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

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UpdateDate 2024-9-16

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Files 0. Websites you may like/0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url | 377B 0. Websites you may like/1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url | 328B 0. Websites you may like/2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url | 286B 0. Websites you may like/3. (NulledPremium.com) Download E-Learning, E-Books, Audio-Books, Comics, Articles and more... etc.url | 163B 0. Websites you may like/4. (FTUApps.com) Download Cracked Developers Applications For Free.url | 239B 0. Websites you may like/How you can help Team-FTU.txt | 237B 01.Welcome! Course introduction/0101.Meet your instructors and why you should study machine learning.mp4 | 84.75MB 01.Welcome! Course introduction/0102.What does the course cover.mp4 | 39.08MB 02.Introduction to neural networks/0201.Introduction to neural networks.mp4 | 45.75MB 02.Introduction to neural networks/0202.Training the model.mp4 | 26.82MB 02.Introduction to neural networks/0203.Types of machine learning.mp4 | 40.85MB 02.Introduction to neural networks/0204.The linear model.mp4 | 26.04MB 02.Introduction to neural networks/0205.The linear model. Multiple inputs.mp4 | 23.69MB 02.Introduction to neural networks/0206.The linear model. Multiple inputs and multiple outputs.mp4 | 42.21MB 02.Introduction to neural networks/0207.Graphical representation.mp4 | 21.96MB 02.Introduction to neural networks/0208.The objective function.mp4 | 17.70MB 02.Introduction to neural networks/0209.L2-norm loss.mp4 | 21.40MB 02.Introduction to neural networks/0210.Cross-entropy loss.mp4 | 33.40MB 02.Introduction to neural networks/0211.One parameter gradient descent.mp4 | 56.41MB 02.Introduction to neural networks/0212.N-parameter gradient descent.mp4 | 57.61MB 03.Setting up the working environment/0301.Setting up the environment - An introduction - Do not skip, please!.mp4 | 6.91MB 03.Setting up the working environment/0302.Why Python and why Jupyter.mp4 | 34.69MB 03.Setting up the working environment/0303.Installing Anaconda.mp4 | 31.33MB 03.Setting up the working environment/0304.The Jupyter dashboard - part 1.mp4 | 9.24MB 03.Setting up the working environment/0305.The Jupyter dashboard - part 2.mp4 | 20.37MB 03.Setting up the working environment/0306.Installing TensorFlow 2.mp4 | 51.17MB 04.Minimal example - your first machine learning algorithm/0401.Minimal example - part 1.mp4 | 36.36MB 04.Minimal example - your first machine learning algorithm/0402.Minimal example - part 2.mp4 | 23.74MB 04.Minimal example - your first machine learning algorithm/0403.Minimal example - part 3.mp4 | 20.43MB 04.Minimal example - your first machine learning algorithm/0404.Minimal example - part 4.mp4 | 30.41MB 05.TensorFlow - An introduction/0501.TensorFlow outline.mp4 | 41.97MB 05.TensorFlow - An introduction/0502.TensorFlow 2 intro.mp4 | 37.84MB 05.TensorFlow - An introduction/0503.A Note on Coding in TensorFlow.mp4 | 8.14MB 05.TensorFlow - An introduction/0504.Types of file formats in TensorFlow and data handling.mp4 | 13.28MB 05.TensorFlow - An introduction/0505.Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 | 32.94MB 05.TensorFlow - An introduction/0506.Interpreting the result and extracting the weights and bias.mp4 | 31.38MB 05.TensorFlow - An introduction/0507.Customizing your model.mp4 | 21.62MB 06.Going deeper Introduction to deep neural networks/0601.Layers.mp4 | 20.55MB 06.Going deeper Introduction to deep neural networks/0602.What is a deep net.mp4 | 32.60MB 06.Going deeper Introduction to deep neural networks/0603.Understanding deep nets in depth.mp4 | 58.18MB 06.Going deeper Introduction to deep neural networks/0604.Why do we need non-linearities.mp4 | 37.97MB 06.Going deeper Introduction to deep neural networks/0605.Activation functions.mp4 | 37.97MB 06.Going deeper Introduction to deep neural networks/0606.Softmax activation.mp4 | 24.98MB 06.Going deeper Introduction to deep neural networks/0607.Backpropagation.mp4 | 52.73MB 06.Going deeper Introduction to deep neural networks/0608.Backpropagation - visual representation.mp4 | 24.39MB 07.Overfitting/0701.Underfitting and overfitting.mp4 | 34.06MB 07.Overfitting/0702.Underfitting and overfitting - classification.mp4 | 32.48MB 07.Overfitting/0703.Training and validation.mp4 | 37.52MB 07.Overfitting/0704.Training, validation, and test.mp4 | 31.32MB 07.Overfitting/0705.N-fold cross validation.mp4 | 25.57MB 07.Overfitting/0706.Early stopping.mp4 | 28.33MB 08.Initialization/0801.Initialization - Introduction.mp4 | 26.17MB 08.Initialization/0802.Types of simple initializations.mp4 | 12.29MB 08.Initialization/0803.Xavier initialization.mp4 | 19.12MB 09.Gradient descent and learning rates/0901.Stochastic gradient descent.mp4 | 34.48MB 09.Gradient descent and learning rates/0902.Gradient descent pitfalls.mp4 | 14.35MB 09.Gradient descent and learning rates/0903.Momentum.mp4 | 18.96MB 09.Gradient descent and learning rates/0904.Learning rate schedules.mp4 | 37.08MB 09.Gradient descent and learning rates/0905.Learning rate schedules. A picture.mp4 | 10.93MB 09.Gradient descent and learning rates/0906.Adaptive learning rate schedules.mp4 | 29.83MB 09.Gradient descent and learning rates/0907.Adaptive moment estimation.mp4 | 29.08MB 10.Preprocessing/1001.Preprocessing introduction.mp4 | 25.55MB 10.Preprocessing/1002.Basic preprocessing.mp4 | 11.11MB 10.Preprocessing/1003.Standardization.mp4 | 40.37MB 10.Preprocessing/1004.Dealing with categorical data.mp4 | 18.22MB 10.Preprocessing/1005.One-hot and binary encoding.mp4 | 32.26MB 11.The MNIST example/1101.The dataset.mp4 | 20.74MB 11.The MNIST example/1102.How to tackle the MNIST.mp4 | 33.29MB 11.The MNIST example/1103.Importing the relevant packages and load the data.mp4 | 15.85MB 11.The MNIST example/1104.Preprocess the data - create a validation dataset and scale the data.mp4 | 27.05MB 11.The MNIST example/1105.Preprocess the data - shuffle and batch the data.mp4 | 36.58MB 11.The MNIST example/1106.Outline the model.mp4 | 27.36MB 11.The MNIST example/1107.Select the loss and the optimizer.mp4 | 12.71MB 11.The MNIST example/1108.Learning.mp4 | 20.43MB 11.The MNIST example/1109.Testing the model.mp4 | 15.26MB 12.Business case/1201.Exploring the dataset and identifying predictors.mp4 | 30.16MB 12.Business case/1202.Outlining the business case solution.mp4 | 9.52MB 12.Business case/1203.Balancing the dataset.mp4 | 13.75MB 12.Business case/1204.Preprocessing the data.mp4 | 44.52MB 12.Business case/1205.Load the preprocessed data.mp4 | 18.22MB 12.Business case/1206.Learning and interpreting the result.mp4 | 26.40MB 12.Business case/1207.Setting an early stopping mechanism.mp4 | 21.45MB 12.Business case/1208.Testing the model.mp4 | 9.63MB 13.Conclusion/1301.See how much you have learned.mp4 | 38.88MB 13.Conclusion/1302.What's further out there in the machine and deep learning world.mp4 | 17.51MB 13.Conclusion/1303.An overview of CNNs.mp4 | 18.62MB 13.Conclusion/1304.An overview of RNNs.mp4 | 27.42MB 13.Conclusion/1305.An overview of non-NN approaches.mp4 | 40.17MB Exercise Files/exercise_files.zip | 1.37MB