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