FileMood

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

FreeCoursesOnline Me PacktPub Master Deep Learning with TensorFlow in Python 2019 Video

Name

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

  DOWNLOAD Copy Link

Trouble downloading? see How To

Total Size

2.5 GB

Total Files

89

Last Seen

2025-04-06 23:39

Hash

8EF76CBAB81900D4D663641CB4F159B74FBCB062

/0. Websites you may like/

0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url

0.4 KB

1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url

0.3 KB

2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url

0.3 KB

3. (NulledPremium.com) Download E-Learning, E-Books, Audio-Books, Comics, Articles and more... etc.url

0.2 KB

4. (FTUApps.com) Download Cracked Developers Applications For Free.url

0.2 KB

How you can help Team-FTU.txt

0.2 KB

/01.Welcome! Course introduction/

0101.Meet your instructors and why you should study machine learning.mp4

88.9 MB

0102.What does the course cover.mp4

41.0 MB

/02.Introduction to neural networks/

0201.Introduction to neural networks.mp4

48.0 MB

0202.Training the model.mp4

28.1 MB

0203.Types of machine learning.mp4

42.8 MB

0204.The linear model.mp4

27.3 MB

0205.The linear model. Multiple inputs.mp4

24.8 MB

0206.The linear model. Multiple inputs and multiple outputs.mp4

44.3 MB

0207.Graphical representation.mp4

23.0 MB

0208.The objective function.mp4

18.6 MB

0209.L2-norm loss.mp4

22.4 MB

0210.Cross-entropy loss.mp4

35.0 MB

0211.One parameter gradient descent.mp4

59.1 MB

0212.N-parameter gradient descent.mp4

60.4 MB

/03.Setting up the working environment/

0301.Setting up the environment - An introduction - Do not skip, please!.mp4

7.2 MB

0302.Why Python and why Jupyter.mp4

36.4 MB

0303.Installing Anaconda.mp4

32.9 MB

0304.The Jupyter dashboard - part 1.mp4

9.7 MB

0305.The Jupyter dashboard - part 2.mp4

21.4 MB

0306.Installing TensorFlow 2.mp4

53.7 MB

/04.Minimal example - your first machine learning algorithm/

0401.Minimal example - part 1.mp4

38.1 MB

0402.Minimal example - part 2.mp4

24.9 MB

0403.Minimal example - part 3.mp4

21.4 MB

0404.Minimal example - part 4.mp4

31.9 MB

/05.TensorFlow - An introduction/

0501.TensorFlow outline.mp4

44.0 MB

0502.TensorFlow 2 intro.mp4

39.7 MB

0503.A Note on Coding in TensorFlow.mp4

8.5 MB

0504.Types of file formats in TensorFlow and data handling.mp4

13.9 MB

0505.Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4

34.5 MB

0506.Interpreting the result and extracting the weights and bias.mp4

32.9 MB

0507.Customizing your model.mp4

22.7 MB

/06.Going deeper Introduction to deep neural networks/

0601.Layers.mp4

21.5 MB

0602.What is a deep net.mp4

34.2 MB

0603.Understanding deep nets in depth.mp4

61.0 MB

0604.Why do we need non-linearities.mp4

39.8 MB

0605.Activation functions.mp4

39.8 MB

0606.Softmax activation.mp4

26.2 MB

0607.Backpropagation.mp4

55.3 MB

0608.Backpropagation - visual representation.mp4

25.6 MB

/07.Overfitting/

0701.Underfitting and overfitting.mp4

35.7 MB

0702.Underfitting and overfitting - classification.mp4

34.1 MB

0703.Training and validation.mp4

39.3 MB

0704.Training, validation, and test.mp4

32.8 MB

0705.N-fold cross validation.mp4

26.8 MB

0706.Early stopping.mp4

29.7 MB

/08.Initialization/

0801.Initialization - Introduction.mp4

27.4 MB

0802.Types of simple initializations.mp4

12.9 MB

0803.Xavier initialization.mp4

20.1 MB

/09.Gradient descent and learning rates/

0901.Stochastic gradient descent.mp4

36.2 MB

0902.Gradient descent pitfalls.mp4

15.0 MB

0903.Momentum.mp4

19.9 MB

0904.Learning rate schedules.mp4

38.9 MB

0905.Learning rate schedules. A picture.mp4

11.5 MB

0906.Adaptive learning rate schedules.mp4

31.3 MB

0907.Adaptive moment estimation.mp4

30.5 MB

/10.Preprocessing/

1001.Preprocessing introduction.mp4

26.8 MB

1002.Basic preprocessing.mp4

11.6 MB

1003.Standardization.mp4

42.3 MB

1004.Dealing with categorical data.mp4

19.1 MB

1005.One-hot and binary encoding.mp4

33.8 MB

/11.The MNIST example/

1101.The dataset.mp4

21.8 MB

1102.How to tackle the MNIST.mp4

34.9 MB

1103.Importing the relevant packages and load the data.mp4

16.6 MB

1104.Preprocess the data - create a validation dataset and scale the data.mp4

28.4 MB

1105.Preprocess the data - shuffle and batch the data.mp4

38.4 MB

1106.Outline the model.mp4

28.7 MB

1107.Select the loss and the optimizer.mp4

13.3 MB

1108.Learning.mp4

21.4 MB

1109.Testing the model.mp4

16.0 MB

/12.Business case/

1201.Exploring the dataset and identifying predictors.mp4

31.6 MB

1202.Outlining the business case solution.mp4

10.0 MB

1203.Balancing the dataset.mp4

14.4 MB

1204.Preprocessing the data.mp4

46.7 MB

1205.Load the preprocessed data.mp4

19.1 MB

1206.Learning and interpreting the result.mp4

27.7 MB

1207.Setting an early stopping mechanism.mp4

22.5 MB

1208.Testing the model.mp4

10.1 MB

/13.Conclusion/

1301.See how much you have learned.mp4

40.8 MB

1302.What's further out there in the machine and deep learning world.mp4

18.4 MB

1303.An overview of CNNs.mp4

19.5 MB

1304.An overview of RNNs.mp4

28.7 MB

1305.An overview of non-NN approaches.mp4

42.1 MB

/Exercise Files/

exercise_files.zip

1.4 MB

 

Total files 89


Copyright © 2025 FileMood.com