[FreeCoursesOnline.Me] PacktPub - Master Deep Learning with TensorFlow 2.0 in Python [2019] [Video]

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[FreeCoursesOnline.Me] PacktPub - Master Deep Learning with TensorFlow 2.0 in Python [2019] [Video]

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种子哈希：8ef76cbab81900d4d663641cb4f159b74fbcb062
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最近下载：2025-02-16

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文件列表

  • 01.Welcome! Course introduction/0101.Meet your instructors and why you should study machine learning.mp4 88.9 MB
  • 06.Going deeper Introduction to deep neural networks/0603.Understanding deep nets in depth.mp4 61.0 MB
  • 02.Introduction to neural networks/0212.N-parameter gradient descent.mp4 60.4 MB
  • 02.Introduction to neural networks/0211.One parameter gradient descent.mp4 59.1 MB
  • 06.Going deeper Introduction to deep neural networks/0607.Backpropagation.mp4 55.3 MB
  • 03.Setting up the working environment/0306.Installing TensorFlow 2.mp4 53.7 MB
  • 02.Introduction to neural networks/0201.Introduction to neural networks.mp4 48.0 MB
  • 12.Business case/1204.Preprocessing the data.mp4 46.7 MB
  • 02.Introduction to neural networks/0206.The linear model. Multiple inputs and multiple outputs.mp4 44.3 MB
  • 05.TensorFlow - An introduction/0501.TensorFlow outline.mp4 44.0 MB
  • 02.Introduction to neural networks/0203.Types of machine learning.mp4 42.8 MB
  • 10.Preprocessing/1003.Standardization.mp4 42.3 MB
  • 13.Conclusion/1305.An overview of non-NN approaches.mp4 42.1 MB
  • 01.Welcome! Course introduction/0102.What does the course cover.mp4 41.0 MB
  • 13.Conclusion/1301.See how much you have learned.mp4 40.8 MB
  • 06.Going deeper Introduction to deep neural networks/0604.Why do we need non-linearities.mp4 39.8 MB
  • 06.Going deeper Introduction to deep neural networks/0605.Activation functions.mp4 39.8 MB
  • 05.TensorFlow - An introduction/0502.TensorFlow 2 intro.mp4 39.7 MB
  • 07.Overfitting/0703.Training and validation.mp4 39.3 MB
  • 09.Gradient descent and learning rates/0904.Learning rate schedules.mp4 38.9 MB
  • 11.The MNIST example/1105.Preprocess the data - shuffle and batch the data.mp4 38.4 MB
  • 04.Minimal example - your first machine learning algorithm/0401.Minimal example - part 1.mp4 38.1 MB
  • 03.Setting up the working environment/0302.Why Python and why Jupyter.mp4 36.4 MB
  • 09.Gradient descent and learning rates/0901.Stochastic gradient descent.mp4 36.2 MB
  • 07.Overfitting/0701.Underfitting and overfitting.mp4 35.7 MB
  • 02.Introduction to neural networks/0210.Cross-entropy loss.mp4 35.0 MB
  • 11.The MNIST example/1102.How to tackle the MNIST.mp4 34.9 MB
  • 05.TensorFlow - An introduction/0505.Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 34.5 MB
  • 06.Going deeper Introduction to deep neural networks/0602.What is a deep net.mp4 34.2 MB
  • 07.Overfitting/0702.Underfitting and overfitting - classification.mp4 34.1 MB
  • 10.Preprocessing/1005.One-hot and binary encoding.mp4 33.8 MB
  • 05.TensorFlow - An introduction/0506.Interpreting the result and extracting the weights and bias.mp4 32.9 MB
  • 03.Setting up the working environment/0303.Installing Anaconda.mp4 32.9 MB
  • 07.Overfitting/0704.Training, validation, and test.mp4 32.8 MB
  • 04.Minimal example - your first machine learning algorithm/0404.Minimal example - part 4.mp4 31.9 MB
  • 12.Business case/1201.Exploring the dataset and identifying predictors.mp4 31.6 MB
  • 09.Gradient descent and learning rates/0906.Adaptive learning rate schedules.mp4 31.3 MB
  • 09.Gradient descent and learning rates/0907.Adaptive moment estimation.mp4 30.5 MB
  • 07.Overfitting/0706.Early stopping.mp4 29.7 MB
  • 13.Conclusion/1304.An overview of RNNs.mp4 28.7 MB
  • 11.The MNIST example/1106.Outline the model.mp4 28.7 MB
  • 11.The MNIST example/1104.Preprocess the data - create a validation dataset and scale the data.mp4 28.4 MB
  • 02.Introduction to neural networks/0202.Training the model.mp4 28.1 MB
  • 12.Business case/1206.Learning and interpreting the result.mp4 27.7 MB
  • 08.Initialization/0801.Initialization - Introduction.mp4 27.4 MB
  • 02.Introduction to neural networks/0204.The linear model.mp4 27.3 MB
  • 07.Overfitting/0705.N-fold cross validation.mp4 26.8 MB
  • 10.Preprocessing/1001.Preprocessing introduction.mp4 26.8 MB
  • 06.Going deeper Introduction to deep neural networks/0606.Softmax activation.mp4 26.2 MB
  • 06.Going deeper Introduction to deep neural networks/0608.Backpropagation - visual representation.mp4 25.6 MB
  • 04.Minimal example - your first machine learning algorithm/0402.Minimal example - part 2.mp4 24.9 MB
  • 02.Introduction to neural networks/0205.The linear model. Multiple inputs.mp4 24.8 MB
  • 02.Introduction to neural networks/0207.Graphical representation.mp4 23.0 MB
  • 05.TensorFlow - An introduction/0507.Customizing your model.mp4 22.7 MB
  • 12.Business case/1207.Setting an early stopping mechanism.mp4 22.5 MB
  • 02.Introduction to neural networks/0209.L2-norm loss.mp4 22.4 MB
  • 11.The MNIST example/1101.The dataset.mp4 21.8 MB
  • 06.Going deeper Introduction to deep neural networks/0601.Layers.mp4 21.5 MB
  • 11.The MNIST example/1108.Learning.mp4 21.4 MB
  • 04.Minimal example - your first machine learning algorithm/0403.Minimal example - part 3.mp4 21.4 MB
  • 03.Setting up the working environment/0305.The Jupyter dashboard - part 2.mp4 21.4 MB
  • 08.Initialization/0803.Xavier initialization.mp4 20.1 MB
  • 09.Gradient descent and learning rates/0903.Momentum.mp4 19.9 MB
  • 13.Conclusion/1303.An overview of CNNs.mp4 19.5 MB
  • 10.Preprocessing/1004.Dealing with categorical data.mp4 19.1 MB
  • 12.Business case/1205.Load the preprocessed data.mp4 19.1 MB
  • 02.Introduction to neural networks/0208.The objective function.mp4 18.6 MB
  • 13.Conclusion/1302.What's further out there in the machine and deep learning world.mp4 18.4 MB
  • 11.The MNIST example/1103.Importing the relevant packages and load the data.mp4 16.6 MB
  • 11.The MNIST example/1109.Testing the model.mp4 16.0 MB
  • 09.Gradient descent and learning rates/0902.Gradient descent pitfalls.mp4 15.0 MB
  • 12.Business case/1203.Balancing the dataset.mp4 14.4 MB
  • 05.TensorFlow - An introduction/0504.Types of file formats in TensorFlow and data handling.mp4 13.9 MB
  • 11.The MNIST example/1107.Select the loss and the optimizer.mp4 13.3 MB
  • 08.Initialization/0802.Types of simple initializations.mp4 12.9 MB
  • 10.Preprocessing/1002.Basic preprocessing.mp4 11.6 MB
  • 09.Gradient descent and learning rates/0905.Learning rate schedules. A picture.mp4 11.5 MB
  • 12.Business case/1208.Testing the model.mp4 10.1 MB
  • 12.Business case/1202.Outlining the business case solution.mp4 10.0 MB
  • 03.Setting up the working environment/0304.The Jupyter dashboard - part 1.mp4 9.7 MB
  • 05.TensorFlow - An introduction/0503.A Note on Coding in TensorFlow.mp4 8.5 MB
  • 03.Setting up the working environment/0301.Setting up the environment - An introduction - Do not skip, please!.mp4 7.2 MB
  • Exercise Files/exercise_files.zip 1.4 MB
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