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| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 001 Meet your instructors and why you should study machine learning_.en.srt |
10.53Кб |
| 001 Meet your instructors and why you should study machine learning_.mp4 |
105.78Мб |
| 002 What does the course cover_.en.srt |
6.45Кб |
| 002 What does the course cover_.mp4 |
16.35Мб |
| 003 Download All Resources and Important FAQ.html |
1.75Кб |
| 004 Course-Notes-Section-2.pdf |
927.67Кб |
| 004 Introduction to neural networks.en.srt |
6.16Кб |
| 004 Introduction to neural networks.mp4 |
13.55Мб |
| 005 Course-Notes-Section-2.pdf |
927.67Кб |
| 005 Training the model.en.srt |
4.47Кб |
| 005 Training the model.mp4 |
8.81Мб |
| 006 Course-Notes-Section-2.pdf |
927.67Кб |
| 006 Types of machine learning.en.srt |
5.47Кб |
| 006 Types of machine learning.mp4 |
12.20Мб |
| 007 Course-Notes-Section-2.pdf |
927.67Кб |
| 007 The linear model.en.srt |
4.07Кб |
| 007 The linear model.mp4 |
9.12Мб |
| 008 Need Help with Linear Algebra_.html |
1.69Кб |
| 009 Course-Notes-Section-2.pdf |
927.67Кб |
| 009 The linear model. Multiple inputs.en.srt |
3.21Кб |
| 009 The linear model. Multiple inputs.mp4 |
7.49Мб |
| 010 Course-Notes-Section-2.pdf |
927.67Кб |
| 010 The linear model. Multiple inputs and multiple outputs.en.srt |
5.67Кб |
| 010 The linear model. Multiple inputs and multiple outputs.mp4 |
38.28Мб |
| 011 Course-Notes-Section-2.pdf |
927.67Кб |
| 011 Graphical representation.en.srt |
2.80Кб |
| 011 Graphical representation.mp4 |
6.34Мб |
| 012 Course-Notes-Section-2.pdf |
927.67Кб |
| 012 The objective function.en.srt |
2.11Кб |
| 012 The objective function.mp4 |
5.71Мб |
| 013 Course-Notes-Section-2.pdf |
927.67Кб |
| 013 L2-norm loss.en.srt |
2.91Кб |
| 013 L2-norm loss.mp4 |
7.26Мб |
| 014 Course-Notes-Section-2.pdf |
927.67Кб |
| 014 Cross-entropy loss.en.srt |
5.55Кб |
| 014 Cross-entropy loss.mp4 |
11.35Мб |
| 015 Course-Notes-Section-2.pdf |
927.67Кб |
| 015 GD-function-example.xlsx |
42.33Кб |
| 015 One parameter gradient descent.en.srt |
8.79Кб |
| 015 One parameter gradient descent.mp4 |
17.76Мб |
| 016 Course-Notes-Section-2.pdf |
927.67Кб |
| 016 N-parameter gradient descent.en.srt |
7.81Кб |
| 016 N-parameter gradient descent.mp4 |
39.45Мб |
| 017 Setting up the environment - An introduction - Do not skip, please!.en.srt |
1.44Кб |
| 017 Setting up the environment - An introduction - Do not skip, please!.mp4 |
5.95Мб |
| 018 Why Python and why Jupyter_.en.srt |
6.53Кб |
| 018 Why Python and why Jupyter_.mp4 |
32.06Мб |
| 019 Installing Anaconda.en.srt |
4.76Кб |
| 019 Installing Anaconda.mp4 |
28.38Мб |
| 020 The Jupyter dashboard - part 1.en.srt |
3.27Кб |
| 020 The Jupyter dashboard - part 1.mp4 |
8.70Мб |
| 021 The Jupyter dashboard - part 2.en.srt |
7.04Кб |
| 021 The Jupyter dashboard - part 2.mp4 |
18.80Мб |
| 022 Jupyter Shortcuts.html |
1.20Кб |
| 022 Shortcuts-for-Jupyter.pdf |
619.17Кб |
| 023 Installing TensorFlow 2.en.srt |
6.63Кб |
| 023 Installing TensorFlow 2.mp4 |
38.72Мб |
| 024 Installing packages - exercise.html |
1.08Кб |
| 025 Installing packages - solution.html |
1.14Кб |
| 026 Minimal example - part 1.en.srt |
4.69Кб |
| 026 Minimal example - part 1.mp4 |
6.53Мб |
| 027 Minimal example - part 2.en.srt |
7.13Кб |
| 027 Minimal example - part 2.mp4 |
10.70Мб |
| 028 Minimal example - part 3.en.srt |
4.63Кб |
| 028 Minimal example - part 3.mp4 |
9.76Мб |
| 029 Minimal example - part 4.en.srt |
11.29Кб |
| 029 Minimal example - part 4.mp4 |
20.80Мб |
| 030 Minimal example - Exercises.html |
2.45Кб |
| 031 TensorFlow outline.en.srt |
5.44Кб |
| 031 TensorFlow outline.mp4 |
33.53Мб |
| 032 TensorFlow 2 intro.en.srt |
3.77Кб |
| 032 TensorFlow 2 intro.mp4 |
21.98Мб |
| 033 A Note on Coding in TensorFlow.en.srt |
1.41Кб |
| 033 A Note on Coding in TensorFlow.mp4 |
6.76Мб |
| 034 Types of file formats in TensorFlow and data handling.en.srt |
3.65Кб |
| 034 Types of file formats in TensorFlow and data handling.mp4 |
16.40Мб |
| 035 Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.en.srt |
8.13Кб |
| 035 Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 |
34.68Мб |
| 036 Interpreting the result and extracting the weights and bias.en.srt |
6.44Кб |
| 036 Interpreting the result and extracting the weights and bias.mp4 |
30.26Мб |
| 037 Cutomizing your model.en.srt |
4.27Кб |
| 037 Cutomizing your model.mp4 |
22.90Мб |
| 038 Minimal example with TensorFlow - Exercises.html |
2.25Кб |
| 039 Course-Notes-Section-6.pdf |
936.42Кб |
| 039 Layers.en.srt |
2.51Кб |
| 039 Layers.mp4 |
4.73Мб |
| 040 Course-Notes-Section-6.pdf |
936.42Кб |
| 040 What is a deep net_.en.srt |
3.41Кб |
| 040 What is a deep net_.mp4 |
6.72Мб |
| 041 Understanding deep nets in depth.en.srt |
6.93Кб |
| 041 Understanding deep nets in depth.mp4 |
13.40Мб |
| 042 Why do we need non-linearities_.en.srt |
3.96Кб |
| 042 Why do we need non-linearities_.mp4 |
8.95Мб |
| 043 Activation functions.en.srt |
5.38Кб |
| 043 Activation functions.mp4 |
8.73Мб |
| 044 Softmax activation.en.srt |
4.48Кб |
| 044 Softmax activation.mp4 |
7.37Мб |
| 045 Backpropagation.en.srt |
4.56Кб |
| 045 Backpropagation.mp4 |
11.05Мб |
| 046 Backpropagation - visual representation.en.srt |
4.18Кб |
| 046 Backpropagation - visual representation.mp4 |
6.84Мб |
| 047 Backpropagation. A peek into the Mathematics of Optimization.html |
1.43Кб |
| 047 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf |
182.38Кб |
| 048 Underfitting and overfitting.en.srt |
5.82Кб |
| 048 Underfitting and overfitting.mp4 |
11.05Мб |
| 049 Underfitting and overfitting - classification.en.srt |
2.81Кб |
| 049 Underfitting and overfitting - classification.mp4 |
6.76Мб |
| 050 Training and validation.en.srt |
5.06Кб |
| 050 Training and validation.mp4 |
9.23Мб |
| 051 Training, validation, and test.en.srt |
3.71Кб |
| 051 Training, validation, and test.mp4 |
7.44Мб |
| 052 N-fold cross validation.en.srt |
4.38Кб |
| 052 N-fold cross validation.mp4 |
6.98Мб |
| 053 Early stopping.en.srt |
7.12Кб |
| 053 Early stopping.mp4 |
9.43Мб |
| 054 Initialization - Introduction.en.srt |
3.68Кб |
| 054 Initialization - Introduction.mp4 |
8.03Мб |
| 055 Types of simple initializations.en.srt |
3.81Кб |
| 055 Types of simple initializations.mp4 |
5.61Мб |
| 056 Xavier initialization.en.srt |
3.86Кб |
| 056 Xavier initialization.mp4 |
5.82Мб |
| 057 Stochastic gradient descent.en.srt |
5.09Кб |
| 057 Stochastic gradient descent.mp4 |
9.38Мб |
| 058 Gradient descent pitfalls.en.srt |
2.93Кб |
| 058 Gradient descent pitfalls.mp4 |
4.30Мб |
| 059 Momentum.en.srt |
3.66Кб |
| 059 Momentum.mp4 |
6.10Мб |
| 060 Learning rate schedules.en.srt |
6.21Кб |
| 060 Learning rate schedules.mp4 |
10.30Мб |
| 061 Learning rate schedules. A picture.en.srt |
2.25Кб |
| 061 Learning rate schedules. A picture.mp4 |
3.14Мб |
| 062 Adaptive learning rate schedules.en.srt |
5.42Кб |
| 062 Adaptive learning rate schedules.mp4 |
8.86Мб |
| 063 Adaptive moment estimation.en.srt |
3.47Кб |
| 063 Adaptive moment estimation.mp4 |
7.77Мб |
| 064 Preprocessing introduction.en.srt |
4.00Кб |
| 064 Preprocessing introduction.mp4 |
8.42Мб |
| 065 Basic preprocessing.en.srt |
1.73Кб |
| 065 Basic preprocessing.mp4 |
3.65Мб |
| 066 Standardization.en.srt |
6.19Кб |
| 066 Standardization.mp4 |
8.32Мб |
| 067 Dealing with categorical data.en.srt |
2.89Кб |
| 067 Dealing with categorical data.mp4 |
6.07Мб |
| 068 One-hot and binary encoding.en.srt |
4.96Кб |
| 068 One-hot and binary encoding.mp4 |
6.24Мб |
| 069 The dataset.en.srt |
3.72Кб |
| 069 The dataset.mp4 |
13.37Мб |
| 070 How to tackle the MNIST.en.srt |
3.64Кб |
| 070 How to tackle the MNIST.mp4 |
18.67Мб |
| 071 Importing the relevant packages and load the data.en.srt |
3.20Кб |
| 071 Importing the relevant packages and load the data.mp4 |
16.32Мб |
| 072 Preprocess the data - create a validation dataset and scale the data.en.srt |
6.50Кб |
| 072 Preprocess the data - create a validation dataset and scale the data.mp4 |
29.05Мб |
| 073 Preprocess the data - scale the test data.html |
997б |
| 074 Preprocess the data - shuffle and batch the data.en.srt |
9.61Кб |
| 074 Preprocess the data - shuffle and batch the data.mp4 |
41.54Мб |
| 075 Preprocess the data - shuffle and batch the data.html |
1004б |
| 076 Outline the model.en.srt |
7.49Кб |
| 076 Outline the model.mp4 |
28.24Мб |
| 077 Select the loss and the optimizer.en.srt |
3.14Кб |
| 077 Select the loss and the optimizer.mp4 |
13.89Мб |
| 078 Learning.en.srt |
8.27Кб |
| 078 Learning.mp4 |
40.95Мб |
| 079 MNIST - exercises.html |
2.86Кб |
| 080 MNIST - solutions.html |
3.00Кб |
| 081 Testing the model.en.srt |
6.26Кб |
| 081 Testing the model.mp4 |
29.54Мб |
| 082 Audiobooks-data.csv |
625.21Кб |
| 082 Exploring the dataset and identifying predictors.en.srt |
11.08Кб |
| 082 Exploring the dataset and identifying predictors.mp4 |
66.26Мб |
| 083 Outlining the business case solution.en.srt |
2.08Кб |
| 083 Outlining the business case solution.mp4 |
7.31Мб |
| 084 Balancing the dataset.en.srt |
4.67Кб |
| 084 Balancing the dataset.mp4 |
30.44Мб |
| 085 Audiobooks-data.csv |
625.21Кб |
| 085 Preprocessing the data.en.srt |
12.71Кб |
| 085 Preprocessing the data.mp4 |
84.29Мб |
| 086 Audiobooks-data.csv |
625.21Кб |
| 086 Preprocessing exercise.html |
1.27Кб |
| 087 Load the preprocessed data.en.srt |
4.89Кб |
| 087 Load the preprocessed data.mp4 |
17.56Мб |
| 088 Load the preprocessed data - Exercise.html |
991б |
| 089 Learning and interpreting the result.en.srt |
6.53Кб |
| 089 Learning and interpreting the result.mp4 |
31.15Мб |
| 090 Setting an early stopping mechanism.en.srt |
8.11Кб |
| 090 Setting an early stopping mechanism.mp4 |
49.81Мб |
| 091 Setting an early stopping mechanism - Exercise.html |
1.09Кб |
| 092 Testing the model.en.srt |
2.12Кб |
| 092 Testing the model.mp4 |
10.80Мб |
| 093 Final exercise.html |
1.30Кб |
| 094 What is a Matrix_.en.srt |
4.51Кб |
| 094 What is a Matrix_.mp4 |
33.59Мб |
| 095 Scalars and Vectors.en.srt |
3.93Кб |
| 095 Scalars and Vectors.mp4 |
33.84Мб |
| 096 Linear Algebra and Geometry.en.srt |
4.27Кб |
| 096 Linear Algebra and Geometry.mp4 |
49.79Мб |
| 097 Scalars, Vectors and Matrices in Python.en.srt |
6.38Кб |
| 097 Scalars, Vectors and Matrices in Python.mp4 |
26.66Мб |
| 098 Tensors.en.srt |
3.75Кб |
| 098 Tensors.mp4 |
22.51Мб |
| 099 Addition and Subtraction of Matrices.en.srt |
4.22Кб |
| 099 Addition and Subtraction of Matrices.mp4 |
32.60Мб |
| 100 Errors when Adding Matrices.en.srt |
2.67Кб |
| 100 Errors when Adding Matrices.mp4 |
11.16Мб |
| 101 Transpose of a Matrix.en.srt |
5.58Кб |
| 101 Transpose of a Matrix.mp4 |
38.08Мб |
| 102 Dot Product of Vectors.en.srt |
4.45Кб |
| 102 Dot Product of Vectors.mp4 |
23.98Мб |
| 103 Dot Product of Matrices.en.srt |
9.92Кб |
| 103 Dot Product of Matrices.mp4 |
49.38Мб |
| 104 Why is Linear Algebra Useful_.en.srt |
12.24Кб |
| 104 Why is Linear Algebra Useful_.mp4 |
144.33Мб |
| 105 See how much you have learned.en.srt |
5.40Кб |
| 105 See how much you have learned.mp4 |
13.95Мб |
| 106 What’s further out there in the machine and deep learning world.en.srt |
2.64Кб |
| 106 What’s further out there in the machine and deep learning world.mp4 |
6.26Мб |
| 107 An overview of CNNs.en.srt |
6.72Кб |
| 107 An overview of CNNs.mp4 |
10.92Мб |
| 108 How DeepMind uses deep learning.html |
2.24Кб |
| 109 An overview of RNNs.en.srt |
3.74Кб |
| 109 An overview of RNNs.mp4 |
4.86Мб |
| 110 An overview of non-NN approaches.en.srt |
5.33Кб |
| 110 An overview of non-NN approaches.mp4 |
7.84Мб |
| 111 Bonus lecture_ Next steps.html |
3.87Кб |
| external-assets-links.txt |
1.74Кб |
| external-assets-links.txt |
1.42Кб |
| external-assets-links.txt |
2.63Кб |
| external-assets-links.txt |
1.47Кб |
| external-assets-links.txt |
1.08Кб |
| TutsNode.com.txt |
63б |