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种子名称： GetFreeCourses.Co-Udemy-TensorFlow Developer Certificate in 2021 Zero to Mastery
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文件数目： 243个文件
文件大小： 18.52 GB
收录时间： 2021-5-30 15:59
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GetFreeCourses.Co-Udemy-TensorFlow Developer Certificate in 2021 Zero to Mastery.torrent

1. Introduction/1. Course Outline.mp458.03MB

16. Appendix Machine Learning Primer/10. Section Review.mp45.56MB

16. Appendix Machine Learning Primer/2. What is Machine Learning.mp428.31MB

16. Appendix Machine Learning Primer/3. AIMachine LearningData Science.mp419.67MB

16. Appendix Machine Learning Primer/4. Exercise Machine Learning Playground.mp442.56MB

16. Appendix Machine Learning Primer/5. How Did We Get Here.mp430.49MB

16. Appendix Machine Learning Primer/6. Exercise YouTube Recommendation Engine.mp419.43MB

16. Appendix Machine Learning Primer/7. Types of Machine Learning.mp422.81MB

16. Appendix Machine Learning Primer/9. What Is Machine Learning Round 2.mp425.51MB

17. Appendix Machine Learning and Data Science Framework/10. Modelling - Picking the Model.mp423.24MB

17. Appendix Machine Learning and Data Science Framework/11. Modelling - Tuning.mp415.98MB

17. Appendix Machine Learning and Data Science Framework/12. Modelling - Comparison.mp444.86MB

17. Appendix Machine Learning and Data Science Framework/14. Experimentation.mp421.3MB

17. Appendix Machine Learning and Data Science Framework/15. Tools We Will Use.mp427.34MB

17. Appendix Machine Learning and Data Science Framework/2. Section Overview.mp413.34MB

17. Appendix Machine Learning and Data Science Framework/3. Introducing Our Framework.mp411.39MB

17. Appendix Machine Learning and Data Science Framework/4. 6 Step Machine Learning Framework.mp423.45MB

17. Appendix Machine Learning and Data Science Framework/5. Types of Machine Learning Problems.mp460.46MB

17. Appendix Machine Learning and Data Science Framework/6. Types of Data.mp429.31MB

17. Appendix Machine Learning and Data Science Framework/7. Types of Evaluation.mp417.74MB

17. Appendix Machine Learning and Data Science Framework/8. Features In Data.mp436.78MB

17. Appendix Machine Learning and Data Science Framework/9. Modelling - Splitting Data.mp427.55MB

18. Appendix Pandas for Data Analysis/10. Manipulating Data.mp4105MB

18. Appendix Pandas for Data Analysis/11. Manipulating Data 2.mp486.56MB

18. Appendix Pandas for Data Analysis/12. Manipulating Data 3.mp491.07MB

18. Appendix Pandas for Data Analysis/14. How To Download The Course Assignments.mp466.79MB

18. Appendix Pandas for Data Analysis/2. Section Overview.mp410.87MB

18. Appendix Pandas for Data Analysis/4. Pandas Introduction.mp427.46MB

18. Appendix Pandas for Data Analysis/5. Series, Data Frames and CSVs.mp495.43MB

18. Appendix Pandas for Data Analysis/7. Describing Data with Pandas.mp475.65MB

18. Appendix Pandas for Data Analysis/8. Selecting and Viewing Data with Pandas.mp472.29MB

18. Appendix Pandas for Data Analysis/9. Selecting and Viewing Data with Pandas Part 2.mp4106.49MB

19. Appendix NumPy/10. Manipulating Arrays 2.mp467.91MB

19. Appendix NumPy/11. Standard Deviation and Variance.mp451.13MB

19. Appendix NumPy/12. Reshape and Transpose.mp453.57MB

19. Appendix NumPy/13. Dot Product vs Element Wise.mp483.8MB

19. Appendix NumPy/14. Exercise Nut Butter Store Sales.mp491.27MB

19. Appendix NumPy/15. Comparison Operators.mp426.38MB

19. Appendix NumPy/16. Sorting Arrays.mp432.82MB

19. Appendix NumPy/17. Turn Images Into NumPy Arrays.mp485.98MB

19. Appendix NumPy/2. Section Overview.mp413.36MB

19. Appendix NumPy/3. NumPy Introduction.mp426.86MB

19. Appendix NumPy/5. NumPy DataTypes and Attributes.mp478.97MB

19. Appendix NumPy/6. Creating NumPy Arrays.mp466.84MB

19. Appendix NumPy/7. NumPy Random Seed.mp451.95MB

19. Appendix NumPy/8. Viewing Arrays and Matrices.mp470.66MB

19. Appendix NumPy/9. Manipulating Arrays.mp480.67MB

2. Deep Learning and TensorFlow Fundamentals/1. What is deep learning.mp434.17MB

2. Deep Learning and TensorFlow Fundamentals/10. Creating your first tensors with TensorFlow and tf.constant().mp4134.83MB

2. Deep Learning and TensorFlow Fundamentals/11. Creating tensors with TensorFlow and tf.Variable().mp470.85MB

2. Deep Learning and TensorFlow Fundamentals/12. Creating random tensors with TensorFlow.mp488.45MB

2. Deep Learning and TensorFlow Fundamentals/13. Shuffling the order of tensors.mp489.86MB

2. Deep Learning and TensorFlow Fundamentals/14. Creating tensors from NumPy arrays.mp4101.34MB

2. Deep Learning and TensorFlow Fundamentals/15. Getting information from your tensors (tensor attributes).mp487.39MB

2. Deep Learning and TensorFlow Fundamentals/16. Indexing and expanding tensors.mp486.57MB

2. Deep Learning and TensorFlow Fundamentals/17. Manipulating tensors with basic operations.mp445.22MB

2. Deep Learning and TensorFlow Fundamentals/18. Matrix multiplication with tensors part 1.mp4100.85MB

2. Deep Learning and TensorFlow Fundamentals/19. Matrix multiplication with tensors part 2.mp4107.79MB

2. Deep Learning and TensorFlow Fundamentals/2. Why use deep learning.mp462.32MB

2. Deep Learning and TensorFlow Fundamentals/20. Matrix multiplication with tensors part 3.mp480.62MB

2. Deep Learning and TensorFlow Fundamentals/21. Changing the datatype of tensors.mp471.39MB

2. Deep Learning and TensorFlow Fundamentals/22. Tensor aggregation (finding the min, max, mean & more).mp489.58MB

2. Deep Learning and TensorFlow Fundamentals/23. Tensor troubleshooting example (updating tensor datatypes).mp469.39MB

2. Deep Learning and TensorFlow Fundamentals/24. Finding the positional minimum and maximum of a tensor (argmin and argmax).mp496.5MB

2. Deep Learning and TensorFlow Fundamentals/25. Squeezing a tensor (removing all 1-dimension axes).mp430.2MB

2. Deep Learning and TensorFlow Fundamentals/26. One-hot encoding tensors.mp459.73MB

2. Deep Learning and TensorFlow Fundamentals/27. Trying out more tensor math operations.mp455.93MB

2. Deep Learning and TensorFlow Fundamentals/28. Exploring TensorFlow and NumPy's compatibility.mp443.75MB

2. Deep Learning and TensorFlow Fundamentals/29. Making sure our tensor operations run really fast on GPUs.mp4110.91MB

2. Deep Learning and TensorFlow Fundamentals/3. What are neural networks.mp463.43MB

2. Deep Learning and TensorFlow Fundamentals/4. What is deep learning already being used for.mp476.21MB

2. Deep Learning and TensorFlow Fundamentals/5. What is and why use TensorFlow.mp469.16MB

2. Deep Learning and TensorFlow Fundamentals/6. What is a Tensor.mp427.58MB

2. Deep Learning and TensorFlow Fundamentals/7. What we're going to cover throughout the course.mp429.38MB

2. Deep Learning and TensorFlow Fundamentals/8. How to approach this course.mp426.18MB

3. Neural network regression with TensorFlow/1. Introduction to Neural Network Regression with TensorFlow.mp460.06MB

3. Neural network regression with TensorFlow/10. Evaluating a TensorFlow model part 2 (the three datasets).mp481.56MB

3. Neural network regression with TensorFlow/11. Evaluating a TensorFlow model part 3 (getting a model summary).mp4192.79MB

3. Neural network regression with TensorFlow/12. Evaluating a TensorFlow model part 4 (visualising a model's layers).mp470.28MB

3. Neural network regression with TensorFlow/13. Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp478.88MB

3. Neural network regression with TensorFlow/14. Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp470.37MB

3. Neural network regression with TensorFlow/15. Evaluating a TensorFlow regression model part 7 (mean absolute error).mp456.09MB

3. Neural network regression with TensorFlow/16. Evaluating a TensorFlow regression model part 7 (mean square error).mp432.31MB

3. Neural network regression with TensorFlow/17. Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4127.26MB

3. Neural network regression with TensorFlow/18. Setting up TensorFlow modelling experiments part 2 (increasing complexity).mp495.63MB

3. Neural network regression with TensorFlow/19. Comparing and tracking your TensorFlow modelling experiments.mp492.25MB

3. Neural network regression with TensorFlow/2. Inputs and outputs of a neural network regression model.mp457.57MB

3. Neural network regression with TensorFlow/20. How to save a TensorFlow model.mp492.29MB

3. Neural network regression with TensorFlow/21. How to load and use a saved TensorFlow model.mp4104.37MB

3. Neural network regression with TensorFlow/22. (Optional) How to save and download files from Google Colab.mp467.7MB

3. Neural network regression with TensorFlow/23. Putting together what we've learned part 1 (preparing a dataset).mp4143.51MB

3. Neural network regression with TensorFlow/24. Putting together what we've learned part 2 (building a regression model).mp4121.38MB

3. Neural network regression with TensorFlow/25. Putting together what we've learned part 3 (improving our regression model).mp4155.11MB

3. Neural network regression with TensorFlow/26. Preprocessing data with feature scaling part 1 (what is feature scaling).mp492.51MB

3. Neural network regression with TensorFlow/27. Preprocessing data with feature scaling part 2 (normalising our data).mp497.18MB

3. Neural network regression with TensorFlow/28. Preprocessing data with feature scaling part 3 (fitting a model on scaled data).mp475.72MB

3. Neural network regression with TensorFlow/3. Anatomy and architecture of a neural network regression model.mp459MB

3. Neural network regression with TensorFlow/4. Creating sample regression data (so we can model it).mp490.16MB

3. Neural network regression with TensorFlow/5. The major steps in modelling with TensorFlow.mp4181.81MB

3. Neural network regression with TensorFlow/6. Steps in improving a model with TensorFlow part 1.mp445.82MB

3. Neural network regression with TensorFlow/7. Steps in improving a model with TensorFlow part 2.mp490.23MB

3. Neural network regression with TensorFlow/8. Steps in improving a model with TensorFlow part 3.mp4132.94MB

3. Neural network regression with TensorFlow/9. Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).mp466.94MB

4. Neural network classification in TensorFlow/1. Introduction to neural network classification in TensorFlow.mp472.81MB

4. Neural network classification in TensorFlow/10. Make our poor classification model work for a regression dataset.mp4123.01MB

4. Neural network classification in TensorFlow/11. Non-linearity part 1 Straight lines and non-straight lines.mp495.62MB

4. Neural network classification in TensorFlow/12. Non-linearity part 2 Building our first neural network with non-linearity.mp459MB

4. Neural network classification in TensorFlow/13. Non-linearity part 3 Upgrading our non-linear model with more layers.mp4123.24MB

4. Neural network classification in TensorFlow/14. Non-linearity part 4 Modelling our non-linear data once and for all.mp496.62MB

4. Neural network classification in TensorFlow/15. Non-linearity part 5 Replicating non-linear activation functions from scratch.mp4146.61MB

4. Neural network classification in TensorFlow/16. Getting great results in less time by tweaking the learning rate.mp4136.78MB

4. Neural network classification in TensorFlow/17. Using the TensorFlow History object to plot a model's loss curves.mp462.12MB

4. Neural network classification in TensorFlow/18. Using callbacks to find a model's ideal learning rate.mp4155.88MB

4. Neural network classification in TensorFlow/19. Training and evaluating a model with an ideal learning rate.mp489.01MB

4. Neural network classification in TensorFlow/2. Example classification problems (and their inputs and outputs).mp450.71MB

4. Neural network classification in TensorFlow/20. Introducing more classification evaluation methods.mp442.21MB

4. Neural network classification in TensorFlow/21. Finding the accuracy of our classification model.mp434.07MB

4. Neural network classification in TensorFlow/22. Creating our first confusion matrix (to see where our model is getting confused).mp465.7MB

4. Neural network classification in TensorFlow/23. Making our confusion matrix prettier.mp4114.12MB

4. Neural network classification in TensorFlow/24. Putting things together with multi-class classification part 1 Getting the data.mp487.22MB

4. Neural network classification in TensorFlow/25. Multi-class classification part 2 Becoming one with the data.mp448.65MB

4. Neural network classification in TensorFlow/26. Multi-class classification part 3 Building a multi-class classification model.mp4142.8MB

4. Neural network classification in TensorFlow/27. Multi-class classification part 4 Improving performance with normalisation.mp4113.41MB

4. Neural network classification in TensorFlow/28. Multi-class classification part 5 Comparing normalised and non-normalised data.mp426.77MB

4. Neural network classification in TensorFlow/29. Multi-class classification part 6 Finding the ideal learning rate.mp473.34MB

4. Neural network classification in TensorFlow/3. Input and output tensors of classification problems.mp451.01MB

4. Neural network classification in TensorFlow/30. Multi-class classification part 7 Evaluating our model.mp4119.14MB

4. Neural network classification in TensorFlow/31. Multi-class classification part 8 Creating a confusion matrix.mp440.52MB

4. Neural network classification in TensorFlow/32. Multi-class classification part 9 Visualising random model predictions.mp465.68MB

4. Neural network classification in TensorFlow/33. What patterns is our model learning.mp4127.96MB

4. Neural network classification in TensorFlow/4. Typical architecture of neural network classification models with TensorFlow.mp4112.73MB

4. Neural network classification in TensorFlow/5. Creating and viewing classification data to model.mp4106.08MB

4. Neural network classification in TensorFlow/6. Checking the input and output shapes of our classification data.mp438.15MB

4. Neural network classification in TensorFlow/7. Building a not very good classification model with TensorFlow.mp4125.29MB

4. Neural network classification in TensorFlow/8. Trying to improve our not very good classification model.mp484.29MB

4. Neural network classification in TensorFlow/9. Creating a function to view our model's not so good predictions.mp4160.55MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/1. Introduction to Computer Vision with TensorFlow.mp475.01MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/10. Improving our non-CNN model by adding more layers.mp4106.47MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/11. Breaking our CNN model down part 1 Becoming one with the data.mp490.92MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/12. Breaking our CNN model down part 2 Preparing to load our data.mp4109.48MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/13. Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.mp40B

5. Computer Vision and Convolutional Neural Networks in TensorFlow/14. Breaking our CNN model down part 4 Building a baseline CNN model.mp485.3MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/15. Breaking our CNN model down part 5 Looking inside a Conv2D layer.mp4186.04MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/16. Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.mp40B

5. Computer Vision and Convolutional Neural Networks in TensorFlow/17. Breaking our CNN model down part 7 Evaluating our CNN's training curves.mp40B

5. Computer Vision and Convolutional Neural Networks in TensorFlow/18. Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.mp40B

5. Computer Vision and Convolutional Neural Networks in TensorFlow/19. Breaking our CNN model down part 9 Reducing overfitting with data augmentation.mp40B

5. Computer Vision and Convolutional Neural Networks in TensorFlow/2. Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.mp476.65MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/20. Breaking our CNN model down part 10 Visualizing our augmented data.mp4157.62MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/21. Breaking our CNN model down part 11 Training a CNN model on augmented data.mp40B

5. Computer Vision and Convolutional Neural Networks in TensorFlow/22. Breaking our CNN model down part 12 Discovering the power of shuffling data.mp40B

5. Computer Vision and Convolutional Neural Networks in TensorFlow/23. Breaking our CNN model down part 13 Exploring options to improve our model.mp40B

5. Computer Vision and Convolutional Neural Networks in TensorFlow/24. Downloading a custom image to make predictions on.mp453.08MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/25. Writing a helper function to load and preprocessing custom images.mp4105.15MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/26. Making a prediction on a custom image with our trained CNN.mp499.9MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/27. Multi-class CNN's part 1 Becoming one with the data.mp4140.19MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/28. Multi-class CNN's part 2 Preparing our data (turning it into tensors).mp472.72MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/29. Multi-class CNN's part 3 Building a multi-class CNN model.mp489.24MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/3. Downloading an image dataset for our first Food Vision model.mp472.94MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/30. Multi-class CNN's part 4 Fitting a multi-class CNN model to the data.mp459.66MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/31. Multi-class CNN's part 5 Evaluating our multi-class CNN model.mp441.05MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/32. Multi-class CNN's part 6 Trying to fix overfitting by removing layers.mp4129.83MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/33. Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.mp40B

5. Computer Vision and Convolutional Neural Networks in TensorFlow/34. Multi-class CNN's part 8 Things you could do to improve your CNN model.mp443.29MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/35. Multi-class CNN's part 9 Making predictions with our model on custom images.mp40B

5. Computer Vision and Convolutional Neural Networks in TensorFlow/36. Saving and loading our trained CNN model.mp469.28MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/4. Becoming One With Data.mp445.61MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/5. Becoming One With Data Part 2.mp4104.59MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/6. Becoming One With Data Part 3.mp439.89MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/7. Building an end to end CNN Model.mp4155.09MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/8. Using a GPU to run our CNN model 5x faster.mp4114.94MB

5. Computer Vision and Convolutional Neural Networks in TensorFlow/9. Trying a non-CNN model on our image data.mp4100.56MB

6. Transfer Learning in TensorFlow Part 1 Feature extraction/1. What is and why use transfer learning.mp465.81MB

6. Transfer Learning in TensorFlow Part 1 Feature extraction/10. Comparing Our Model's Results.mp4143.93MB

6. Transfer Learning in TensorFlow Part 1 Feature extraction/2. Downloading and preparing data for our first transfer learning model.mp4132.67MB

6. Transfer Learning in TensorFlow Part 1 Feature extraction/3. Introducing Callbacks in TensorFlow and making a callback to track our models.mp494.89MB

6. Transfer Learning in TensorFlow Part 1 Feature extraction/4. Exploring the TensorFlow Hub website for pretrained models.mp4102.96MB

6. Transfer Learning in TensorFlow Part 1 Feature extraction/5. Building and compiling a TensorFlow Hub feature extraction model.mp4135.63MB

6. Transfer Learning in TensorFlow Part 1 Feature extraction/6. Blowing our previous models out of the water with transfer learning.mp499.46MB

6. Transfer Learning in TensorFlow Part 1 Feature extraction/7. Plotting the loss curves of our ResNet feature extraction model.mp462.09MB

6. Transfer Learning in TensorFlow Part 1 Feature extraction/8. Building and training a pre-trained EfficientNet model on our data.mp4105.93MB

6. Transfer Learning in TensorFlow Part 1 Feature extraction/9. Different Types of Transfer Learning.mp4110.57MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/1. Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.mp461.46MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/10. Downloading and preparing the data for Model 1 (1 percent of training data).mp497.8MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/11. Building a data augmentation layer to use inside our model.mp4117.46MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/12. Visualising what happens when images pass through our data augmentation layer.mp4119.36MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/13. Building Model 1 (with a data augmentation layer and 1% of training data).mp4152.95MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/14. Building Model 2 (with a data augmentation layer and 10% of training data).mp4159.77MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/15. Creating a ModelCheckpoint to save our model's weights during training.mp468.99MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/16. Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp468.15MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/17. Loading and comparing saved weights to our existing trained Model 2.mp462.67MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/18. Preparing Model 3 (our first fine-tuned model).mp4198.23MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/19. Fitting and evaluating Model 3 (our first fine-tuned model).mp469.16MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/2. Importing a script full of helper functions (and saving lots of space).mp489.39MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/20. Comparing our model's results before and after fine-tuning.mp484.18MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/21. Downloading and preparing data for our biggest experiment yet (Model 4).mp456.68MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/22. Preparing our final modelling experiment (Model 4).mp496.42MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/23. Fine-tuning Model 4 on 100% of the training data and evaluating its results.mp496.84MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/24. Comparing our modelling experiment results in TensorBoard.mp495.75MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/25. How to view and delete previous TensorBoard experiments.mp421.91MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/3. Downloading and turning our images into a TensorFlow BatchDataset.mp4173.6MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/4. Discussing the four (actually five) modelling experiments we're running.mp415.87MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/5. Comparing the TensorFlow Keras Sequential API versus the Functional API.mp426.45MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/6. Creating our first model with the TensorFlow Keras Functional API.mp4132.18MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/7. Compiling and fitting our first Functional API model.mp4132.84MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/8. Getting a feature vector from our trained model.mp4147.62MB

7. Transfer Learning in TensorFlow Part 2 Fine tuning/9. Drilling into the concept of a feature vector (a learned representation).mp451.5MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/1. Introduction to Transfer Learning Part 3 Scaling Up.mp441.53MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/10. Downloading a pretrained model to make and evaluate predictions with.mp478.69MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/11. Making predictions with our trained model on 25,250 test samples.mp4115.59MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/12. Unravelling our test dataset for comparing ground truth labels to predictions.mp443.81MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/13. Confirming our model's predictions are in the same order as the test labels.mp450.54MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/14. Creating a confusion matrix for our model's 101 different classes.mp4156.6MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/15. Evaluating every individual class in our dataset.mp4131.77MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/16. Plotting our model's F1-scores for each separate class.mp477.94MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/17. Creating a function to load and prepare images for making predictions.mp4109.54MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/18. Making predictions on our test images and evaluating them.mp4171.68MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/19. Discussing the benefits of finding your model's most wrong predictions.mp459.3MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/2. Getting helper functions ready and downloading data to model.mp4131.54MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/20. Writing code to uncover our model's most wrong predictions.mp4109.6MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/21. Plotting and visualising the samples our model got most wrong.mp4125.49MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/22. Making predictions on and plotting our own custom images.mp4108.3MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/3. Outlining the model we're going to build and building a ModelCheckpoint callback.mp440.61MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/4. Creating a data augmentation layer to use with our model.mp440.56MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/5. Creating a headless EfficientNetB0 model with data augmentation built in.mp480.41MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/6. Fitting and evaluating our biggest transfer learning model yet.mp470.15MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/7. Unfreezing some layers in our base model to prepare for fine-tuning.mp4100.07MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/8. Fine-tuning our feature extraction model and evaluating its performance.mp466.23MB

8. Transfer Learning with TensorFlow Part 3 Scaling Up/9. Saving and loading our trained model.mp457.41MB

9. Milestone Project 1 Food Vision Big™/1. Introduction to Milestone Project 1 Food Vision Big™.mp442.32MB

9. Milestone Project 1 Food Vision Big™/10. Turning on mixed precision training with TensorFlow.mp4107.71MB

9. Milestone Project 1 Food Vision Big™/11. Creating a feature extraction model capable of using mixed precision training.mp4107.92MB

9. Milestone Project 1 Food Vision Big™/12. Checking to see if our model is using mixed precision training layer by layer.mp487.67MB

9. Milestone Project 1 Food Vision Big™/13. Training and evaluating a feature extraction model (Food Vision Big™).mp489.02MB

9. Milestone Project 1 Food Vision Big™/14. Introducing your Milestone Project 1 challenge build a model to beat DeepFood.mp489.12MB

9. Milestone Project 1 Food Vision Big™/2. Making sure we have access to the right GPU for mixed precision training.mp488.15MB

9. Milestone Project 1 Food Vision Big™/3. Getting helper functions ready.mp431.09MB

9. Milestone Project 1 Food Vision Big™/4. Introduction to TensorFlow Datasets (TFDS).mp4116.84MB

9. Milestone Project 1 Food Vision Big™/5. Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4116.71MB

9. Milestone Project 1 Food Vision Big™/6. Creating a preprocessing function to prepare our data for modelling.mp4132.19MB

9. Milestone Project 1 Food Vision Big™/7. Batching and preparing our datasets (to make them run fast).mp4132.24MB

9. Milestone Project 1 Food Vision Big™/8. Exploring what happens when we batch and prefetch our data.mp463.82MB

9. Milestone Project 1 Food Vision Big™/9. Creating modelling callbacks for our feature extraction model.mp460.79MB