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种子名称：
[FreeCoursesOnline.Me] PacktPub - Master Deep Learning with TensorFlow 2.0 in Python [2019] [Video]
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视频
文件数目：
82个文件
文件大小：
2.3 GB
收录时间：
2020-6-5 18:52
已经下载：
3次
资源热度：
168
最近下载：
2024-5-24 16:05
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[FreeCoursesOnline.Me] PacktPub - Master Deep Learning with TensorFlow 2.0 in Python [2019] [Video].torrent
01.Welcome! Course introduction/0101.Meet your instructors and why you should study machine learning.mp484.75MB
01.Welcome! Course introduction/0102.What does the course cover.mp439.08MB
02.Introduction to neural networks/0201.Introduction to neural networks.mp445.75MB
02.Introduction to neural networks/0202.Training the model.mp426.82MB
02.Introduction to neural networks/0203.Types of machine learning.mp440.85MB
02.Introduction to neural networks/0204.The linear model.mp426.04MB
02.Introduction to neural networks/0205.The linear model. Multiple inputs.mp423.69MB
02.Introduction to neural networks/0206.The linear model. Multiple inputs and multiple outputs.mp442.21MB
02.Introduction to neural networks/0207.Graphical representation.mp421.96MB
02.Introduction to neural networks/0208.The objective function.mp417.7MB
02.Introduction to neural networks/0209.L2-norm loss.mp421.4MB
02.Introduction to neural networks/0210.Cross-entropy loss.mp433.4MB
02.Introduction to neural networks/0211.One parameter gradient descent.mp456.41MB
02.Introduction to neural networks/0212.N-parameter gradient descent.mp457.61MB
03.Setting up the working environment/0301.Setting up the environment - An introduction - Do not skip, please!.mp46.91MB
03.Setting up the working environment/0302.Why Python and why Jupyter.mp434.69MB
03.Setting up the working environment/0303.Installing Anaconda.mp431.33MB
03.Setting up the working environment/0304.The Jupyter dashboard - part 1.mp49.24MB
03.Setting up the working environment/0305.The Jupyter dashboard - part 2.mp420.37MB
03.Setting up the working environment/0306.Installing TensorFlow 2.mp451.17MB
04.Minimal example - your first machine learning algorithm/0401.Minimal example - part 1.mp436.36MB
04.Minimal example - your first machine learning algorithm/0402.Minimal example - part 2.mp423.74MB
04.Minimal example - your first machine learning algorithm/0403.Minimal example - part 3.mp420.43MB
04.Minimal example - your first machine learning algorithm/0404.Minimal example - part 4.mp430.41MB
05.TensorFlow - An introduction/0501.TensorFlow outline.mp441.97MB
05.TensorFlow - An introduction/0502.TensorFlow 2 intro.mp437.84MB
05.TensorFlow - An introduction/0503.A Note on Coding in TensorFlow.mp48.14MB
05.TensorFlow - An introduction/0504.Types of file formats in TensorFlow and data handling.mp413.28MB
05.TensorFlow - An introduction/0505.Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp432.94MB
05.TensorFlow - An introduction/0506.Interpreting the result and extracting the weights and bias.mp431.38MB
05.TensorFlow - An introduction/0507.Customizing your model.mp421.62MB
06.Going deeper Introduction to deep neural networks/0601.Layers.mp420.55MB
06.Going deeper Introduction to deep neural networks/0602.What is a deep net.mp432.6MB
06.Going deeper Introduction to deep neural networks/0603.Understanding deep nets in depth.mp458.18MB
06.Going deeper Introduction to deep neural networks/0604.Why do we need non-linearities.mp437.97MB
06.Going deeper Introduction to deep neural networks/0605.Activation functions.mp437.97MB
06.Going deeper Introduction to deep neural networks/0606.Softmax activation.mp424.98MB
06.Going deeper Introduction to deep neural networks/0607.Backpropagation.mp452.73MB
06.Going deeper Introduction to deep neural networks/0608.Backpropagation - visual representation.mp424.39MB
07.Overfitting/0701.Underfitting and overfitting.mp434.06MB
07.Overfitting/0702.Underfitting and overfitting - classification.mp432.48MB
07.Overfitting/0703.Training and validation.mp437.52MB
07.Overfitting/0704.Training, validation, and test.mp431.32MB
07.Overfitting/0705.N-fold cross validation.mp425.57MB
07.Overfitting/0706.Early stopping.mp428.33MB
08.Initialization/0801.Initialization - Introduction.mp426.17MB
08.Initialization/0802.Types of simple initializations.mp412.29MB
08.Initialization/0803.Xavier initialization.mp419.12MB
09.Gradient descent and learning rates/0901.Stochastic gradient descent.mp434.48MB
09.Gradient descent and learning rates/0902.Gradient descent pitfalls.mp414.35MB
09.Gradient descent and learning rates/0903.Momentum.mp418.96MB
09.Gradient descent and learning rates/0904.Learning rate schedules.mp437.08MB
09.Gradient descent and learning rates/0905.Learning rate schedules. A picture.mp410.93MB
09.Gradient descent and learning rates/0906.Adaptive learning rate schedules.mp429.83MB
09.Gradient descent and learning rates/0907.Adaptive moment estimation.mp429.08MB
10.Preprocessing/1001.Preprocessing introduction.mp425.55MB
10.Preprocessing/1002.Basic preprocessing.mp411.11MB
10.Preprocessing/1003.Standardization.mp440.37MB
10.Preprocessing/1004.Dealing with categorical data.mp418.22MB
10.Preprocessing/1005.One-hot and binary encoding.mp432.26MB
11.The MNIST example/1101.The dataset.mp420.74MB
11.The MNIST example/1102.How to tackle the MNIST.mp433.29MB
11.The MNIST example/1103.Importing the relevant packages and load the data.mp415.85MB
11.The MNIST example/1104.Preprocess the data - create a validation dataset and scale the data.mp427.05MB
11.The MNIST example/1105.Preprocess the data - shuffle and batch the data.mp436.58MB
11.The MNIST example/1106.Outline the model.mp427.36MB
11.The MNIST example/1107.Select the loss and the optimizer.mp412.71MB
11.The MNIST example/1108.Learning.mp420.43MB
11.The MNIST example/1109.Testing the model.mp415.26MB
12.Business case/1201.Exploring the dataset and identifying predictors.mp430.16MB
12.Business case/1202.Outlining the business case solution.mp49.52MB
12.Business case/1203.Balancing the dataset.mp413.75MB
12.Business case/1204.Preprocessing the data.mp444.52MB
12.Business case/1205.Load the preprocessed data.mp418.22MB
12.Business case/1206.Learning and interpreting the result.mp426.4MB
12.Business case/1207.Setting an early stopping mechanism.mp421.45MB
12.Business case/1208.Testing the model.mp49.63MB
13.Conclusion/1301.See how much you have learned.mp438.88MB
13.Conclusion/1302.What's further out there in the machine and deep learning world.mp417.51MB
13.Conclusion/1303.An overview of CNNs.mp418.62MB
13.Conclusion/1304.An overview of RNNs.mp427.42MB
13.Conclusion/1305.An overview of non-NN approaches.mp440.17MB
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