Download Packt | Master Deep Learning with TensorFlow 2.0 in Python [2019] [FCO] Torrent

Packt | Master Deep Learning with TensorFlow 2.0 in Python [2019] [FCO]

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01.Welcome! Course introduction

	0101.Meet your instructors and why you should study machine learning.mp4	MP4	84.75 MB
	0102.What does the course cover.mp4	MP4	39.08 MB

02.Introduction to neural networks

0201.Introduction to neural networks.mp4	MP4	45.75 MB
0202.Training the model.mp4	MP4	26.82 MB
0203.Types of machine learning.mp4	MP4	40.85 MB
0204.The linear model.mp4	MP4	26.04 MB
0205.The linear model. Multiple inputs.mp4	MP4	23.69 MB
0206.The linear model. Multiple inputs and multiple outputs.mp4	MP4	42.21 MB
0207.Graphical representation.mp4	MP4	21.96 MB
0208.The objective function.mp4	MP4	17.7 MB
0209.L2-norm loss.mp4	MP4	21.4 MB
0210.Cross-entropy loss.mp4	MP4	33.4 MB
0211.One parameter gradient descent.mp4	MP4	56.41 MB
0212.N-parameter gradient descent.mp4	MP4	57.61 MB

03.Setting up the working environment

0301.Setting up the environment - An introduction - Do not skip, please!.mp4	MP4	6.91 MB
0302.Why Python and why Jupyter.mp4	MP4	34.69 MB
0303.Installing Anaconda.mp4	MP4	31.33 MB
0304.The Jupyter dashboard - part 1.mp4	MP4	9.24 MB
0305.The Jupyter dashboard - part 2.mp4	MP4	20.37 MB
0306.Installing TensorFlow 2.mp4	MP4	51.17 MB

04.Minimal example - your first machine learning algorithm

05.TensorFlow - An introduction

0501.TensorFlow outline.mp4	MP4	41.97 MB
0502.TensorFlow 2 intro.mp4	MP4	37.84 MB
0503.A Note on Coding in TensorFlow.mp4	MP4	8.14 MB
0504.Types of file formats in TensorFlow and data handling.mp4	MP4	13.28 MB
0505.Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4	MP4	32.94 MB
0506.Interpreting the result and extracting the weights and bias.mp4	MP4	31.38 MB
0507.Customizing your model.mp4	MP4	21.62 MB

06.Going deeper Introduction to deep neural networks

0601.Layers.mp4	MP4	20.55 MB
0602.What is a deep net.mp4	MP4	32.6 MB
0603.Understanding deep nets in depth.mp4	MP4	58.18 MB
0604.Why do we need non-linearities.mp4	MP4	37.97 MB
0605.Activation functions.mp4	MP4	37.97 MB
0606.Softmax activation.mp4	MP4	24.98 MB
0607.Backpropagation.mp4	MP4	52.73 MB
0608.Backpropagation - visual representation.mp4	MP4	24.39 MB

07.Overfitting

0701.Underfitting and overfitting.mp4	MP4	34.06 MB
0702.Underfitting and overfitting - classification.mp4	MP4	32.48 MB
0703.Training and validation.mp4	MP4	37.52 MB
0704.Training, validation, and test.mp4	MP4	31.32 MB
0705.N-fold cross validation.mp4	MP4	25.57 MB
0706.Early stopping.mp4	MP4	28.33 MB

08.Initialization

0801.Initialization - Introduction.mp4	MP4	26.17 MB
0802.Types of simple initializations.mp4	MP4	12.29 MB
0803.Xavier initialization.mp4	MP4	19.12 MB

09.Gradient descent and learning rates

0901.Stochastic gradient descent.mp4	MP4	34.48 MB
0902.Gradient descent pitfalls.mp4	MP4	14.35 MB
0903.Momentum.mp4	MP4	18.96 MB
0904.Learning rate schedules.mp4	MP4	37.08 MB
0905.Learning rate schedules. A picture.mp4	MP4	10.93 MB
0906.Adaptive learning rate schedules.mp4	MP4	29.83 MB
0907.Adaptive moment estimation.mp4	MP4	29.08 MB

10.Preprocessing

1001.Preprocessing introduction.mp4	MP4	25.55 MB
1002.Basic preprocessing.mp4	MP4	11.11 MB
1003.Standardization.mp4	MP4	40.37 MB
1004.Dealing with categorical data.mp4	MP4	18.22 MB
1005.One-hot and binary encoding.mp4	MP4	32.26 MB

11.The MNIST example

1101.The dataset.mp4	MP4	20.74 MB
1102.How to tackle the MNIST.mp4	MP4	33.29 MB
1103.Importing the relevant packages and load the data.mp4	MP4	15.85 MB
1104.Preprocess the data - create a validation dataset and scale the data.mp4	MP4	27.05 MB
1105.Preprocess the data - shuffle and batch the data.mp4	MP4	36.58 MB
1106.Outline the model.mp4	MP4	27.36 MB
1107.Select the loss and the optimizer.mp4	MP4	12.71 MB
1108.Learning.mp4	MP4	20.43 MB
1109.Testing the model.mp4	MP4	15.26 MB

12.Business case

1201.Exploring the dataset and identifying predictors.mp4	MP4	30.16 MB
1202.Outlining the business case solution.mp4	MP4	9.52 MB
1203.Balancing the dataset.mp4	MP4	13.75 MB
1204.Preprocessing the data.mp4	MP4	44.52 MB
1205.Load the preprocessed data.mp4	MP4	18.22 MB
1206.Learning and interpreting the result.mp4	MP4	26.4 MB
1207.Setting an early stopping mechanism.mp4	MP4	21.45 MB
1208.Testing the model.mp4	MP4	9.63 MB

13.Conclusion

1301.See how much you have learned.mp4	MP4	38.88 MB
1302.What's further out there in the machine and deep learning world.mp4	MP4	17.51 MB
1303.An overview of CNNs.mp4	MP4	18.62 MB
1304.An overview of RNNs.mp4	MP4	27.42 MB
1305.An overview of non-NN approaches.mp4	MP4	40.17 MB

Exercise Files

exercise_files.zip

ZIP

1.37 MB

Packt | Master Deep Learning with TensorFlow 2.0 in Python [2019] [FCO]

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