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1.1 All course materials and links!.html |
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1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
114B |
1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
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1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
119B |
1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
114B |
1. Become An Alumni.html |
944B |
1. Course Outline.mp4 |
58.03MB |
1. Course Outline.srt |
7.97KB |
1. Introduction to Computer Vision with TensorFlow.mp4 |
75.01MB |
1. Introduction to Computer Vision with TensorFlow.srt |
15.00KB |
1. Introduction to Milestone Project 1 Food Vision Big™.mp4 |
42.32MB |
1. Introduction to Milestone Project 1 Food Vision Big™.srt |
9.17KB |
1. Introduction to neural network classification in TensorFlow.mp4 |
72.81MB |
1. Introduction to neural network classification in TensorFlow.srt |
12.76KB |
1. Introduction to Neural Network Regression with TensorFlow.mp4 |
60.06MB |
1. Introduction to Neural Network Regression with TensorFlow.srt |
11.41KB |
1. Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.mp4 |
61.46MB |
1. Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.srt |
9.78KB |
1. Introduction to Transfer Learning Part 3 Scaling Up.mp4 |
41.53MB |
1. Introduction to Transfer Learning Part 3 Scaling Up.srt |
10.12KB |
1. More Videos Coming Soon!.html |
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1. More Videos Coming Soon!.html |
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1. More Videos Coming Soon!.html |
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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 |
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1. Quick Note Upcoming Videos.html |
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1. Quick Note Upcoming Videos.html |
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1. Quick Note Upcoming Videos.html |
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1. Special Bonus Lecture.html |
3.65KB |
1. What is and why use transfer learning.mp4 |
65.81MB |
1. What is and why use transfer learning.srt |
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1. What is deep learning.mp4 |
34.17MB |
1. What is deep learning.srt |
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10 |
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10.1 car-sales-missing-data.csv |
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10.1 httpswww.mathsisfun.comdatastandard-deviation.html.html |
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10.2 httpsjakevdp.github.ioPythonDataScienceHandbook03.00-introduction-to-pandas.html.html |
146B |
10. Comparing Our Model's Results.mp4 |
143.93MB |
10. Comparing Our Model's Results.srt |
21.56KB |
10. Creating your first tensors with TensorFlow and tf.constant().mp4 |
134.83MB |
10. Creating your first tensors with TensorFlow and tf.constant().srt |
24.75KB |
10. Downloading and preparing the data for Model 1 (1 percent of training data).mp4 |
97.80MB |
10. Downloading and preparing the data for Model 1 (1 percent of training data).srt |
12.98KB |
10. Downloading a pretrained model to make and evaluate predictions with.mp4 |
78.69MB |
10. Downloading a pretrained model to make and evaluate predictions with.srt |
8.91KB |
10. Evaluating a TensorFlow model part 2 (the three datasets).mp4 |
81.56MB |
10. Evaluating a TensorFlow model part 2 (the three datasets).srt |
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10. Improving our non-CNN model by adding more layers.mp4 |
106.47MB |
10. Improving our non-CNN model by adding more layers.srt |
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10. Make our poor classification model work for a regression dataset.mp4 |
123.01MB |
10. Make our poor classification model work for a regression dataset.srt |
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10. Manipulating Arrays 2.mp4 |
67.91MB |
10. Manipulating Arrays 2.srt |
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10. Manipulating Data.mp4 |
105.00MB |
10. Manipulating Data.srt |
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10. Modelling - Picking the Model.mp4 |
23.24MB |
10. Modelling - Picking the Model.srt |
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10. Section Review.mp4 |
5.56MB |
10. Section Review.srt |
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10. Turning on mixed precision training with TensorFlow.mp4 |
107.71MB |
10. Turning on mixed precision training with TensorFlow.srt |
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11.1 httpswww.mathsisfun.comdatastandard-deviation.html.html |
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11.1 pandas-anatomy-of-a-dataframe.png |
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11. Breaking our CNN model down part 1 Becoming one with the data.mp4 |
90.92MB |
11. Breaking our CNN model down part 1 Becoming one with the data.srt |
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11. Building a data augmentation layer to use inside our model.mp4 |
117.46MB |
11. Building a data augmentation layer to use inside our model.srt |
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11. Creating a feature extraction model capable of using mixed precision training.mp4 |
107.92MB |
11. Creating a feature extraction model capable of using mixed precision training.srt |
17.41KB |
11. Creating tensors with TensorFlow and tf.Variable().mp4 |
70.85MB |
11. Creating tensors with TensorFlow and tf.Variable().srt |
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11. Evaluating a TensorFlow model part 3 (getting a model summary).mp4 |
192.79MB |
11. Evaluating a TensorFlow model part 3 (getting a model summary).srt |
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11. Making predictions with our trained model on 25,250 test samples.mp4 |
115.59MB |
11. Making predictions with our trained model on 25,250 test samples.srt |
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11. Manipulating Data 2.mp4 |
86.56MB |
11. Manipulating Data 2.srt |
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11. Modelling - Tuning.mp4 |
15.98MB |
11. Modelling - Tuning.srt |
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11. Non-linearity part 1 Straight lines and non-straight lines.mp4 |
95.62MB |
11. Non-linearity part 1 Straight lines and non-straight lines.srt |
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11. Standard Deviation and Variance.mp4 |
51.13MB |
11. Standard Deviation and Variance.srt |
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11. TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum.html |
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12.1 Pandas video notes.html |
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12.2 Pandas video code.html |
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12. Breaking our CNN model down part 2 Preparing to load our data.mp4 |
109.48MB |
12. Breaking our CNN model down part 2 Preparing to load our data.srt |
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12. Checking to see if our model is using mixed precision training layer by layer.mp4 |
87.67MB |
12. Checking to see if our model is using mixed precision training layer by layer.srt |
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12. Creating random tensors with TensorFlow.mp4 |
88.45MB |
12. Creating random tensors with TensorFlow.srt |
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12. Evaluating a TensorFlow model part 4 (visualising a model's layers).mp4 |
70.28MB |
12. Evaluating a TensorFlow model part 4 (visualising a model's layers).srt |
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12. Manipulating Data 3.mp4 |
91.07MB |
12. Manipulating Data 3.srt |
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12. Modelling - Comparison.mp4 |
44.86MB |
12. Modelling - Comparison.srt |
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12. Non-linearity part 2 Building our first neural network with non-linearity.mp4 |
59.00MB |
12. Non-linearity part 2 Building our first neural network with non-linearity.srt |
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12. Reshape and Transpose.mp4 |
53.57MB |
12. Reshape and Transpose.srt |
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12. Unravelling our test dataset for comparing ground truth labels to predictions.mp4 |
43.81MB |
12. Unravelling our test dataset for comparing ground truth labels to predictions.srt |
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12. Visualising what happens when images pass through our data augmentation layer.mp4 |
119.36MB |
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 |
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13. Assignment Pandas Practice.html |
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13. Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.mp4 |
103.42MB |
13. Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.srt |
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13. Building Model 1 (with a data augmentation layer and 1% of training data).mp4 |
152.95MB |
13. Building Model 1 (with a data augmentation layer and 1% of training data).srt |
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13. Confirming our model's predictions are in the same order as the test labels.mp4 |
50.54MB |
13. Confirming our model's predictions are in the same order as the test labels.srt |
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13. Dot Product vs Element Wise.mp4 |
83.80MB |
13. Dot Product vs Element Wise.srt |
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13. Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp4 |
78.88MB |
13. Evaluating a TensorFlow model part 5 (visualising a model's predictions).srt |
11.92KB |
13. Non-linearity part 3 Upgrading our non-linear model with more layers.mp4 |
123.24MB |
13. Non-linearity part 3 Upgrading our non-linear model with more layers.srt |
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13. Overfitting and Underfitting Definitions.html |
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13. Shuffling the order of tensors.mp4 |
89.86MB |
13. Shuffling the order of tensors.srt |
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13. Training and evaluating a feature extraction model (Food Vision Big™).mp4 |
89.02MB |
13. Training and evaluating a feature extraction model (Food Vision Big™).srt |
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14.1 Course Notes.html |
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14.2 httpscolab.research.google.com.html |
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14. Breaking our CNN model down part 4 Building a baseline CNN model.mp4 |
85.30MB |
14. Breaking our CNN model down part 4 Building a baseline CNN model.srt |
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14. Building Model 2 (with a data augmentation layer and 10% of training data).mp4 |
159.77MB |
14. Building Model 2 (with a data augmentation layer and 10% of training data).srt |
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14. Creating a confusion matrix for our model's 101 different classes.mp4 |
156.60MB |
14. Creating a confusion matrix for our model's 101 different classes.srt |
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14. Creating tensors from NumPy arrays.mp4 |
101.34MB |
14. Creating tensors from NumPy arrays.srt |
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14. Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp4 |
70.37MB |
14. Evaluating a TensorFlow model part 6 (common regression evaluation metrics).srt |
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14. Exercise Nut Butter Store Sales.mp4 |
91.27MB |
14. Exercise Nut Butter Store Sales.srt |
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14. Experimentation.mp4 |
21.30MB |
14. Experimentation.srt |
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14. How To Download The Course Assignments.mp4 |
66.79MB |
14. How To Download The Course Assignments.srt |
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14. Introducing your Milestone Project 1 challenge build a model to beat DeepFood.mp4 |
89.12MB |
14. Introducing your Milestone Project 1 challenge build a model to beat DeepFood.srt |
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14. Non-linearity part 4 Modelling our non-linear data once and for all.mp4 |
96.62MB |
14. Non-linearity part 4 Modelling our non-linear data once and for all.srt |
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15.1 CNN Explainer website.html |
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15. Breaking our CNN model down part 5 Looking inside a Conv2D layer.mp4 |
186.04MB |
15. Breaking our CNN model down part 5 Looking inside a Conv2D layer.srt |
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15. Comparison Operators.mp4 |
26.38MB |
15. Comparison Operators.srt |
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15. Creating a ModelCheckpoint to save our model's weights during training.mp4 |
68.99MB |
15. Creating a ModelCheckpoint to save our model's weights during training.srt |
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15. Evaluating a TensorFlow regression model part 7 (mean absolute error).mp4 |
56.09MB |
15. Evaluating a TensorFlow regression model part 7 (mean absolute error).srt |
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15. Evaluating every individual class in our dataset.mp4 |
131.77MB |
15. Evaluating every individual class in our dataset.srt |
19.30KB |
15. Getting information from your tensors (tensor attributes).mp4 |
87.39MB |
15. Getting information from your tensors (tensor attributes).srt |
16.96KB |
15. Milestone Project 1 Food Vision Big™, exercises and extra-curriculum.html |
2.32KB |
15. Non-linearity part 5 Replicating non-linear activation functions from scratch.mp4 |
146.61MB |
15. Non-linearity part 5 Replicating non-linear activation functions from scratch.srt |
18.28KB |
15. Tools We Will Use.mp4 |
27.34MB |
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 |
77.08MB |
16. Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.srt |
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16. Evaluating a TensorFlow regression model part 7 (mean square error).mp4 |
32.31MB |
16. Evaluating a TensorFlow regression model part 7 (mean square error).srt |
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16. Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp4 |
68.15MB |
16. Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).srt |
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16. Getting great results in less time by tweaking the learning rate.mp4 |
136.78MB |
16. Getting great results in less time by tweaking the learning rate.srt |
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16. Indexing and expanding tensors.mp4 |
86.57MB |
16. Indexing and expanding tensors.srt |
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16. Optional Elements of AI.html |
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16. Plotting our model's F1-scores for each separate class.mp4 |
77.94MB |
16. Plotting our model's F1-scores for each separate class.srt |
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16. Sorting Arrays.mp4 |
32.82MB |
16. Sorting Arrays.srt |
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17.1 numpy-images.zip |
7.27MB |
17.2 NumPy Video code.html |
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17.3 Section Notes.html |
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17. Breaking our CNN model down part 7 Evaluating our CNN's training curves.mp4 |
106.20MB |
17. Breaking our CNN model down part 7 Evaluating our CNN's training curves.srt |
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17. Creating a function to load and prepare images for making predictions.mp4 |
109.54MB |
17. Creating a function to load and prepare images for making predictions.srt |
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17. Loading and comparing saved weights to our existing trained Model 2.mp4 |
62.67MB |
17. Loading and comparing saved weights to our existing trained Model 2.srt |
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17. Manipulating tensors with basic operations.mp4 |
45.22MB |
17. Manipulating tensors with basic operations.srt |
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17. Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4 |
127.26MB |
17. Setting up TensorFlow modelling experiments part 1 (start with a simple model).srt |
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17. Turn Images Into NumPy Arrays.mp4 |
85.98MB |
17. Turn Images Into NumPy Arrays.srt |
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17. Using the TensorFlow History object to plot a model's loss curves.mp4 |
62.12MB |
17. Using the TensorFlow History object to plot a model's loss curves.srt |
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18. Assignment NumPy Practice.html |
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18. Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.mp4 |
130.44MB |
18. Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.srt |
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18. Making predictions on our test images and evaluating them.mp4 |
171.68MB |
18. Making predictions on our test images and evaluating them.srt |
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18. Matrix multiplication with tensors part 1.mp4 |
100.85MB |
18. Matrix multiplication with tensors part 1.srt |
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18. Preparing Model 3 (our first fine-tuned model).mp4 |
198.23MB |
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 |
95.63MB |
18. Setting up TensorFlow modelling experiments part 2 (increasing complexity).srt |
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18. Using callbacks to find a model's ideal learning rate.mp4 |
155.88MB |
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 |
66.08MB |
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 |
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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 |
59.30MB |
19. Discussing the benefits of finding your model's most wrong predictions.srt |
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19. Fitting and evaluating Model 3 (our first fine-tuned model).mp4 |
69.16MB |
19. Fitting and evaluating Model 3 (our first fine-tuned model).srt |
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19. Matrix multiplication with tensors part 2.mp4 |
107.79MB |
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 |
89.01MB |
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2. Downloading and preparing data for our first transfer learning model.mp4 |
132.67MB |
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 |
50.71MB |
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 |
131.54MB |
2. Getting helper functions ready and downloading data to model.srt |
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2. Importing a script full of helper functions (and saving lots of space).mp4 |
89.39MB |
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 |
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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 |
76.65MB |
2. Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.srt |
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2. Join Our Online Classroom!.html |
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2. LinkedIn Endorsements.html |
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2. Making sure we have access to the right GPU for mixed precision training.mp4 |
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2. Making sure we have access to the right GPU for mixed precision training.srt |
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2. Section Overview.mp4 |
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2. What is Machine Learning.mp4 |
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2. Why use deep learning.mp4 |
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20. Breaking our CNN model down part 10 Visualizing our augmented data.mp4 |
157.62MB |
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 |
84.18MB |
20. Comparing our model's results before and after fine-tuning.srt |
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20. How to save a TensorFlow model.mp4 |
92.29MB |
20. How to save a TensorFlow model.srt |
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20. Introducing more classification evaluation methods.mp4 |
42.21MB |
20. Introducing more classification evaluation methods.srt |
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20. Matrix multiplication with tensors part 3.mp4 |
80.62MB |
20. Matrix multiplication with tensors part 3.srt |
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20. Writing code to uncover our model's most wrong predictions.mp4 |
109.60MB |
20. Writing code to uncover our model's most wrong predictions.srt |
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21. Breaking our CNN model down part 11 Training a CNN model on augmented data.mp4 |
94.06MB |
21. Breaking our CNN model down part 11 Training a CNN model on augmented data.srt |
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21. Changing the datatype of tensors.mp4 |
71.39MB |
21. Changing the datatype of tensors.srt |
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21. Downloading and preparing data for our biggest experiment yet (Model 4).mp4 |
56.68MB |
21. Downloading and preparing data for our biggest experiment yet (Model 4).srt |
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21. Finding the accuracy of our classification model.mp4 |
34.07MB |
21. Finding the accuracy of our classification model.srt |
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21. How to load and use a saved TensorFlow model.mp4 |
104.37MB |
21. How to load and use a saved TensorFlow model.srt |
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21. Plotting and visualising the samples our model got most wrong.mp4 |
125.49MB |
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 |
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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 |
103.86MB |
22. Breaking our CNN model down part 12 Discovering the power of shuffling data.srt |
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22. Creating our first confusion matrix (to see where our model is getting confused).mp4 |
65.70MB |
22. Creating our first confusion matrix (to see where our model is getting confused).srt |
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22. Making predictions on and plotting our own custom images.mp4 |
108.30MB |
22. Making predictions on and plotting our own custom images.srt |
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22. Preparing our final modelling experiment (Model 4).mp4 |
96.42MB |
22. Preparing our final modelling experiment (Model 4).srt |
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22. Tensor aggregation (finding the min, max, mean & more).mp4 |
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23. Breaking our CNN model down part 13 Exploring options to improve our model.mp4 |
50.34MB |
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 |
96.84MB |
23. Fine-tuning Model 4 on 100% of the training data and evaluating its results.srt |
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23. Making our confusion matrix prettier.mp4 |
114.12MB |
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23. Putting together what we've learned part 1 (preparing a dataset).mp4 |
143.51MB |
23. Putting together what we've learned part 1 (preparing a dataset).srt |
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23. Tensor troubleshooting example (updating tensor datatypes).mp4 |
69.39MB |
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23. Transfer Learning in TensorFlow Part 3 challenge, exercises and extra-curriculum.html |
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24. Comparing our modelling experiment results in TensorBoard.mp4 |
95.75MB |
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24. Downloading a custom image to make predictions on.mp4 |
53.08MB |
24. Downloading a custom image to make predictions on.srt |
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24. Finding the positional minimum and maximum of a tensor (argmin and argmax).mp4 |
96.50MB |
24. Finding the positional minimum and maximum of a tensor (argmin and argmax).srt |
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24. Putting things together with multi-class classification part 1 Getting the data.mp4 |
87.22MB |
24. Putting things together with multi-class classification part 1 Getting the data.srt |
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24. Putting together what we've learned part 2 (building a regression model).mp4 |
121.38MB |
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25. How to view and delete previous TensorBoard experiments.mp4 |
21.91MB |
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 |
48.65MB |
25. Multi-class classification part 2 Becoming one with the data.srt |
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25. Putting together what we've learned part 3 (improving our regression model).mp4 |
155.11MB |
25. Putting together what we've learned part 3 (improving our regression model).srt |
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25. Squeezing a tensor (removing all 1-dimension axes).mp4 |
30.20MB |
25. Squeezing a tensor (removing all 1-dimension axes).srt |
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25. Writing a helper function to load and preprocessing custom images.mp4 |
105.15MB |
25. Writing a helper function to load and preprocessing custom images.srt |
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26. Making a prediction on a custom image with our trained CNN.mp4 |
99.90MB |
26. Making a prediction on a custom image with our trained CNN.srt |
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26. Multi-class classification part 3 Building a multi-class classification model.mp4 |
142.80MB |
26. Multi-class classification part 3 Building a multi-class classification model.srt |
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26. One-hot encoding tensors.mp4 |
59.73MB |
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26. Preprocessing data with feature scaling part 1 (what is feature scaling).mp4 |
92.51MB |
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26. Transfer Learning in TensorFlow Part 2 challenge, exercises and extra-curriculum.html |
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27. Multi-class classification part 4 Improving performance with normalisation.mp4 |
113.41MB |
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27. Multi-class CNN's part 1 Becoming one with the data.mp4 |
140.19MB |
27. Multi-class CNN's part 1 Becoming one with the data.srt |
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27. Preprocessing data with feature scaling part 2 (normalising our data).mp4 |
97.18MB |
27. Preprocessing data with feature scaling part 2 (normalising our data).srt |
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27. Trying out more tensor math operations.mp4 |
55.93MB |
27. Trying out more tensor math operations.srt |
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28. Exploring TensorFlow and NumPy's compatibility.mp4 |
43.75MB |
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 |
26.77MB |
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 |
72.72MB |
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 |
75.72MB |
28. Preprocessing data with feature scaling part 3 (fitting a model on scaled data).srt |
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29. Making sure our tensor operations run really fast on GPUs.mp4 |
110.91MB |
29. Making sure our tensor operations run really fast on GPUs.srt |
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29. Multi-class classification part 6 Finding the ideal learning rate.mp4 |
73.34MB |
29. Multi-class classification part 6 Finding the ideal learning rate.srt |
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29. Multi-class CNN's part 3 Building a multi-class CNN model.mp4 |
89.24MB |
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29. TensorFlow Regression challenge, exercises & extra-curriculum.html |
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3.1 httpsnumpy.orgdoc.html |
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3.3 NumPy Notes.html |
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3. AIMachine LearningData Science.mp4 |
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3. Anatomy and architecture of a neural network regression model.mp4 |
59.00MB |
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3. Downloading and turning our images into a TensorFlow BatchDataset.mp4 |
173.60MB |
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3. Downloading an image dataset for our first Food Vision model.mp4 |
72.94MB |
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3. Downloading Workbooks and Assignments.html |
967B |
3. Exercise Meet The Community.html |
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3. Getting helper functions ready.mp4 |
31.09MB |
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3. Input and output tensors of classification problems.mp4 |
51.01MB |
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3. Introducing Callbacks in TensorFlow and making a callback to track our models.mp4 |
94.89MB |
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3. Introducing Our Framework.mp4 |
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3. NumPy Introduction.mp4 |
26.86MB |
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3. Outlining the model we're going to build and building a ModelCheckpoint callback.mp4 |
40.61MB |
3. Outlining the model we're going to build and building a ModelCheckpoint callback.srt |
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3. TensorFlow Certificate.html |
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3. What are neural networks.mp4 |
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30. Multi-class classification part 7 Evaluating our model.mp4 |
119.14MB |
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30. Multi-class CNN's part 4 Fitting a multi-class CNN model to the data.mp4 |
59.66MB |
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30. TensorFlow Fundamentals challenge, exercises & extra-curriculum.html |
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31. Multi-class classification part 8 Creating a confusion matrix.mp4 |
40.52MB |
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 |
41.05MB |
31. Multi-class CNN's part 5 Evaluating our multi-class CNN model.srt |
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31. Python + Machine Learning Monthly.html |
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32. LinkedIn Endorsements.html |
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32. Multi-class classification part 9 Visualising random model predictions.mp4 |
65.68MB |
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32. Multi-class CNN's part 6 Trying to fix overfitting by removing layers.mp4 |
129.83MB |
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33. Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.mp4 |
121.02MB |
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33. What patterns is our model learning.mp4 |
127.96MB |
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34. Multi-class CNN's part 8 Things you could do to improve your CNN model.mp4 |
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34. TensorFlow classification challenge, exercises & extra-curriculum.html |
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35. Multi-class CNN's part 9 Making predictions with our model on custom images.mp4 |
118.98MB |
35. Multi-class CNN's part 9 Making predictions with our model on custom images.srt |
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36. Saving and loading our trained CNN model.mp4 |
69.28MB |
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4.1 10 Minutes to pandas.html |
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4.1 6 Step Guide.html |
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4.1 httpsteachablemachine.withgoogle.com.html |
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4.1 Zero to Mastery TensorFlow Deep Learning on GitHub.html |
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4.2 Intro to pandas code.html |
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4.3 Intro to pandas notes.html |
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4. 6 Step Machine Learning Framework.mp4 |
23.45MB |
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4. All Course Resources + Notebooks.html |
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4. Becoming One With Data.mp4 |
45.61MB |
4. Becoming One With Data.srt |
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4. Course Review.html |
176B |
4. Creating a data augmentation layer to use with our model.mp4 |
40.56MB |
4. Creating a data augmentation layer to use with our model.srt |
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4. Creating sample regression data (so we can model it).mp4 |
90.16MB |
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 |
15.87MB |
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4. Exercise Machine Learning Playground.mp4 |
42.56MB |
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4. Exploring the TensorFlow Hub website for pretrained models.mp4 |
102.96MB |
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4. Introduction to TensorFlow Datasets (TFDS).mp4 |
116.84MB |
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4. Pandas Introduction.mp4 |
27.46MB |
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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 |
112.73MB |
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4. What is deep learning already being used for.mp4 |
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5. Building and compiling a TensorFlow Hub feature extraction model.mp4 |
135.63MB |
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5. Comparing the TensorFlow Keras Sequential API versus the Functional API.mp4 |
26.45MB |
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5. Creating a headless EfficientNetB0 model with data augmentation built in.mp4 |
80.41MB |
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5. Creating and viewing classification data to model.mp4 |
106.08MB |
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5. Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4 |
116.71MB |
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5. How Did We Get Here.mp4 |
30.49MB |
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5. NumPy DataTypes and Attributes.mp4 |
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5. Series, Data Frames and CSVs.mp4 |
95.43MB |
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5. The Final Challenge.html |
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5. The major steps in modelling with TensorFlow.mp4 |
181.81MB |
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5. Types of Machine Learning Problems.mp4 |
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5. What is and why use TensorFlow.mp4 |
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6. Becoming One With Data Part 3.mp4 |
39.89MB |
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6. Blowing our previous models out of the water with transfer learning.mp4 |
99.46MB |
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6. Checking the input and output shapes of our classification data.mp4 |
38.15MB |
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6. Creating a preprocessing function to prepare our data for modelling.mp4 |
132.19MB |
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6. Creating NumPy Arrays.mp4 |
66.84MB |
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6. Creating our first model with the TensorFlow Keras Functional API.mp4 |
132.18MB |
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6. Data from URLs.html |
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6. Exercise YouTube Recommendation Engine.mp4 |
19.43MB |
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6. Fitting and evaluating our biggest transfer learning model yet.mp4 |
70.15MB |
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11.43KB |
6. Steps in improving a model with TensorFlow part 1.mp4 |
45.82MB |
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6. Types of Data.mp4 |
29.31MB |
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6. What is a Tensor.mp4 |
27.58MB |
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7. Batching and preparing our datasets (to make them run fast).mp4 |
132.24MB |
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7. Building an end to end CNN Model.mp4 |
155.09MB |
7. Building an end to end CNN Model.srt |
26.00KB |
7. Building a not very good classification model with TensorFlow.mp4 |
125.29MB |
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7. Compiling and fitting our first Functional API model.mp4 |
132.84MB |
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7. Describing Data with Pandas.mp4 |
75.65MB |
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7. NumPy Random Seed.mp4 |
51.95MB |
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7. Plotting the loss curves of our ResNet feature extraction model.mp4 |
62.09MB |
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7. Steps in improving a model with TensorFlow part 2.mp4 |
90.23MB |
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7. Types of Evaluation.mp4 |
17.74MB |
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7. Types of Machine Learning.mp4 |
22.81MB |
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7. Unfreezing some layers in our base model to prepare for fine-tuning.mp4 |
100.07MB |
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7. What we're going to cover throughout the course.mp4 |
29.38MB |
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8.1 car-sales.csv |
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8. Are You Getting It Yet.html |
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8. Building and training a pre-trained EfficientNet model on our data.mp4 |
105.93MB |
8. Building and training a pre-trained EfficientNet model on our data.srt |
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8. Exploring what happens when we batch and prefetch our data.mp4 |
63.82MB |
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8. Features In Data.mp4 |
36.78MB |
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8. Fine-tuning our feature extraction model and evaluating its performance.mp4 |
66.23MB |
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8. Getting a feature vector from our trained model.mp4 |
147.62MB |
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8. How to approach this course.mp4 |
26.18MB |
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8. Selecting and Viewing Data with Pandas.mp4 |
72.29MB |
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8. Steps in improving a model with TensorFlow part 3.mp4 |
132.94MB |
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8. Trying to improve our not very good classification model.mp4 |
84.29MB |
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8. Using a GPU to run our CNN model 5x faster.mp4 |
114.94MB |
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8. Viewing Arrays and Matrices.mp4 |
70.66MB |
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9. Creating a function to view our model's not so good predictions.mp4 |
160.55MB |
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9. Creating modelling callbacks for our feature extraction model.mp4 |
60.79MB |
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9. Different Types of Transfer Learning.mp4 |
110.57MB |
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9. Drilling into the concept of a feature vector (a learned representation).mp4 |
51.50MB |
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9. Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).mp4 |
66.94MB |
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9. Manipulating Arrays.mp4 |
80.67MB |
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9. Modelling - Splitting Data.mp4 |
27.55MB |
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9. Need A Refresher.html |
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9. Saving and loading our trained model.mp4 |
57.41MB |
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9. Selecting and Viewing Data with Pandas Part 2.mp4 |
106.49MB |
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9. Trying a non-CNN model on our image data.mp4 |
100.56MB |
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9. What Is Machine Learning Round 2.mp4 |
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