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🌲 / learning Programming Deep Learning with Python video edition PART 1 THE FUNDAMENTALS OF DEEP LEARNING

c File Name Size files dur q vq aq Vc Ac Fmt Res fps T Date
-001. Chapter 1. What is deep learning.mp428671115520.405624440305125h264aacmov1280x72030mp42023-09-06 18:48:39
-002. Chapter 1. Learning representations from data.mp433021943580.522109455319125h264aacmov1280x72030mp42023-09-06 18:48:43
-003. Chapter 1. Understanding how deep learning works, in three figures.mp412616746313.887347321186125h264aacmov1280x72030mp42023-09-06 18:48:46
-004. Chapter 1. Don’t believe the short-term hype.mp429266560424.507211551416125h264aacmov1280x72030mp42023-09-06 18:48:49
-005. Chapter 1. Before deep learning a brief history of machine learning.mp427331028529.484626412277125h264aacmov1280x72030mp42023-09-06 18:48:52
-006. Chapter 1. Decision trees, random forests, and gradient boosting machines.mp438033719656.590658463328125h264aacmov1280x72030mp42023-09-06 18:48:57
-007. Chapter 1. Why deep learning Why now.mp427309627537.82059406270125h264aacmov1280x72030mp42023-09-06 18:49:01
-008. Chapter 1. A new wave of investment.mp423667219404.72381467332125h264aacmov1280x72030mp42023-09-06 18:49:04
-009. Chapter 2. Before we begin the mathematical building blocks of neural networks.mp421734862532.085261326191125h264aacmov1280x72030mp42023-09-06 18:49:07
-010. Chapter 2. Data representations for neural networks.mp412836811539.58532919055125h264aacmov1280x72030mp42023-09-06 18:49:09
-011. Chapter 2. Real-world examples of data tensors.mp419105922445.010454343208125h264aacmov1280x72030mp42023-09-06 18:49:12
-012. Chapter 2. The gears of neural networks tensor operations.mp414297923355.613605321186125h264aacmov1280x72030mp42023-09-06 18:49:14
-013. Chapter 2. Tensor dot.mp410970626440.1574619964125h264aacmov1280x72030mp42023-09-06 18:49:16
-014. Chapter 2. The engine of neural networks gradient-based optimization.mp422759957572.650522317182125h264aacmov1280x72030mp42023-09-06 18:49:19
-015. Chapter 2. Stochastic gradient descent.mp421689789514.670295337201125h264aacmov1280x72030mp42023-09-06 18:49:23
-016. Chapter 2. Looking back at our first example.mp410185630240.67483338203125h264aacmov1280x72030mp42023-09-06 18:49:25
-017. Chapter 3. Getting started with neural networks.mp427917620604.485079369234125h264aacmov1280x72030mp42023-09-06 18:49:28
-018. Chapter 3. Introduction to Keras.mp418183675451.279819322187125h264aacmov1280x72030mp42023-09-06 18:49:31
-019. Chapter 3. Setting up a deep-learning workstation.mp424249353445.846349435299125h264aacmov1280x72030mp42023-09-06 18:49:34
-020. Chapter 3. Classifying movie reviews a binary classification example.mp425076213612.078005327192125h264aacmov1280x72030mp42023-09-06 18:49:37
-021. Chapter 3. Validating your approach.mp416218645348.717279372236125h264aacmov1280x72030mp42023-09-06 18:49:39
-022. Chapter 3. Classifying newswires a multiclass classification example.mp429101920634.276281367231125h264aacmov1280x72030mp42023-09-06 18:49:43
-023. Chapter 3. Predicting house prices a regression example.mp427675658621.319546356221125h264aacmov1280x72030mp42023-09-06 18:49:46
-024. Chapter 4. Fundamentals of machine learning.mp434247491620.948027441306125h264aacmov1280x72030mp42023-09-06 18:49:50
-025. Chapter 4. Evaluating machine-learning models.mp428021849524.074376427292125h264aacmov1280x72030mp42023-09-06 18:49:54
-026. Chapter 4. Data preprocessing, feature engineering, and feature learning.mp425366762507.890068399264125h264aacmov1280x72030mp42023-09-06 18:49:57
-027. Chapter 4. Overfitting and underfitting.mp421480115418.237823410275125h264aacmov1280x72030mp42023-09-06 18:50:00
-028. Chapter 4. Adding weight regularization.mp419302677392.951293392257125h264aacmov1280x72030mp42023-09-06 18:50:03
-029. Chapter 4. The universal workflow of machine learning.mp421322247409.34458416281125h264aacmov1280x72030mp42023-09-06 18:50:05
-030. Chapter 4. Developing a model that does better than a baseline.mp424389564452.371156431296125h264aacmov1280x72030mp42023-09-06 18:50:09

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