Download Udemy - Deep Learning with TensorFlow 2.0 [2020] Torrent

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Udemy - Deep Learning with TensorFlow 2.0 [2020]

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[TutsNode.com] - Deep Learning with TensorFlow 2.0

01 Welcome! Course introduction

001 Meet your instructors and why you should study machine learning_.en.srt	SRT	10.53 KB
001 Meet your instructors and why you should study machine learning_.mp4	MP4	105.78 MB
002 What does the course cover_.en.srt	SRT	6.45 KB
002 What does the course cover_.mp4	MP4	16.35 MB
003 Download All Resources and Important FAQ.html	HTML	1.75 KB

02 Introduction to neural networks

004 Course-Notes-Section-2.pdf	PDF	927.67 KB
004 Introduction to neural networks.en.srt	SRT	6.16 KB
004 Introduction to neural networks.mp4	MP4	13.55 MB
005 Course-Notes-Section-2.pdf	PDF	927.67 KB
005 Training the model.en.srt	SRT	4.47 KB
005 Training the model.mp4	MP4	8.81 MB
006 Course-Notes-Section-2.pdf	PDF	927.67 KB
006 Types of machine learning.en.srt	SRT	5.47 KB
006 Types of machine learning.mp4	MP4	12.2 MB
007 Course-Notes-Section-2.pdf	PDF	927.67 KB
007 The linear model.en.srt	SRT	4.07 KB
007 The linear model.mp4	MP4	9.12 MB
008 Need Help with Linear Algebra_.html	HTML	1.69 KB
009 Course-Notes-Section-2.pdf	PDF	927.67 KB
009 The linear model. Multiple inputs.en.srt	SRT	3.21 KB
009 The linear model. Multiple inputs.mp4	MP4	7.49 MB
010 Course-Notes-Section-2.pdf	PDF	927.67 KB
010 The linear model. Multiple inputs and multiple outputs.en.srt	SRT	5.67 KB
010 The linear model. Multiple inputs and multiple outputs.mp4	MP4	38.28 MB
011 Course-Notes-Section-2.pdf	PDF	927.67 KB
011 Graphical representation.en.srt	SRT	2.8 KB
011 Graphical representation.mp4	MP4	6.34 MB
012 Course-Notes-Section-2.pdf	PDF	927.67 KB
012 The objective function.en.srt	SRT	2.11 KB
012 The objective function.mp4	MP4	5.71 MB
013 Course-Notes-Section-2.pdf	PDF	927.67 KB
013 L2-norm loss.en.srt	SRT	2.91 KB
013 L2-norm loss.mp4	MP4	7.26 MB
014 Course-Notes-Section-2.pdf	PDF	927.67 KB
014 Cross-entropy loss.en.srt	SRT	5.55 KB
014 Cross-entropy loss.mp4	MP4	11.35 MB
015 Course-Notes-Section-2.pdf	PDF	927.67 KB
015 GD-function-example.xlsx	XLSX	42.33 KB
015 One parameter gradient descent.en.srt	SRT	8.79 KB
015 One parameter gradient descent.mp4	MP4	17.76 MB
016 Course-Notes-Section-2.pdf	PDF	927.67 KB
016 N-parameter gradient descent.en.srt	SRT	7.81 KB
016 N-parameter gradient descent.mp4	MP4	39.45 MB

03 Setting up the working environment

017 Setting up the environment - An introduction - Do not skip, please!.en.srt	SRT	1.44 KB
017 Setting up the environment - An introduction - Do not skip, please!.mp4	MP4	5.95 MB
018 Why Python and why Jupyter_.en.srt	SRT	6.53 KB
018 Why Python and why Jupyter_.mp4	MP4	32.06 MB
019 Installing Anaconda.en.srt	SRT	4.76 KB
019 Installing Anaconda.mp4	MP4	28.38 MB
020 The Jupyter dashboard - part 1.en.srt	SRT	3.27 KB
020 The Jupyter dashboard - part 1.mp4	MP4	8.7 MB
021 The Jupyter dashboard - part 2.en.srt	SRT	7.04 KB
021 The Jupyter dashboard - part 2.mp4	MP4	18.8 MB
022 Jupyter Shortcuts.html	HTML	1.2 KB
022 Shortcuts-for-Jupyter.pdf	PDF	619.17 KB
023 Installing TensorFlow 2.en.srt	SRT	6.63 KB
023 Installing TensorFlow 2.mp4	MP4	38.72 MB
024 Installing packages - exercise.html	HTML	1.08 KB
025 Installing packages - solution.html	HTML	1.14 KB

04 Minimal example - your first machine learning algorithm

026 Minimal example - part 1.en.srt	SRT	4.69 KB
026 Minimal example - part 1.mp4	MP4	6.53 MB
027 Minimal example - part 2.en.srt	SRT	7.13 KB
027 Minimal example - part 2.mp4	MP4	10.7 MB
028 Minimal example - part 3.en.srt	SRT	4.63 KB
028 Minimal example - part 3.mp4	MP4	9.76 MB
029 Minimal example - part 4.en.srt	SRT	11.29 KB
029 Minimal example - part 4.mp4	MP4	20.8 MB
030 Minimal example - Exercises.html	HTML	2.45 KB
external-assets-links.txt	TXT	1.74 KB

05 TensorFlow - An introduction

031 TensorFlow outline.en.srt	SRT	5.44 KB
031 TensorFlow outline.mp4	MP4	33.53 MB
032 TensorFlow 2 intro.en.srt	SRT	3.77 KB
032 TensorFlow 2 intro.mp4	MP4	21.98 MB
033 A Note on Coding in TensorFlow.en.srt	SRT	1.41 KB
033 A Note on Coding in TensorFlow.mp4	MP4	6.76 MB
034 Types of file formats in TensorFlow and data handling.en.srt	SRT	3.65 KB
034 Types of file formats in TensorFlow and data handling.mp4	MP4	16.4 MB
035 Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.en.srt	SRT	8.13 KB
035 Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4	MP4	34.68 MB
036 Interpreting the result and extracting the weights and bias.en.srt	SRT	6.44 KB
036 Interpreting the result and extracting the weights and bias.mp4	MP4	30.26 MB
037 Cutomizing your model.en.srt	SRT	4.27 KB
037 Cutomizing your model.mp4	MP4	22.9 MB
038 Minimal example with TensorFlow - Exercises.html	HTML	2.25 KB
external-assets-links.txt	TXT	1.42 KB

06 Going deeper_ Introduction to deep neural networks

039 Course-Notes-Section-6.pdf	PDF	936.42 KB
039 Layers.en.srt	SRT	2.51 KB
039 Layers.mp4	MP4	4.73 MB
040 Course-Notes-Section-6.pdf	PDF	936.42 KB
040 What is a deep net_.en.srt	SRT	3.41 KB
040 What is a deep net_.mp4	MP4	6.72 MB
041 Understanding deep nets in depth.en.srt	SRT	6.93 KB
041 Understanding deep nets in depth.mp4	MP4	13.4 MB
042 Why do we need non-linearities_.en.srt	SRT	3.96 KB
042 Why do we need non-linearities_.mp4	MP4	8.95 MB
043 Activation functions.en.srt	SRT	5.38 KB
043 Activation functions.mp4	MP4	8.73 MB
044 Softmax activation.en.srt	SRT	4.48 KB
044 Softmax activation.mp4	MP4	7.37 MB
045 Backpropagation.en.srt	SRT	4.56 KB
045 Backpropagation.mp4	MP4	11.05 MB
046 Backpropagation - visual representation.en.srt	SRT	4.18 KB
046 Backpropagation - visual representation.mp4	MP4	6.84 MB

07 Backpropagation. A peek into the Mathematics of Optimization

	047 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf	PDF	182.38 KB
	047 Backpropagation. A peek into the Mathematics of Optimization.html	HTML	1.43 KB

08 Overfitting

048 Underfitting and overfitting.en.srt	SRT	5.82 KB
048 Underfitting and overfitting.mp4	MP4	11.05 MB
049 Underfitting and overfitting - classification.en.srt	SRT	2.81 KB
049 Underfitting and overfitting - classification.mp4	MP4	6.76 MB
050 Training and validation.en.srt	SRT	5.06 KB
050 Training and validation.mp4	MP4	9.23 MB
051 Training, validation, and test.en.srt	SRT	3.71 KB
051 Training, validation, and test.mp4	MP4	7.44 MB
052 N-fold cross validation.en.srt	SRT	4.38 KB
052 N-fold cross validation.mp4	MP4	6.98 MB
053 Early stopping.en.srt	SRT	7.12 KB
053 Early stopping.mp4	MP4	9.43 MB

09 Initialization

054 Initialization - Introduction.en.srt	SRT	3.68 KB
054 Initialization - Introduction.mp4	MP4	8.03 MB
055 Types of simple initializations.en.srt	SRT	3.81 KB
055 Types of simple initializations.mp4	MP4	5.61 MB
056 Xavier initialization.en.srt	SRT	3.86 KB
056 Xavier initialization.mp4	MP4	5.82 MB

10 Gradient descent and learning rates

057 Stochastic gradient descent.en.srt	SRT	5.09 KB
057 Stochastic gradient descent.mp4	MP4	9.38 MB
058 Gradient descent pitfalls.en.srt	SRT	2.93 KB
058 Gradient descent pitfalls.mp4	MP4	4.3 MB
059 Momentum.en.srt	SRT	3.66 KB
059 Momentum.mp4	MP4	6.1 MB
060 Learning rate schedules.en.srt	SRT	6.21 KB
060 Learning rate schedules.mp4	MP4	10.3 MB
061 Learning rate schedules. A picture.en.srt	SRT	2.25 KB
061 Learning rate schedules. A picture.mp4	MP4	3.14 MB
062 Adaptive learning rate schedules.en.srt	SRT	5.42 KB
062 Adaptive learning rate schedules.mp4	MP4	8.86 MB
063 Adaptive moment estimation.en.srt	SRT	3.47 KB
063 Adaptive moment estimation.mp4	MP4	7.77 MB

11 Preprocessing

064 Preprocessing introduction.en.srt	SRT	4 KB
064 Preprocessing introduction.mp4	MP4	8.42 MB
065 Basic preprocessing.en.srt	SRT	1.73 KB
065 Basic preprocessing.mp4	MP4	3.65 MB
066 Standardization.en.srt	SRT	6.19 KB
066 Standardization.mp4	MP4	8.32 MB
067 Dealing with categorical data.en.srt	SRT	2.89 KB
067 Dealing with categorical data.mp4	MP4	6.07 MB
068 One-hot and binary encoding.en.srt	SRT	4.96 KB
068 One-hot and binary encoding.mp4	MP4	6.24 MB

12 The MNIST example

069 The dataset.en.srt	SRT	3.72 KB
069 The dataset.mp4	MP4	13.37 MB
070 How to tackle the MNIST.en.srt	SRT	3.64 KB
070 How to tackle the MNIST.mp4	MP4	18.67 MB
071 Importing the relevant packages and load the data.en.srt	SRT	3.2 KB
071 Importing the relevant packages and load the data.mp4	MP4	16.32 MB
072 Preprocess the data - create a validation dataset and scale the data.en.srt	SRT	6.5 KB
072 Preprocess the data - create a validation dataset and scale the data.mp4	MP4	29.05 MB
073 Preprocess the data - scale the test data.html	HTML	997 B
074 Preprocess the data - shuffle and batch the data.en.srt	SRT	9.61 KB
074 Preprocess the data - shuffle and batch the data.mp4	MP4	41.54 MB
075 Preprocess the data - shuffle and batch the data.html	HTML	1004 B
076 Outline the model.en.srt	SRT	7.49 KB
076 Outline the model.mp4	MP4	28.24 MB
077 Select the loss and the optimizer.en.srt	SRT	3.14 KB
077 Select the loss and the optimizer.mp4	MP4	13.89 MB
078 Learning.en.srt	SRT	8.27 KB
078 Learning.mp4	MP4	40.95 MB
079 MNIST - exercises.html	HTML	2.86 KB
080 MNIST - solutions.html	HTML	3 KB
081 Testing the model.en.srt	SRT	6.26 KB
081 Testing the model.mp4	MP4	29.54 MB
external-assets-links.txt	TXT	2.63 KB

13 Business case

082 Audiobooks-data.csv	CSV	625.21 KB
082 Exploring the dataset and identifying predictors.en.srt	SRT	11.08 KB
082 Exploring the dataset and identifying predictors.mp4	MP4	66.26 MB
083 Outlining the business case solution.en.srt	SRT	2.08 KB
083 Outlining the business case solution.mp4	MP4	7.31 MB
084 Balancing the dataset.en.srt	SRT	4.67 KB
084 Balancing the dataset.mp4	MP4	30.44 MB
085 Audiobooks-data.csv	CSV	625.21 KB
085 Preprocessing the data.en.srt	SRT	12.71 KB
085 Preprocessing the data.mp4	MP4	84.29 MB
086 Audiobooks-data.csv	CSV	625.21 KB
086 Preprocessing exercise.html	HTML	1.27 KB
087 Load the preprocessed data.en.srt	SRT	4.89 KB
087 Load the preprocessed data.mp4	MP4	17.56 MB
088 Load the preprocessed data - Exercise.html	HTML	991 B
089 Learning and interpreting the result.en.srt	SRT	6.53 KB
089 Learning and interpreting the result.mp4	MP4	31.15 MB
090 Setting an early stopping mechanism.en.srt	SRT	8.11 KB
090 Setting an early stopping mechanism.mp4	MP4	49.81 MB
091 Setting an early stopping mechanism - Exercise.html	HTML	1.09 KB
092 Testing the model.en.srt	SRT	2.12 KB
092 Testing the model.mp4	MP4	10.8 MB
093 Final exercise.html	HTML	1.3 KB
external-assets-links.txt	TXT	1.47 KB

14 Appendix_ Linear Algebra Fundamentals

094 What is a Matrix_.en.srt	SRT	4.51 KB
094 What is a Matrix_.mp4	MP4	33.59 MB
095 Scalars and Vectors.en.srt	SRT	3.93 KB
095 Scalars and Vectors.mp4	MP4	33.84 MB
096 Linear Algebra and Geometry.en.srt	SRT	4.27 KB
096 Linear Algebra and Geometry.mp4	MP4	49.79 MB
097 Scalars, Vectors and Matrices in Python.en.srt	SRT	6.38 KB
097 Scalars, Vectors and Matrices in Python.mp4	MP4	26.66 MB
098 Tensors.en.srt	SRT	3.75 KB
098 Tensors.mp4	MP4	22.51 MB
099 Addition and Subtraction of Matrices.en.srt	SRT	4.22 KB
099 Addition and Subtraction of Matrices.mp4	MP4	32.6 MB
100 Errors when Adding Matrices.en.srt	SRT	2.67 KB
100 Errors when Adding Matrices.mp4	MP4	11.16 MB
101 Transpose of a Matrix.en.srt	SRT	5.58 KB
101 Transpose of a Matrix.mp4	MP4	38.08 MB
102 Dot Product of Vectors.en.srt	SRT	4.45 KB
102 Dot Product of Vectors.mp4	MP4	23.98 MB
103 Dot Product of Matrices.en.srt	SRT	9.92 KB
103 Dot Product of Matrices.mp4	MP4	49.38 MB
104 Why is Linear Algebra Useful_.en.srt	SRT	12.24 KB
104 Why is Linear Algebra Useful_.mp4	MP4	144.33 MB
external-assets-links.txt	TXT	1.08 KB

15 Conclusion

105 See how much you have learned.en.srt	SRT	5.4 KB
105 See how much you have learned.mp4	MP4	13.95 MB
106 What’s further out there in the machine and deep learning world.en.srt	SRT	2.64 KB
106 What’s further out there in the machine and deep learning world.mp4	MP4	6.26 MB
107 An overview of CNNs.en.srt	SRT	6.72 KB
107 An overview of CNNs.mp4	MP4	10.92 MB
108 How DeepMind uses deep learning.html	HTML	2.24 KB
109 An overview of RNNs.en.srt	SRT	3.74 KB
109 An overview of RNNs.mp4	MP4	4.86 MB
110 An overview of non-NN approaches.en.srt	SRT	5.33 KB
110 An overview of non-NN approaches.mp4	MP4	7.84 MB

16 Bonus lecture

111 Bonus lecture_ Next steps.html

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3.87 KB

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Udemy - Deep Learning with TensorFlow 2.0 [2020]

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