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

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File Name
Size
01.Welcome! Course introduction/0101.Meet your instructors and why you should study machine learning.mp4
85 MB
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237 B
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01.Welcome! Course introduction/0102.What does the course cover.mp4
39 MB
02.Introduction to neural networks/0201.Introduction to neural networks.mp4
46 MB
02.Introduction to neural networks/0202.Training the model.mp4
27 MB
02.Introduction to neural networks/0203.Types of machine learning.mp4
41 MB
02.Introduction to neural networks/0204.The linear model.mp4
26 MB
02.Introduction to neural networks/0205.The linear model. Multiple inputs.mp4
24 MB
02.Introduction to neural networks/0206.The linear model. Multiple inputs and multiple outputs.mp4
42 MB
02.Introduction to neural networks/0207.Graphical representation.mp4
22 MB
02.Introduction to neural networks/0208.The objective function.mp4
18 MB
02.Introduction to neural networks/0209.L2-norm loss.mp4
21 MB
02.Introduction to neural networks/0210.Cross-entropy loss.mp4
33 MB
02.Introduction to neural networks/0211.One parameter gradient descent.mp4
56 MB
02.Introduction to neural networks/0212.N-parameter gradient descent.mp4
58 MB
03.Setting up the working environment/0301.Setting up the environment - An introduction - Do not skip, please!.mp4
6.9 MB
03.Setting up the working environment/0302.Why Python and why Jupyter.mp4
35 MB
03.Setting up the working environment/0303.Installing Anaconda.mp4
31 MB
03.Setting up the working environment/0304.The Jupyter dashboard - part 1.mp4
9.2 MB
03.Setting up the working environment/0305.The Jupyter dashboard - part 2.mp4
20 MB
03.Setting up the working environment/0306.Installing TensorFlow 2.mp4
51 MB
04.Minimal example - your first machine learning algorithm/0401.Minimal example - part 1.mp4
36 MB
04.Minimal example - your first machine learning algorithm/0402.Minimal example - part 2.mp4
24 MB
04.Minimal example - your first machine learning algorithm/0403.Minimal example - part 3.mp4
20 MB
04.Minimal example - your first machine learning algorithm/0404.Minimal example - part 4.mp4
30 MB
05.TensorFlow - An introduction/0501.TensorFlow outline.mp4
42 MB
05.TensorFlow - An introduction/0502.TensorFlow 2 intro.mp4
38 MB
05.TensorFlow - An introduction/0503.A Note on Coding in TensorFlow.mp4
8.1 MB
05.TensorFlow - An introduction/0504.Types of file formats in TensorFlow and data handling.mp4
13 MB
05.TensorFlow - An introduction/0505.Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4
33 MB
05.TensorFlow - An introduction/0506.Interpreting the result and extracting the weights and bias.mp4
31 MB
05.TensorFlow - An introduction/0507.Customizing your model.mp4
22 MB
06.Going deeper Introduction to deep neural networks/0601.Layers.mp4
20 MB
06.Going deeper Introduction to deep neural networks/0602.What is a deep net.mp4
33 MB
06.Going deeper Introduction to deep neural networks/0603.Understanding deep nets in depth.mp4
58 MB
06.Going deeper Introduction to deep neural networks/0604.Why do we need non-linearities.mp4
38 MB
06.Going deeper Introduction to deep neural networks/0605.Activation functions.mp4
38 MB
06.Going deeper Introduction to deep neural networks/0606.Softmax activation.mp4
25 MB
06.Going deeper Introduction to deep neural networks/0607.Backpropagation.mp4
53 MB
06.Going deeper Introduction to deep neural networks/0608.Backpropagation - visual representation.mp4
24 MB
07.Overfitting/0701.Underfitting and overfitting.mp4
34 MB
07.Overfitting/0702.Underfitting and overfitting - classification.mp4
32 MB
07.Overfitting/0703.Training and validation.mp4
38 MB
07.Overfitting/0704.Training, validation, and test.mp4
31 MB
07.Overfitting/0705.N-fold cross validation.mp4
26 MB
07.Overfitting/0706.Early stopping.mp4
28 MB
08.Initialization/0801.Initialization - Introduction.mp4
26 MB
08.Initialization/0802.Types of simple initializations.mp4
12 MB
08.Initialization/0803.Xavier initialization.mp4
19 MB
09.Gradient descent and learning rates/0901.Stochastic gradient descent.mp4
34 MB
09.Gradient descent and learning rates/0902.Gradient descent pitfalls.mp4
14 MB
09.Gradient descent and learning rates/0903.Momentum.mp4
19 MB
09.Gradient descent and learning rates/0904.Learning rate schedules.mp4
37 MB
09.Gradient descent and learning rates/0905.Learning rate schedules. A picture.mp4
11 MB
09.Gradient descent and learning rates/0906.Adaptive learning rate schedules.mp4
30 MB
09.Gradient descent and learning rates/0907.Adaptive moment estimation.mp4
29 MB
10.Preprocessing/1001.Preprocessing introduction.mp4
26 MB
10.Preprocessing/1002.Basic preprocessing.mp4
11 MB
10.Preprocessing/1003.Standardization.mp4
40 MB
10.Preprocessing/1004.Dealing with categorical data.mp4
18 MB
10.Preprocessing/1005.One-hot and binary encoding.mp4
32 MB
11.The MNIST example/1101.The dataset.mp4
21 MB
11.The MNIST example/1102.How to tackle the MNIST.mp4
33 MB
11.The MNIST example/1103.Importing the relevant packages and load the data.mp4
16 MB
11.The MNIST example/1104.Preprocess the data - create a validation dataset and scale the data.mp4
27 MB
11.The MNIST example/1105.Preprocess the data - shuffle and batch the data.mp4
37 MB
11.The MNIST example/1106.Outline the model.mp4
27 MB
11.The MNIST example/1107.Select the loss and the optimizer.mp4
13 MB
11.The MNIST example/1108.Learning.mp4
20 MB
11.The MNIST example/1109.Testing the model.mp4
15 MB
12.Business case/1201.Exploring the dataset and identifying predictors.mp4
30 MB
12.Business case/1202.Outlining the business case solution.mp4
9.5 MB
12.Business case/1203.Balancing the dataset.mp4
14 MB
12.Business case/1204.Preprocessing the data.mp4
44 MB
12.Business case/1205.Load the preprocessed data.mp4
18 MB
12.Business case/1206.Learning and interpreting the result.mp4
26 MB
12.Business case/1207.Setting an early stopping mechanism.mp4
22 MB
12.Business case/1208.Testing the model.mp4
9.6 MB
13.Conclusion/1301.See how much you have learned.mp4
39 MB
13.Conclusion/1302.What's further out there in the machine and deep learning world.mp4
18 MB
13.Conclusion/1303.An overview of CNNs.mp4
19 MB
13.Conclusion/1304.An overview of RNNs.mp4
27 MB
13.Conclusion/1305.An overview of non-NN approaches.mp4
40 MB
Exercise Files/exercise_files.zip
1.4 MB