mp4214801152023-09-06 18:50:00
-028. Chapter 4. Adding weight regularization.mp4193026772023-09-06 18:50:03
-029. Chapter 4. The universal workflow of machine learning.mp4213222472023-09-06 18:50:05
-030. Chapter 4. Developing a model that does better than a baseline.mp4243895642023-09-06 18:50:09

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