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| 1.1 All course materials and links!.html |
114б |
| 1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
114б |
| 1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
119б |
| 1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
119б |
| 1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
114б |
| 1. Become An Alumni.html |
944б |
| 1. Course Outline.mp4 |
58.03Мб |
| 1. Course Outline.srt |
7.97Кб |
| 1. Introduction to Computer Vision with TensorFlow.mp4 |
75.01Мб |
| 1. Introduction to Computer Vision with TensorFlow.srt |
15.00Кб |
| 1. Introduction to Milestone Project 1 Food Vision Big™.mp4 |
42.32Мб |
| 1. Introduction to Milestone Project 1 Food Vision Big™.srt |
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| 1. Introduction to neural network classification in TensorFlow.mp4 |
72.81Мб |
| 1. Introduction to neural network classification in TensorFlow.srt |
12.76Кб |
| 1. Introduction to Neural Network Regression with TensorFlow.mp4 |
60.06Мб |
| 1. Introduction to Neural Network Regression with TensorFlow.srt |
11.41Кб |
| 1. Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.mp4 |
61.46Мб |
| 1. Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.srt |
9.78Кб |
| 1. Introduction to Transfer Learning Part 3 Scaling Up.mp4 |
41.53Мб |
| 1. Introduction to Transfer Learning Part 3 Scaling Up.srt |
10.12Кб |
| 1. More Videos Coming Soon!.html |
41б |
| 1. More Videos Coming Soon!.html |
41б |
| 1. More Videos Coming Soon!.html |
41б |
| 1. More Videos Coming Soon!.html |
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| 1. More Videos Coming Soon!.html |
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| 1. Quick Note Upcoming Videos.html |
706б |
| 1. Quick Note Upcoming Videos.html |
706б |
| 1. Quick Note Upcoming Videos.html |
706б |
| 1. Quick Note Upcoming Videos.html |
706б |
| 1. Special Bonus Lecture.html |
3.65Кб |
| 1. What is and why use transfer learning.mp4 |
65.81Мб |
| 1. What is and why use transfer learning.srt |
15.94Кб |
| 1. What is deep learning.mp4 |
34.17Мб |
| 1. What is deep learning.srt |
6.80Кб |
| 10 |
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| 10.1 car-sales-missing-data.csv |
287б |
| 10.1 httpswww.mathsisfun.comdatastandard-deviation.html.html |
116б |
| 10.2 httpsjakevdp.github.ioPythonDataScienceHandbook03.00-introduction-to-pandas.html.html |
146б |
| 10. Comparing Our Model's Results.mp4 |
143.93Мб |
| 10. Comparing Our Model's Results.srt |
21.56Кб |
| 10. Creating your first tensors with TensorFlow and tf.constant().mp4 |
134.83Мб |
| 10. Creating your first tensors with TensorFlow and tf.constant().srt |
24.75Кб |
| 10. Downloading and preparing the data for Model 1 (1 percent of training data).mp4 |
97.80Мб |
| 10. Downloading and preparing the data for Model 1 (1 percent of training data).srt |
12.98Кб |
| 10. Downloading a pretrained model to make and evaluate predictions with.mp4 |
78.69Мб |
| 10. Downloading a pretrained model to make and evaluate predictions with.srt |
8.91Кб |
| 10. Evaluating a TensorFlow model part 2 (the three datasets).mp4 |
81.56Мб |
| 10. Evaluating a TensorFlow model part 2 (the three datasets).srt |
14.05Кб |
| 10. Improving our non-CNN model by adding more layers.mp4 |
106.47Мб |
| 10. Improving our non-CNN model by adding more layers.srt |
13.98Кб |
| 10. Make our poor classification model work for a regression dataset.mp4 |
123.01Мб |
| 10. Make our poor classification model work for a regression dataset.srt |
16.33Кб |
| 10. Manipulating Arrays 2.mp4 |
67.91Мб |
| 10. Manipulating Arrays 2.srt |
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| 10. Manipulating Data.mp4 |
105.00Мб |
| 10. Manipulating Data.srt |
18.56Кб |
| 10. Modelling - Picking the Model.mp4 |
23.24Мб |
| 10. Modelling - Picking the Model.srt |
6.23Кб |
| 10. Section Review.mp4 |
5.56Мб |
| 10. Section Review.srt |
2.20Кб |
| 10. Turning on mixed precision training with TensorFlow.mp4 |
107.71Мб |
| 10. Turning on mixed precision training with TensorFlow.srt |
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| 11.1 httpswww.mathsisfun.comdatastandard-deviation.html.html |
116б |
| 11.1 pandas-anatomy-of-a-dataframe.png |
333.24Кб |
| 11. Breaking our CNN model down part 1 Becoming one with the data.mp4 |
90.92Мб |
| 11. Breaking our CNN model down part 1 Becoming one with the data.srt |
13.00Кб |
| 11. Building a data augmentation layer to use inside our model.mp4 |
117.46Мб |
| 11. Building a data augmentation layer to use inside our model.srt |
16.15Кб |
| 11. Creating a feature extraction model capable of using mixed precision training.mp4 |
107.92Мб |
| 11. Creating a feature extraction model capable of using mixed precision training.srt |
17.41Кб |
| 11. Creating tensors with TensorFlow and tf.Variable().mp4 |
70.85Мб |
| 11. Creating tensors with TensorFlow and tf.Variable().srt |
9.90Кб |
| 11. Evaluating a TensorFlow model part 3 (getting a model summary).mp4 |
192.79Мб |
| 11. Evaluating a TensorFlow model part 3 (getting a model summary).srt |
21.53Кб |
| 11. Making predictions with our trained model on 25,250 test samples.mp4 |
115.59Мб |
| 11. Making predictions with our trained model on 25,250 test samples.srt |
16.24Кб |
| 11. Manipulating Data 2.mp4 |
86.56Мб |
| 11. Manipulating Data 2.srt |
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| 11. Modelling - Tuning.mp4 |
15.98Мб |
| 11. Modelling - Tuning.srt |
5.09Кб |
| 11. Non-linearity part 1 Straight lines and non-straight lines.mp4 |
95.62Мб |
| 11. Non-linearity part 1 Straight lines and non-straight lines.srt |
13.79Кб |
| 11. Standard Deviation and Variance.mp4 |
51.13Мб |
| 11. Standard Deviation and Variance.srt |
9.81Кб |
| 11. TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum.html |
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| 12.1 Pandas video notes.html |
185б |
| 12.2 Pandas video code.html |
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| 12. Breaking our CNN model down part 2 Preparing to load our data.mp4 |
109.48Мб |
| 12. Breaking our CNN model down part 2 Preparing to load our data.srt |
16.51Кб |
| 12. Checking to see if our model is using mixed precision training layer by layer.mp4 |
87.67Мб |
| 12. Checking to see if our model is using mixed precision training layer by layer.srt |
10.27Кб |
| 12. Creating random tensors with TensorFlow.mp4 |
88.45Мб |
| 12. Creating random tensors with TensorFlow.srt |
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| 12. Evaluating a TensorFlow model part 4 (visualising a model's layers).mp4 |
70.28Мб |
| 12. Evaluating a TensorFlow model part 4 (visualising a model's layers).srt |
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| 12. Manipulating Data 3.mp4 |
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| 12. Manipulating Data 3.srt |
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| 12. Modelling - Comparison.mp4 |
44.86Мб |
| 12. Modelling - Comparison.srt |
13.32Кб |
| 12. Non-linearity part 2 Building our first neural network with non-linearity.mp4 |
59.00Мб |
| 12. Non-linearity part 2 Building our first neural network with non-linearity.srt |
7.58Кб |
| 12. Reshape and Transpose.mp4 |
53.57Мб |
| 12. Reshape and Transpose.srt |
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| 12. Unravelling our test dataset for comparing ground truth labels to predictions.mp4 |
43.81Мб |
| 12. Unravelling our test dataset for comparing ground truth labels to predictions.srt |
7.72Кб |
| 12. Visualising what happens when images pass through our data augmentation layer.mp4 |
119.36Мб |
| 12. Visualising what happens when images pass through our data augmentation layer.srt |
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| 13.1 httpswww.mathsisfun.comalgebramatrix-multiplying.html.html |
119б |
| 13. Assignment Pandas Practice.html |
2.05Кб |
| 13. Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.mp4 |
103.42Мб |
| 13. Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.srt |
13.45Кб |
| 13. Building Model 1 (with a data augmentation layer and 1% of training data).mp4 |
152.95Мб |
| 13. Building Model 1 (with a data augmentation layer and 1% of training data).srt |
22.42Кб |
| 13. Confirming our model's predictions are in the same order as the test labels.mp4 |
50.54Мб |
| 13. Confirming our model's predictions are in the same order as the test labels.srt |
6.77Кб |
| 13. Dot Product vs Element Wise.mp4 |
83.80Мб |
| 13. Dot Product vs Element Wise.srt |
15.89Кб |
| 13. Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp4 |
78.88Мб |
| 13. Evaluating a TensorFlow model part 5 (visualising a model's predictions).srt |
11.92Кб |
| 13. Non-linearity part 3 Upgrading our non-linear model with more layers.mp4 |
123.24Мб |
| 13. Non-linearity part 3 Upgrading our non-linear model with more layers.srt |
14.34Кб |
| 13. Overfitting and Underfitting Definitions.html |
1.97Кб |
| 13. Shuffling the order of tensors.mp4 |
89.86Мб |
| 13. Shuffling the order of tensors.srt |
12.63Кб |
| 13. Training and evaluating a feature extraction model (Food Vision Big™).mp4 |
89.02Мб |
| 13. Training and evaluating a feature extraction model (Food Vision Big™).srt |
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389.08Кб |
| 14.1 Course Notes.html |
108б |
| 14.2 httpscolab.research.google.com.html |
95б |
| 14. Breaking our CNN model down part 4 Building a baseline CNN model.mp4 |
85.30Мб |
| 14. Breaking our CNN model down part 4 Building a baseline CNN model.srt |
11.22Кб |
| 14. Building Model 2 (with a data augmentation layer and 10% of training data).mp4 |
159.77Мб |
| 14. Building Model 2 (with a data augmentation layer and 10% of training data).srt |
23.45Кб |
| 14. Creating a confusion matrix for our model's 101 different classes.mp4 |
156.60Мб |
| 14. Creating a confusion matrix for our model's 101 different classes.srt |
17.49Кб |
| 14. Creating tensors from NumPy arrays.mp4 |
101.34Мб |
| 14. Creating tensors from NumPy arrays.srt |
15.03Кб |
| 14. Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp4 |
70.37Мб |
| 14. Evaluating a TensorFlow model part 6 (common regression evaluation metrics).srt |
11.16Кб |
| 14. Exercise Nut Butter Store Sales.mp4 |
91.27Мб |
| 14. Exercise Nut Butter Store Sales.srt |
17.41Кб |
| 14. Experimentation.mp4 |
21.30Мб |
| 14. Experimentation.srt |
5.09Кб |
| 14. How To Download The Course Assignments.mp4 |
66.79Мб |
| 14. How To Download The Course Assignments.srt |
11.24Кб |
| 14. Introducing your Milestone Project 1 challenge build a model to beat DeepFood.mp4 |
89.12Мб |
| 14. Introducing your Milestone Project 1 challenge build a model to beat DeepFood.srt |
11.24Кб |
| 14. Non-linearity part 4 Modelling our non-linear data once and for all.mp4 |
96.62Мб |
| 14. Non-linearity part 4 Modelling our non-linear data once and for all.srt |
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1.39Мб |
| 15.1 CNN Explainer website.html |
102б |
| 15. Breaking our CNN model down part 5 Looking inside a Conv2D layer.mp4 |
186.04Мб |
| 15. Breaking our CNN model down part 5 Looking inside a Conv2D layer.srt |
22.79Кб |
| 15. Comparison Operators.mp4 |
26.38Мб |
| 15. Comparison Operators.srt |
5.22Кб |
| 15. Creating a ModelCheckpoint to save our model's weights during training.mp4 |
68.99Мб |
| 15. Creating a ModelCheckpoint to save our model's weights during training.srt |
10.72Кб |
| 15. Evaluating a TensorFlow regression model part 7 (mean absolute error).mp4 |
56.09Мб |
| 15. Evaluating a TensorFlow regression model part 7 (mean absolute error).srt |
8.10Кб |
| 15. Evaluating every individual class in our dataset.mp4 |
131.77Мб |
| 15. Evaluating every individual class in our dataset.srt |
19.30Кб |
| 15. Getting information from your tensors (tensor attributes).mp4 |
87.39Мб |
| 15. Getting information from your tensors (tensor attributes).srt |
16.96Кб |
| 15. Milestone Project 1 Food Vision Big™, exercises and extra-curriculum.html |
2.32Кб |
| 15. Non-linearity part 5 Replicating non-linear activation functions from scratch.mp4 |
146.61Мб |
| 15. Non-linearity part 5 Replicating non-linear activation functions from scratch.srt |
18.28Кб |
| 15. Tools We Will Use.mp4 |
27.34Мб |
| 15. Tools We Will Use.srt |
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| 16. Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.mp4 |
77.08Мб |
| 16. Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.srt |
9.86Кб |
| 16. Evaluating a TensorFlow regression model part 7 (mean square error).mp4 |
32.31Мб |
| 16. Evaluating a TensorFlow regression model part 7 (mean square error).srt |
3.88Кб |
| 16. Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp4 |
68.15Мб |
| 16. Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).srt |
9.85Кб |
| 16. Getting great results in less time by tweaking the learning rate.mp4 |
136.78Мб |
| 16. Getting great results in less time by tweaking the learning rate.srt |
19.38Кб |
| 16. Indexing and expanding tensors.mp4 |
86.57Мб |
| 16. Indexing and expanding tensors.srt |
16.96Кб |
| 16. Optional Elements of AI.html |
975б |
| 16. Plotting our model's F1-scores for each separate class.mp4 |
77.94Мб |
| 16. Plotting our model's F1-scores for each separate class.srt |
10.69Кб |
| 16. Sorting Arrays.mp4 |
32.82Мб |
| 16. Sorting Arrays.srt |
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| 17.1 numpy-images.zip |
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| 17.2 NumPy Video code.html |
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| 17.3 Section Notes.html |
184б |
| 17. Breaking our CNN model down part 7 Evaluating our CNN's training curves.mp4 |
106.20Мб |
| 17. Breaking our CNN model down part 7 Evaluating our CNN's training curves.srt |
17.08Кб |
| 17. Creating a function to load and prepare images for making predictions.mp4 |
109.54Мб |
| 17. Creating a function to load and prepare images for making predictions.srt |
15.79Кб |
| 17. Loading and comparing saved weights to our existing trained Model 2.mp4 |
62.67Мб |
| 17. Loading and comparing saved weights to our existing trained Model 2.srt |
9.65Кб |
| 17. Manipulating tensors with basic operations.mp4 |
45.22Мб |
| 17. Manipulating tensors with basic operations.srt |
6.95Кб |
| 17. Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4 |
127.26Мб |
| 17. Setting up TensorFlow modelling experiments part 1 (start with a simple model).srt |
17.44Кб |
| 17. Turn Images Into NumPy Arrays.mp4 |
85.98Мб |
| 17. Turn Images Into NumPy Arrays.srt |
10.60Кб |
| 17. Using the TensorFlow History object to plot a model's loss curves.mp4 |
62.12Мб |
| 17. Using the TensorFlow History object to plot a model's loss curves.srt |
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| 18. Assignment NumPy Practice.html |
2.17Кб |
| 18. Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.mp4 |
130.44Мб |
| 18. Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.srt |
19.25Кб |
| 18. Making predictions on our test images and evaluating them.mp4 |
171.68Мб |
| 18. Making predictions on our test images and evaluating them.srt |
23.48Кб |
| 18. Matrix multiplication with tensors part 1.mp4 |
100.85Мб |
| 18. Matrix multiplication with tensors part 1.srt |
15.22Кб |
| 18. Preparing Model 3 (our first fine-tuned model).mp4 |
198.23Мб |
| 18. Preparing Model 3 (our first fine-tuned model).srt |
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| 18. Setting up TensorFlow modelling experiments part 2 (increasing complexity).mp4 |
95.63Мб |
| 18. Setting up TensorFlow modelling experiments part 2 (increasing complexity).srt |
15.86Кб |
| 18. Using callbacks to find a model's ideal learning rate.mp4 |
155.88Мб |
| 18. Using callbacks to find a model's ideal learning rate.srt |
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| 19. Breaking our CNN model down part 9 Reducing overfitting with data augmentation.mp4 |
66.08Мб |
| 19. Breaking our CNN model down part 9 Reducing overfitting with data augmentation.srt |
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| 19. Comparing and tracking your TensorFlow modelling experiments.mp4 |
92.25Мб |
| 19. Comparing and tracking your TensorFlow modelling experiments.srt |
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| 19. Discussing the benefits of finding your model's most wrong predictions.mp4 |
59.30Мб |
| 19. Discussing the benefits of finding your model's most wrong predictions.srt |
9.41Кб |
| 19. Fitting and evaluating Model 3 (our first fine-tuned model).mp4 |
69.16Мб |
| 19. Fitting and evaluating Model 3 (our first fine-tuned model).srt |
10.61Кб |
| 19. Matrix multiplication with tensors part 2.mp4 |
107.79Мб |
| 19. Matrix multiplication with tensors part 2.srt |
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| 19. Optional Extra NumPy resources.html |
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| 19. Training and evaluating a model with an ideal learning rate.mp4 |
89.01Мб |
| 19. Training and evaluating a model with an ideal learning rate.srt |
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| 2. Downloading and preparing data for our first transfer learning model.mp4 |
132.67Мб |
| 2. Downloading and preparing data for our first transfer learning model.srt |
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| 2. Example classification problems (and their inputs and outputs).mp4 |
50.71Мб |
| 2. Example classification problems (and their inputs and outputs).srt |
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| 2. Getting helper functions ready and downloading data to model.mp4 |
131.54Мб |
| 2. Getting helper functions ready and downloading data to model.srt |
17.73Кб |
| 2. Importing a script full of helper functions (and saving lots of space).mp4 |
89.39Мб |
| 2. Importing a script full of helper functions (and saving lots of space).srt |
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| 2. Inputs and outputs of a neural network regression model.mp4 |
57.57Мб |
| 2. Inputs and outputs of a neural network regression model.srt |
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| 2. Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.mp4 |
76.65Мб |
| 2. Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.srt |
12.11Кб |
| 2. Join Our Online Classroom!.html |
2.43Кб |
| 2. LinkedIn Endorsements.html |
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| 2. Making sure we have access to the right GPU for mixed precision training.mp4 |
88.15Мб |
| 2. Making sure we have access to the right GPU for mixed precision training.srt |
14.06Кб |
| 2. Section Overview.mp4 |
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| 2. Section Overview.mp4 |
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| 2. Section Overview.mp4 |
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| 2. What is Machine Learning.mp4 |
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| 2. What is Machine Learning.srt |
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| 2. Why use deep learning.mp4 |
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| 2. Why use deep learning.srt |
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| 20. Breaking our CNN model down part 10 Visualizing our augmented data.mp4 |
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| 20. Breaking our CNN model down part 10 Visualizing our augmented data.srt |
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| 20. Comparing our model's results before and after fine-tuning.mp4 |
84.18Мб |
| 20. Comparing our model's results before and after fine-tuning.srt |
13.82Кб |
| 20. How to save a TensorFlow model.mp4 |
92.29Мб |
| 20. How to save a TensorFlow model.srt |
11.39Кб |
| 20. Introducing more classification evaluation methods.mp4 |
42.21Мб |
| 20. Introducing more classification evaluation methods.srt |
8.87Кб |
| 20. Matrix multiplication with tensors part 3.mp4 |
80.62Мб |
| 20. Matrix multiplication with tensors part 3.srt |
13.27Кб |
| 20. Writing code to uncover our model's most wrong predictions.mp4 |
109.60Мб |
| 20. Writing code to uncover our model's most wrong predictions.srt |
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| 21 |
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| 21. Breaking our CNN model down part 11 Training a CNN model on augmented data.mp4 |
94.06Мб |
| 21. Breaking our CNN model down part 11 Training a CNN model on augmented data.srt |
13.58Кб |
| 21. Changing the datatype of tensors.mp4 |
71.39Мб |
| 21. Changing the datatype of tensors.srt |
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| 21. Downloading and preparing data for our biggest experiment yet (Model 4).mp4 |
56.68Мб |
| 21. Downloading and preparing data for our biggest experiment yet (Model 4).srt |
8.97Кб |
| 21. Finding the accuracy of our classification model.mp4 |
34.07Мб |
| 21. Finding the accuracy of our classification model.srt |
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| 21. How to load and use a saved TensorFlow model.mp4 |
104.37Мб |
| 21. How to load and use a saved TensorFlow model.srt |
12.81Кб |
| 21. Plotting and visualising the samples our model got most wrong.mp4 |
125.49Мб |
| 21. Plotting and visualising the samples our model got most wrong.srt |
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| 22. (Optional) How to save and download files from Google Colab.mp4 |
67.70Мб |
| 22. (Optional) How to save and download files from Google Colab.srt |
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| 22. Breaking our CNN model down part 12 Discovering the power of shuffling data.mp4 |
103.86Мб |
| 22. Breaking our CNN model down part 12 Discovering the power of shuffling data.srt |
14.30Кб |
| 22. Creating our first confusion matrix (to see where our model is getting confused).mp4 |
65.70Мб |
| 22. Creating our first confusion matrix (to see where our model is getting confused).srt |
11.54Кб |
| 22. Making predictions on and plotting our own custom images.mp4 |
108.30Мб |
| 22. Making predictions on and plotting our own custom images.srt |
14.61Кб |
| 22. Preparing our final modelling experiment (Model 4).mp4 |
96.42Мб |
| 22. Preparing our final modelling experiment (Model 4).srt |
14.88Кб |
| 22. Tensor aggregation (finding the min, max, mean & more).mp4 |
89.58Мб |
| 22. Tensor aggregation (finding the min, max, mean & more).srt |
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| 23 |
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| 23. Breaking our CNN model down part 13 Exploring options to improve our model.mp4 |
50.34Мб |
| 23. Breaking our CNN model down part 13 Exploring options to improve our model.srt |
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| 23. Fine-tuning Model 4 on 100% of the training data and evaluating its results.mp4 |
96.84Мб |
| 23. Fine-tuning Model 4 on 100% of the training data and evaluating its results.srt |
14.85Кб |
| 23. Making our confusion matrix prettier.mp4 |
114.12Мб |
| 23. Making our confusion matrix prettier.srt |
18.28Кб |
| 23. Putting together what we've learned part 1 (preparing a dataset).mp4 |
143.51Мб |
| 23. Putting together what we've learned part 1 (preparing a dataset).srt |
18.70Кб |
| 23. Tensor troubleshooting example (updating tensor datatypes).mp4 |
69.39Мб |
| 23. Tensor troubleshooting example (updating tensor datatypes).srt |
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| 23. Transfer Learning in TensorFlow Part 3 challenge, exercises and extra-curriculum.html |
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| 24 |
1.16Мб |
| 24. Comparing our modelling experiment results in TensorBoard.mp4 |
95.75Мб |
| 24. Comparing our modelling experiment results in TensorBoard.srt |
15.74Кб |
| 24. Downloading a custom image to make predictions on.mp4 |
53.08Мб |
| 24. Downloading a custom image to make predictions on.srt |
6.93Кб |
| 24. Finding the positional minimum and maximum of a tensor (argmin and argmax).mp4 |
96.50Мб |
| 24. Finding the positional minimum and maximum of a tensor (argmin and argmax).srt |
12.38Кб |
| 24. Putting things together with multi-class classification part 1 Getting the data.mp4 |
87.22Мб |
| 24. Putting things together with multi-class classification part 1 Getting the data.srt |
13.77Кб |
| 24. Putting together what we've learned part 2 (building a regression model).mp4 |
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| 24. Putting together what we've learned part 2 (building a regression model).srt |
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| 25 |
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| 25. How to view and delete previous TensorBoard experiments.mp4 |
21.91Мб |
| 25. How to view and delete previous TensorBoard experiments.srt |
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| 25. Multi-class classification part 2 Becoming one with the data.mp4 |
48.65Мб |
| 25. Multi-class classification part 2 Becoming one with the data.srt |
9.99Кб |
| 25. Putting together what we've learned part 3 (improving our regression model).mp4 |
155.11Мб |
| 25. Putting together what we've learned part 3 (improving our regression model).srt |
18.80Кб |
| 25. Squeezing a tensor (removing all 1-dimension axes).mp4 |
30.20Мб |
| 25. Squeezing a tensor (removing all 1-dimension axes).srt |
3.84Кб |
| 25. Writing a helper function to load and preprocessing custom images.mp4 |
105.15Мб |
| 25. Writing a helper function to load and preprocessing custom images.srt |
13.73Кб |
| 26 |
1.76Мб |
| 26. Making a prediction on a custom image with our trained CNN.mp4 |
99.90Мб |
| 26. Making a prediction on a custom image with our trained CNN.srt |
15.46Кб |
| 26. Multi-class classification part 3 Building a multi-class classification model.mp4 |
142.80Мб |
| 26. Multi-class classification part 3 Building a multi-class classification model.srt |
21.13Кб |
| 26. One-hot encoding tensors.mp4 |
59.73Мб |
| 26. One-hot encoding tensors.srt |
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| 26. Preprocessing data with feature scaling part 1 (what is feature scaling).mp4 |
92.51Мб |
| 26. Preprocessing data with feature scaling part 1 (what is feature scaling).srt |
13.88Кб |
| 26. Transfer Learning in TensorFlow Part 2 challenge, exercises and extra-curriculum.html |
2.64Кб |
| 27 |
1.81Мб |
| 27. Multi-class classification part 4 Improving performance with normalisation.mp4 |
113.41Мб |
| 27. Multi-class classification part 4 Improving performance with normalisation.srt |
16.21Кб |
| 27. Multi-class CNN's part 1 Becoming one with the data.mp4 |
140.19Мб |
| 27. Multi-class CNN's part 1 Becoming one with the data.srt |
22.69Кб |
| 27. Preprocessing data with feature scaling part 2 (normalising our data).mp4 |
97.18Мб |
| 27. Preprocessing data with feature scaling part 2 (normalising our data).srt |
13.93Кб |
| 27. Trying out more tensor math operations.mp4 |
55.93Мб |
| 27. Trying out more tensor math operations.srt |
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| 28 |
1.82Мб |
| 28. Exploring TensorFlow and NumPy's compatibility.mp4 |
43.75Мб |
| 28. Exploring TensorFlow and NumPy's compatibility.srt |
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| 28. Multi-class classification part 5 Comparing normalised and non-normalised data.mp4 |
26.77Мб |
| 28. Multi-class classification part 5 Comparing normalised and non-normalised data.srt |
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| 28. Multi-class CNN's part 2 Preparing our data (turning it into tensors).mp4 |
72.72Мб |
| 28. Multi-class CNN's part 2 Preparing our data (turning it into tensors).srt |
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| 28. Preprocessing data with feature scaling part 3 (fitting a model on scaled data).mp4 |
75.72Мб |
| 28. Preprocessing data with feature scaling part 3 (fitting a model on scaled data).srt |
10.97Кб |
| 29 |
232.10Кб |
| 29. Making sure our tensor operations run really fast on GPUs.mp4 |
110.91Мб |
| 29. Making sure our tensor operations run really fast on GPUs.srt |
14.45Кб |
| 29. Multi-class classification part 6 Finding the ideal learning rate.mp4 |
73.34Мб |
| 29. Multi-class classification part 6 Finding the ideal learning rate.srt |
14.91Кб |
| 29. Multi-class CNN's part 3 Building a multi-class CNN model.mp4 |
89.24Мб |
| 29. Multi-class CNN's part 3 Building a multi-class CNN model.srt |
10.65Кб |
| 29. TensorFlow Regression challenge, exercises & extra-curriculum.html |
1.98Кб |
| 3 |
198.20Кб |
| 3.1 httpsnumpy.orgdoc.html |
83б |
| 3.2 NumPy Video code.html |
190б |
| 3.3 NumPy Notes.html |
184б |
| 3. AIMachine LearningData Science.mp4 |
19.67Мб |
| 3. AIMachine LearningData Science.srt |
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| 3. Anatomy and architecture of a neural network regression model.mp4 |
59.00Мб |
| 3. Anatomy and architecture of a neural network regression model.srt |
12.25Кб |
| 3. Downloading and turning our images into a TensorFlow BatchDataset.mp4 |
173.60Мб |
| 3. Downloading and turning our images into a TensorFlow BatchDataset.srt |
22.01Кб |
| 3. Downloading an image dataset for our first Food Vision model.mp4 |
72.94Мб |
| 3. Downloading an image dataset for our first Food Vision model.srt |
10.31Кб |
| 3. Downloading Workbooks and Assignments.html |
967б |
| 3. Exercise Meet The Community.html |
2.83Кб |
| 3. Getting helper functions ready.mp4 |
31.09Мб |
| 3. Getting helper functions ready.srt |
3.94Кб |
| 3. Input and output tensors of classification problems.mp4 |
51.01Мб |
| 3. Input and output tensors of classification problems.srt |
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| 3. Introducing Callbacks in TensorFlow and making a callback to track our models.mp4 |
94.89Мб |
| 3. Introducing Callbacks in TensorFlow and making a callback to track our models.srt |
14.26Кб |
| 3. Introducing Our Framework.mp4 |
11.39Мб |
| 3. Introducing Our Framework.srt |
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| 3. NumPy Introduction.mp4 |
26.86Мб |
| 3. NumPy Introduction.srt |
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| 3. Outlining the model we're going to build and building a ModelCheckpoint callback.mp4 |
40.61Мб |
| 3. Outlining the model we're going to build and building a ModelCheckpoint callback.srt |
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| 3. TensorFlow Certificate.html |
385б |
| 3. What are neural networks.mp4 |
63.43Мб |
| 3. What are neural networks.srt |
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| 30 |
467.04Кб |
| 30. Multi-class classification part 7 Evaluating our model.mp4 |
119.14Мб |
| 30. Multi-class classification part 7 Evaluating our model.srt |
16.96Кб |
| 30. Multi-class CNN's part 4 Fitting a multi-class CNN model to the data.mp4 |
59.66Мб |
| 30. Multi-class CNN's part 4 Fitting a multi-class CNN model to the data.srt |
8.96Кб |
| 30. TensorFlow Fundamentals challenge, exercises & extra-curriculum.html |
1.95Кб |
| 31 |
1.56Мб |
| 31. Multi-class classification part 8 Creating a confusion matrix.mp4 |
40.52Мб |
| 31. Multi-class classification part 8 Creating a confusion matrix.srt |
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| 31. Multi-class CNN's part 5 Evaluating our multi-class CNN model.mp4 |
41.05Мб |
| 31. Multi-class CNN's part 5 Evaluating our multi-class CNN model.srt |
6.79Кб |
| 31. Python + Machine Learning Monthly.html |
796б |
| 32 |
178.64Кб |
| 32. LinkedIn Endorsements.html |
2.05Кб |
| 32. Multi-class classification part 9 Visualising random model predictions.mp4 |
65.68Мб |
| 32. Multi-class classification part 9 Visualising random model predictions.srt |
13.52Кб |
| 32. Multi-class CNN's part 6 Trying to fix overfitting by removing layers.mp4 |
129.83Мб |
| 32. Multi-class CNN's part 6 Trying to fix overfitting by removing layers.srt |
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| 33 |
42.12Кб |
| 33. Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.mp4 |
121.02Мб |
| 33. Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.srt |
16.32Кб |
| 33. What patterns is our model learning.mp4 |
127.96Мб |
| 33. What patterns is our model learning.srt |
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| 34 |
756.96Кб |
| 34. Multi-class CNN's part 8 Things you could do to improve your CNN model.mp4 |
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| 34. Multi-class CNN's part 8 Things you could do to improve your CNN model.srt |
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| 34. TensorFlow classification challenge, exercises & extra-curriculum.html |
2.48Кб |
| 35 |
523.70Кб |
| 35. Multi-class CNN's part 9 Making predictions with our model on custom images.mp4 |
118.98Мб |
| 35. Multi-class CNN's part 9 Making predictions with our model on custom images.srt |
11.90Кб |
| 36 |
722.23Кб |
| 36. Saving and loading our trained CNN model.mp4 |
69.28Мб |
| 36. Saving and loading our trained CNN model.srt |
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| 37 |
781.82Кб |
| 37. TensorFlow computer vision and CNNs challenge, exercises & extra-curriculum.html |
2.51Кб |
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1011.34Кб |
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638.66Кб |
| 4 |
410.84Кб |
| 4.1 10 Minutes to pandas.html |
127б |
| 4.1 6 Step Guide.html |
147б |
| 4.1 httpsteachablemachine.withgoogle.com.html |
101б |
| 4.1 Zero to Mastery TensorFlow Deep Learning on GitHub.html |
114б |
| 4.2 Intro to pandas code.html |
191б |
| 4.3 Intro to pandas notes.html |
185б |
| 4. 6 Step Machine Learning Framework.mp4 |
23.45Мб |
| 4. 6 Step Machine Learning Framework.srt |
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| 4. All Course Resources + Notebooks.html |
1.97Кб |
| 4. Becoming One With Data.mp4 |
45.61Мб |
| 4. Becoming One With Data.srt |
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| 4. Course Review.html |
176б |
| 4. Creating a data augmentation layer to use with our model.mp4 |
40.56Мб |
| 4. Creating a data augmentation layer to use with our model.srt |
6.25Кб |
| 4. Creating sample regression data (so we can model it).mp4 |
90.16Мб |
| 4. Creating sample regression data (so we can model it).srt |
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| 4. Discussing the four (actually five) modelling experiments we're running.mp4 |
15.87Мб |
| 4. Discussing the four (actually five) modelling experiments we're running.srt |
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| 4. Exercise Machine Learning Playground.mp4 |
42.56Мб |
| 4. Exercise Machine Learning Playground.srt |
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| 4. Exploring the TensorFlow Hub website for pretrained models.mp4 |
102.96Мб |
| 4. Exploring the TensorFlow Hub website for pretrained models.srt |
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| 4. Introduction to TensorFlow Datasets (TFDS).mp4 |
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| 4. Introduction to TensorFlow Datasets (TFDS).srt |
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| 4. Pandas Introduction.mp4 |
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| 4. Pandas Introduction.srt |
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| 4. Quick Note Correction In Next Video.html |
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| 4. Typical architecture of neural network classification models with TensorFlow.mp4 |
112.73Мб |
| 4. Typical architecture of neural network classification models with TensorFlow.srt |
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| 4. What is deep learning already being used for.mp4 |
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| 4. What is deep learning already being used for.srt |
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| 5 |
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| 5.1 pandas-anatomy-of-a-dataframe.png |
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| 5. Becoming One With Data Part 2.mp4 |
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| 5. Becoming One With Data Part 2.srt |
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| 5. Building and compiling a TensorFlow Hub feature extraction model.mp4 |
135.63Мб |
| 5. Building and compiling a TensorFlow Hub feature extraction model.srt |
18.91Кб |
| 5. Comparing the TensorFlow Keras Sequential API versus the Functional API.mp4 |
26.45Мб |
| 5. Comparing the TensorFlow Keras Sequential API versus the Functional API.srt |
4.03Кб |
| 5. Creating a headless EfficientNetB0 model with data augmentation built in.mp4 |
80.41Мб |
| 5. Creating a headless EfficientNetB0 model with data augmentation built in.srt |
13.45Кб |
| 5. Creating and viewing classification data to model.mp4 |
106.08Мб |
| 5. Creating and viewing classification data to model.srt |
14.39Кб |
| 5. Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4 |
116.71Мб |
| 5. Exploring and becoming one with the data (Food101 from TensorFlow Datasets).srt |
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| 5. How Did We Get Here.mp4 |
30.49Мб |
| 5. How Did We Get Here.srt |
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| 5. NumPy DataTypes and Attributes.mp4 |
78.97Мб |
| 5. NumPy DataTypes and Attributes.srt |
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| 5. Series, Data Frames and CSVs.mp4 |
95.43Мб |
| 5. Series, Data Frames and CSVs.srt |
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| 5. The Final Challenge.html |
176б |
| 5. The major steps in modelling with TensorFlow.mp4 |
181.81Мб |
| 5. The major steps in modelling with TensorFlow.srt |
25.74Кб |
| 5. Types of Machine Learning Problems.mp4 |
60.46Мб |
| 5. Types of Machine Learning Problems.srt |
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| 5. What is and why use TensorFlow.mp4 |
69.16Мб |
| 5. What is and why use TensorFlow.srt |
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| 6 |
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| 6.1 httpsml-playground.com#.html |
88б |
| 6. Becoming One With Data Part 3.mp4 |
39.89Мб |
| 6. Becoming One With Data Part 3.srt |
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| 6. Blowing our previous models out of the water with transfer learning.mp4 |
99.46Мб |
| 6. Blowing our previous models out of the water with transfer learning.srt |
13.66Кб |
| 6. Checking the input and output shapes of our classification data.mp4 |
38.15Мб |
| 6. Checking the input and output shapes of our classification data.srt |
6.57Кб |
| 6. Creating a preprocessing function to prepare our data for modelling.mp4 |
132.19Мб |
| 6. Creating a preprocessing function to prepare our data for modelling.srt |
18.84Кб |
| 6. Creating NumPy Arrays.mp4 |
66.84Мб |
| 6. Creating NumPy Arrays.srt |
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| 6. Creating our first model with the TensorFlow Keras Functional API.mp4 |
132.18Мб |
| 6. Creating our first model with the TensorFlow Keras Functional API.srt |
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| 6. Data from URLs.html |
1.09Кб |
| 6. Exercise YouTube Recommendation Engine.mp4 |
19.43Мб |
| 6. Exercise YouTube Recommendation Engine.srt |
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| 6. Fitting and evaluating our biggest transfer learning model yet.mp4 |
70.15Мб |
| 6. Fitting and evaluating our biggest transfer learning model yet.srt |
11.43Кб |
| 6. Steps in improving a model with TensorFlow part 1.mp4 |
45.82Мб |
| 6. Steps in improving a model with TensorFlow part 1.srt |
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| 6. Types of Data.mp4 |
29.31Мб |
| 6. Types of Data.srt |
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| 6. What is a Tensor.mp4 |
27.58Мб |
| 6. What is a Tensor.srt |
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| 7 |
231.26Кб |
| 7. Batching and preparing our datasets (to make them run fast).mp4 |
132.24Мб |
| 7. Batching and preparing our datasets (to make them run fast).srt |
19.22Кб |
| 7. Building an end to end CNN Model.mp4 |
155.09Мб |
| 7. Building an end to end CNN Model.srt |
26.00Кб |
| 7. Building a not very good classification model with TensorFlow.mp4 |
125.29Мб |
| 7. Building a not very good classification model with TensorFlow.srt |
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| 7. Compiling and fitting our first Functional API model.mp4 |
132.84Мб |
| 7. Compiling and fitting our first Functional API model.srt |
15.76Кб |
| 7. Describing Data with Pandas.mp4 |
75.65Мб |
| 7. Describing Data with Pandas.srt |
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| 7. NumPy Random Seed.mp4 |
51.95Мб |
| 7. NumPy Random Seed.srt |
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| 7. Plotting the loss curves of our ResNet feature extraction model.mp4 |
62.09Мб |
| 7. Plotting the loss curves of our ResNet feature extraction model.srt |
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| 7. Steps in improving a model with TensorFlow part 2.mp4 |
90.23Мб |
| 7. Steps in improving a model with TensorFlow part 2.srt |
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| 7. Types of Evaluation.mp4 |
17.74Мб |
| 7. Types of Evaluation.srt |
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| 7. Types of Machine Learning.mp4 |
22.81Мб |
| 7. Types of Machine Learning.srt |
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| 7. Unfreezing some layers in our base model to prepare for fine-tuning.mp4 |
100.07Мб |
| 7. Unfreezing some layers in our base model to prepare for fine-tuning.srt |
16.60Кб |
| 7. What we're going to cover throughout the course.mp4 |
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| 7. What we're going to cover throughout the course.srt |
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1.93Мб |
| 77 |
101.61Кб |
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551.93Кб |
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205.27Кб |
| 8 |
390.43Кб |
| 8.1 car-sales.csv |
369б |
| 8. Are You Getting It Yet.html |
160б |
| 8. Building and training a pre-trained EfficientNet model on our data.mp4 |
105.93Мб |
| 8. Building and training a pre-trained EfficientNet model on our data.srt |
14.27Кб |
| 8. Exploring what happens when we batch and prefetch our data.mp4 |
63.82Мб |
| 8. Exploring what happens when we batch and prefetch our data.srt |
9.41Кб |
| 8. Features In Data.mp4 |
36.78Мб |
| 8. Features In Data.srt |
6.88Кб |
| 8. Fine-tuning our feature extraction model and evaluating its performance.mp4 |
66.23Мб |
| 8. Fine-tuning our feature extraction model and evaluating its performance.srt |
11.87Кб |
| 8. Getting a feature vector from our trained model.mp4 |
147.62Мб |
| 8. Getting a feature vector from our trained model.srt |
17.74Кб |
| 8. How to approach this course.mp4 |
26.18Мб |
| 8. How to approach this course.srt |
8.24Кб |
| 8. Selecting and Viewing Data with Pandas.mp4 |
72.29Мб |
| 8. Selecting and Viewing Data with Pandas.srt |
15.22Кб |
| 8. Steps in improving a model with TensorFlow part 3.mp4 |
132.94Мб |
| 8. Steps in improving a model with TensorFlow part 3.srt |
16.84Кб |
| 8. Trying to improve our not very good classification model.mp4 |
84.29Мб |
| 8. Trying to improve our not very good classification model.srt |
12.67Кб |
| 8. Using a GPU to run our CNN model 5x faster.mp4 |
114.94Мб |
| 8. Using a GPU to run our CNN model 5x faster.srt |
13.05Кб |
| 8. Viewing Arrays and Matrices.mp4 |
70.66Мб |
| 8. Viewing Arrays and Matrices.srt |
13.86Кб |
| 80 |
835.74Кб |
| 81 |
1.16Мб |
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1.38Мб |
| 83 |
1.50Мб |
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1.58Мб |
| 85 |
256.63Кб |
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379.02Кб |
| 87 |
393.55Кб |
| 88 |
585.56Кб |
| 89 |
1.11Мб |
| 9 |
1.40Мб |
| 9.1 httpswww.mathsisfun.comdatastandard-deviation.html.html |
116б |
| 9. Creating a function to view our model's not so good predictions.mp4 |
160.55Мб |
| 9. Creating a function to view our model's not so good predictions.srt |
18.99Кб |
| 9. Creating modelling callbacks for our feature extraction model.mp4 |
60.79Мб |
| 9. Creating modelling callbacks for our feature extraction model.srt |
9.84Кб |
| 9. Different Types of Transfer Learning.mp4 |
110.57Мб |
| 9. Different Types of Transfer Learning.srt |
15.67Кб |
| 9. Drilling into the concept of a feature vector (a learned representation).mp4 |
51.50Мб |
| 9. Drilling into the concept of a feature vector (a learned representation).srt |
5.39Кб |
| 9. Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).mp4 |
66.94Мб |
| 9. Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).srt |
9.77Кб |
| 9. Manipulating Arrays.mp4 |
80.67Мб |
| 9. Manipulating Arrays.srt |
17.14Кб |
| 9. Modelling - Splitting Data.mp4 |
27.55Мб |
| 9. Modelling - Splitting Data.srt |
7.79Кб |
| 9. Need A Refresher.html |
942б |
| 9. Saving and loading our trained model.mp4 |
57.41Мб |
| 9. Saving and loading our trained model.srt |
8.98Кб |
| 9. Selecting and Viewing Data with Pandas Part 2.mp4 |
106.49Мб |
| 9. Selecting and Viewing Data with Pandas Part 2.srt |
18.95Кб |
| 9. Trying a non-CNN model on our image data.mp4 |
100.56Мб |
| 9. Trying a non-CNN model on our image data.srt |
11.63Кб |
| 9. What Is Machine Learning Round 2.mp4 |
25.51Мб |
| 9. What Is Machine Learning Round 2.srt |
6.25Кб |
| 90 |
1.94Мб |
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1.49Мб |
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1.71Мб |
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1.75Мб |
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750.67Кб |
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951.41Кб |
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1.08Мб |
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1.77Мб |
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1.84Мб |
| 99 |
141.64Кб |
| TutsNode.com.txt |
61б |