GetFreeCourses.Co-Udemy-TensorFlow Developer Certificate in 2021 Zero to Mastery

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GetFreeCourses.Co-Udemy-TensorFlow Developer Certificate in 2021 Zero to Mastery

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文件列表

  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/18. Preparing Model 3 (our first fine-tuned model).mp4 207.9 MB
  • 3. Neural network regression with TensorFlow/11. Evaluating a TensorFlow model part 3 (getting a model summary).mp4 202.2 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/15. Breaking our CNN model down part 5 Looking inside a Conv2D layer.mp4 195.1 MB
  • 3. Neural network regression with TensorFlow/5. The major steps in modelling with TensorFlow.mp4 190.6 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/3. Downloading and turning our images into a TensorFlow BatchDataset.mp4 182.0 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/18. Making predictions on our test images and evaluating them.mp4 180.0 MB
  • 4. Neural network classification in TensorFlow/9. Creating a function to view our model's not so good predictions.mp4 168.3 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/14. Building Model 2 (with a data augmentation layer and 10% of training data).mp4 167.5 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/20. Breaking our CNN model down part 10 Visualizing our augmented data.mp4 165.3 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/14. Creating a confusion matrix for our model's 101 different classes.mp4 164.2 MB
  • 4. Neural network classification in TensorFlow/18. Using callbacks to find a model's ideal learning rate.mp4 163.5 MB
  • 3. Neural network regression with TensorFlow/25. Putting together what we've learned part 3 (improving our regression model).mp4 162.6 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/7. Building an end to end CNN Model.mp4 162.6 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/13. Building Model 1 (with a data augmentation layer and 1% of training data).mp4 160.4 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/8. Getting a feature vector from our trained model.mp4 154.8 MB
  • 4. Neural network classification in TensorFlow/15. Non-linearity part 5 Replicating non-linear activation functions from scratch.mp4 153.7 MB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/10. Comparing Our Model's Results.mp4 150.9 MB
  • 3. Neural network regression with TensorFlow/23. Putting together what we've learned part 1 (preparing a dataset).mp4 150.5 MB
  • 4. Neural network classification in TensorFlow/26. Multi-class classification part 3 Building a multi-class classification model.mp4 149.7 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/27. Multi-class CNN's part 1 Becoming one with the data.mp4 147.0 MB
  • 4. Neural network classification in TensorFlow/16. Getting great results in less time by tweaking the learning rate.mp4 143.4 MB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/5. Building and compiling a TensorFlow Hub feature extraction model.mp4 142.2 MB
  • 2. Deep Learning and TensorFlow Fundamentals/10. Creating your first tensors with TensorFlow and tf.constant().mp4 141.4 MB
  • 3. Neural network regression with TensorFlow/8. Steps in improving a model with TensorFlow part 3.mp4 139.4 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/7. Compiling and fitting our first Functional API model.mp4 139.3 MB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/2. Downloading and preparing data for our first transfer learning model.mp4 139.1 MB
  • 9. Milestone Project 1 Food Vision Big™/7. Batching and preparing our datasets (to make them run fast).mp4 138.7 MB
  • 9. Milestone Project 1 Food Vision Big™/6. Creating a preprocessing function to prepare our data for modelling.mp4 138.6 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/6. Creating our first model with the TensorFlow Keras Functional API.mp4 138.6 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/15. Evaluating every individual class in our dataset.mp4 138.2 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/2. Getting helper functions ready and downloading data to model.mp4 137.9 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/32. Multi-class CNN's part 6 Trying to fix overfitting by removing layers.mp4 136.1 MB
  • 4. Neural network classification in TensorFlow/33. What patterns is our model learning.mp4 134.2 MB
  • 3. Neural network regression with TensorFlow/17. Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4 133.4 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/21. Plotting and visualising the samples our model got most wrong.mp4 131.6 MB
  • 4. Neural network classification in TensorFlow/7. Building a not very good classification model with TensorFlow.mp4 131.4 MB
  • 4. Neural network classification in TensorFlow/13. Non-linearity part 3 Upgrading our non-linear model with more layers.mp4 129.2 MB
  • 4. Neural network classification in TensorFlow/10. Make our poor classification model work for a regression dataset.mp4 129.0 MB
  • 3. Neural network regression with TensorFlow/24. Putting together what we've learned part 2 (building a regression model).mp4 127.3 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/12. Visualising what happens when images pass through our data augmentation layer.mp4 125.2 MB
  • 4. Neural network classification in TensorFlow/30. Multi-class classification part 7 Evaluating our model.mp4 124.9 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/11. Building a data augmentation layer to use inside our model.mp4 123.2 MB
  • 9. Milestone Project 1 Food Vision Big™/4. Introduction to TensorFlow Datasets (TFDS).mp4 122.5 MB
  • 9. Milestone Project 1 Food Vision Big™/5. Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4 122.4 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/11. Making predictions with our trained model on 25,250 test samples.mp4 121.2 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/8. Using a GPU to run our CNN model 5x faster.mp4 120.5 MB
  • 4. Neural network classification in TensorFlow/23. Making our confusion matrix prettier.mp4 119.7 MB
  • 4. Neural network classification in TensorFlow/27. Multi-class classification part 4 Improving performance with normalisation.mp4 118.9 MB
  • 4. Neural network classification in TensorFlow/4. Typical architecture of neural network classification models with TensorFlow.mp4 118.2 MB
  • 2. Deep Learning and TensorFlow Fundamentals/29. Making sure our tensor operations run really fast on GPUs.mp4 116.3 MB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/9. Different Types of Transfer Learning.mp4 115.9 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/20. Writing code to uncover our model's most wrong predictions.mp4 114.9 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/17. Creating a function to load and prepare images for making predictions.mp4 114.9 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/12. Breaking our CNN model down part 2 Preparing to load our data.mp4 114.8 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/22. Making predictions on and plotting our own custom images.mp4 113.6 MB
  • 9. Milestone Project 1 Food Vision Big™/11. Creating a feature extraction model capable of using mixed precision training.mp4 113.2 MB
  • 2. Deep Learning and TensorFlow Fundamentals/19. Matrix multiplication with tensors part 2.mp4 113.0 MB
  • 9. Milestone Project 1 Food Vision Big™/10. Turning on mixed precision training with TensorFlow.mp4 112.9 MB
  • 18. Appendix Pandas for Data Analysis/9. Selecting and Viewing Data with Pandas Part 2.mp4 111.7 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/10. Improving our non-CNN model by adding more layers.mp4 111.6 MB
  • 4. Neural network classification in TensorFlow/5. Creating and viewing classification data to model.mp4 111.2 MB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/8. Building and training a pre-trained EfficientNet model on our data.mp4 111.1 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/25. Writing a helper function to load and preprocessing custom images.mp4 110.3 MB
  • 18. Appendix Pandas for Data Analysis/10. Manipulating Data.mp4 110.1 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/5. Becoming One With Data Part 2.mp4 109.7 MB
  • 3. Neural network regression with TensorFlow/21. How to load and use a saved TensorFlow model.mp4 109.4 MB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/4. Exploring the TensorFlow Hub website for pretrained models.mp4 108.0 MB
  • 2. Deep Learning and TensorFlow Fundamentals/14. Creating tensors from NumPy arrays.mp4 106.3 MB
  • 2. Deep Learning and TensorFlow Fundamentals/18. Matrix multiplication with tensors part 1.mp4 105.8 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/9. Trying a non-CNN model on our image data.mp4 105.4 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/7. Unfreezing some layers in our base model to prepare for fine-tuning.mp4 104.9 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/26. Making a prediction on a custom image with our trained CNN.mp4 104.8 MB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/6. Blowing our previous models out of the water with transfer learning.mp4 104.3 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/10. Downloading and preparing the data for Model 1 (1 percent of training data).mp4 102.6 MB
  • 3. Neural network regression with TensorFlow/27. Preprocessing data with feature scaling part 2 (normalising our data).mp4 101.9 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/23. Fine-tuning Model 4 on 100% of the training data and evaluating its results.mp4 101.5 MB
  • 4. Neural network classification in TensorFlow/14. Non-linearity part 4 Modelling our non-linear data once and for all.mp4 101.3 MB
  • 2. Deep Learning and TensorFlow Fundamentals/24. Finding the positional minimum and maximum of a tensor (argmin and argmax).mp4 101.2 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/22. Preparing our final modelling experiment (Model 4).mp4 101.1 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/24. Comparing our modelling experiment results in TensorBoard.mp4 100.4 MB
  • 3. Neural network regression with TensorFlow/18. Setting up TensorFlow modelling experiments part 2 (increasing complexity).mp4 100.3 MB
  • 4. Neural network classification in TensorFlow/11. Non-linearity part 1 Straight lines and non-straight lines.mp4 100.3 MB
  • 18. Appendix Pandas for Data Analysis/5. Series, Data Frames and CSVs.mp4 100.1 MB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/3. Introducing Callbacks in TensorFlow and making a callback to track our models.mp4 99.5 MB
  • 3. Neural network regression with TensorFlow/26. Preprocessing data with feature scaling part 1 (what is feature scaling).mp4 97.0 MB
  • 3. Neural network regression with TensorFlow/20. How to save a TensorFlow model.mp4 96.8 MB
  • 3. Neural network regression with TensorFlow/19. Comparing and tracking your TensorFlow modelling experiments.mp4 96.7 MB
  • 19. Appendix NumPy/14. Exercise Nut Butter Store Sales.mp4 95.7 MB
  • 18. Appendix Pandas for Data Analysis/12. Manipulating Data 3.mp4 95.5 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/11. Breaking our CNN model down part 1 Becoming one with the data.mp4 95.3 MB
  • 3. Neural network regression with TensorFlow/7. Steps in improving a model with TensorFlow part 2.mp4 94.6 MB
  • 3. Neural network regression with TensorFlow/4. Creating sample regression data (so we can model it).mp4 94.5 MB
  • 2. Deep Learning and TensorFlow Fundamentals/13. Shuffling the order of tensors.mp4 94.2 MB
  • 2. Deep Learning and TensorFlow Fundamentals/22. Tensor aggregation (finding the min, max, mean & more).mp4 93.9 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/2. Importing a script full of helper functions (and saving lots of space).mp4 93.7 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/29. Multi-class CNN's part 3 Building a multi-class CNN model.mp4 93.6 MB
  • 9. Milestone Project 1 Food Vision Big™/14. Introducing your Milestone Project 1 challenge build a model to beat DeepFood.mp4 93.4 MB
  • 9. Milestone Project 1 Food Vision Big™/13. Training and evaluating a feature extraction model (Food Vision Big™).mp4 93.3 MB
  • 4. Neural network classification in TensorFlow/19. Training and evaluating a model with an ideal learning rate.mp4 93.3 MB
  • 2. Deep Learning and TensorFlow Fundamentals/12. Creating random tensors with TensorFlow.mp4 92.7 MB
  • 9. Milestone Project 1 Food Vision Big™/2. Making sure we have access to the right GPU for mixed precision training.mp4 92.4 MB
  • 9. Milestone Project 1 Food Vision Big™/12. Checking to see if our model is using mixed precision training layer by layer.mp4 91.9 MB
  • 2. Deep Learning and TensorFlow Fundamentals/15. Getting information from your tensors (tensor attributes).mp4 91.6 MB
  • 4. Neural network classification in TensorFlow/24. Putting things together with multi-class classification part 1 Getting the data.mp4 91.5 MB
  • 2. Deep Learning and TensorFlow Fundamentals/16. Indexing and expanding tensors.mp4 90.8 MB
  • 18. Appendix Pandas for Data Analysis/11. Manipulating Data 2.mp4 90.8 MB
  • 19. Appendix NumPy/17. Turn Images Into NumPy Arrays.mp4 90.2 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/14. Breaking our CNN model down part 4 Building a baseline CNN model.mp4 89.4 MB
  • 4. Neural network classification in TensorFlow/8. Trying to improve our not very good classification model.mp4 88.4 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/20. Comparing our model's results before and after fine-tuning.mp4 88.3 MB
  • 19. Appendix NumPy/13. Dot Product vs Element Wise.mp4 87.9 MB
  • 3. Neural network regression with TensorFlow/10. Evaluating a TensorFlow model part 2 (the three datasets).mp4 85.5 MB
  • 19. Appendix NumPy/9. Manipulating Arrays.mp4 84.6 MB
  • 2. Deep Learning and TensorFlow Fundamentals/20. Matrix multiplication with tensors part 3.mp4 84.5 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/5. Creating a headless EfficientNetB0 model with data augmentation built in.mp4 84.3 MB
  • 19. Appendix NumPy/5. NumPy DataTypes and Attributes.mp4 82.8 MB
  • 3. Neural network regression with TensorFlow/13. Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp4 82.7 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/10. Downloading a pretrained model to make and evaluate predictions with.mp4 82.5 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/16. Plotting our model's F1-scores for each separate class.mp4 81.7 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/2. Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.mp4 80.4 MB
  • 2. Deep Learning and TensorFlow Fundamentals/4. What is deep learning already being used for.mp4 79.9 MB
  • 3. Neural network regression with TensorFlow/28. Preprocessing data with feature scaling part 3 (fitting a model on scaled data).mp4 79.4 MB
  • 18. Appendix Pandas for Data Analysis/7. Describing Data with Pandas.mp4 79.3 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/1. Introduction to Computer Vision with TensorFlow.mp4 78.7 MB
  • 4. Neural network classification in TensorFlow/29. Multi-class classification part 6 Finding the ideal learning rate.mp4 76.9 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/3. Downloading an image dataset for our first Food Vision model.mp4 76.5 MB
  • 4. Neural network classification in TensorFlow/1. Introduction to neural network classification in TensorFlow.mp4 76.3 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/28. Multi-class CNN's part 2 Preparing our data (turning it into tensors).mp4 76.2 MB
  • 18. Appendix Pandas for Data Analysis/8. Selecting and Viewing Data with Pandas.mp4 75.8 MB
  • 2. Deep Learning and TensorFlow Fundamentals/21. Changing the datatype of tensors.mp4 74.9 MB
  • 2. Deep Learning and TensorFlow Fundamentals/11. Creating tensors with TensorFlow and tf.Variable().mp4 74.3 MB
  • 19. Appendix NumPy/8. Viewing Arrays and Matrices.mp4 74.1 MB
  • 3. Neural network regression with TensorFlow/14. Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp4 73.8 MB
  • 3. Neural network regression with TensorFlow/12. Evaluating a TensorFlow model part 4 (visualising a model's layers).mp4 73.7 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/6. Fitting and evaluating our biggest transfer learning model yet.mp4 73.6 MB
  • 2. Deep Learning and TensorFlow Fundamentals/23. Tensor troubleshooting example (updating tensor datatypes).mp4 72.8 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/36. Saving and loading our trained CNN model.mp4 72.6 MB
  • 2. Deep Learning and TensorFlow Fundamentals/5. What is and why use TensorFlow.mp4 72.5 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/19. Fitting and evaluating Model 3 (our first fine-tuned model).mp4 72.5 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/15. Creating a ModelCheckpoint to save our model's weights during training.mp4 72.3 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/16. Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp4 71.5 MB
  • 19. Appendix NumPy/10. Manipulating Arrays 2.mp4 71.2 MB
  • 3. Neural network regression with TensorFlow/22. (Optional) How to save and download files from Google Colab.mp4 71.0 MB
  • 3. Neural network regression with TensorFlow/9. Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).mp4 70.2 MB
  • 19. Appendix NumPy/6. Creating NumPy Arrays.mp4 70.1 MB
  • 18. Appendix Pandas for Data Analysis/14. How To Download The Course Assignments.mp4 70.0 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/8. Fine-tuning our feature extraction model and evaluating its performance.mp4 69.4 MB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/1. What is and why use transfer learning.mp4 69.0 MB
  • 4. Neural network classification in TensorFlow/22. Creating our first confusion matrix (to see where our model is getting confused).mp4 68.9 MB
  • 4. Neural network classification in TensorFlow/32. Multi-class classification part 9 Visualising random model predictions.mp4 68.9 MB
  • 9. Milestone Project 1 Food Vision Big™/8. Exploring what happens when we batch and prefetch our data.mp4 66.9 MB
  • 2. Deep Learning and TensorFlow Fundamentals/3. What are neural networks.mp4 66.5 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/17. Loading and comparing saved weights to our existing trained Model 2.mp4 65.7 MB
  • 2. Deep Learning and TensorFlow Fundamentals/2. Why use deep learning.mp4 65.3 MB
  • 4. Neural network classification in TensorFlow/17. Using the TensorFlow History object to plot a model's loss curves.mp4 65.1 MB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/7. Plotting the loss curves of our ResNet feature extraction model.mp4 65.1 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/1. Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.mp4 64.4 MB
  • 9. Milestone Project 1 Food Vision Big™/9. Creating modelling callbacks for our feature extraction model.mp4 63.7 MB
  • 17. Appendix Machine Learning and Data Science Framework/5. Types of Machine Learning Problems.mp4 63.4 MB
  • 3. Neural network regression with TensorFlow/1. Introduction to Neural Network Regression with TensorFlow.mp4 63.0 MB
  • 2. Deep Learning and TensorFlow Fundamentals/26. One-hot encoding tensors.mp4 62.6 MB
  • 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.mp4 62.6 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/19. Discussing the benefits of finding your model's most wrong predictions.mp4 62.2 MB
  • 3. Neural network regression with TensorFlow/3. Anatomy and architecture of a neural network regression model.mp4 61.9 MB
  • 4. Neural network classification in TensorFlow/12. Non-linearity part 2 Building our first neural network with non-linearity.mp4 61.9 MB
  • 1. Introduction/1. Course Outline.mp4 60.9 MB
  • 3. Neural network regression with TensorFlow/2. Inputs and outputs of a neural network regression model.mp4 60.4 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/9. Saving and loading our trained model.mp4 60.2 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/21. Downloading and preparing data for our biggest experiment yet (Model 4).mp4 59.4 MB
  • 3. Neural network regression with TensorFlow/15. Evaluating a TensorFlow regression model part 7 (mean absolute error).mp4 58.8 MB
  • 2. Deep Learning and TensorFlow Fundamentals/27. Trying out more tensor math operations.mp4 58.6 MB
  • 19. Appendix NumPy/12. Reshape and Transpose.mp4 56.2 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/24. Downloading a custom image to make predictions on.mp4 55.7 MB
  • 19. Appendix NumPy/7. NumPy Random Seed.mp4 54.5 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/9. Drilling into the concept of a feature vector (a learned representation).mp4 54.0 MB
  • 19. Appendix NumPy/11. Standard Deviation and Variance.mp4 53.6 MB
  • 4. Neural network classification in TensorFlow/3. Input and output tensors of classification problems.mp4 53.5 MB
  • 4. Neural network classification in TensorFlow/2. Example classification problems (and their inputs and outputs).mp4 53.2 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/13. Confirming our model's predictions are in the same order as the test labels.mp4 53.0 MB
  • 4. Neural network classification in TensorFlow/25. Multi-class classification part 2 Becoming one with the data.mp4 51.0 MB
  • 3. Neural network regression with TensorFlow/6. Steps in improving a model with TensorFlow part 1.mp4 48.0 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/4. Becoming One With Data.mp4 47.8 MB
  • 2. Deep Learning and TensorFlow Fundamentals/17. Manipulating tensors with basic operations.mp4 47.4 MB
  • 17. Appendix Machine Learning and Data Science Framework/12. Modelling - Comparison.mp4 47.0 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/12. Unravelling our test dataset for comparing ground truth labels to predictions.mp4 45.9 MB
  • 2. Deep Learning and TensorFlow Fundamentals/28. Exploring TensorFlow and NumPy's compatibility.mp4 45.9 MB
  • 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.mp4 45.4 MB
  • 16. Appendix Machine Learning Primer/4. Exercise Machine Learning Playground.mp4 44.6 MB
  • 9. Milestone Project 1 Food Vision Big™/1. Introduction to Milestone Project 1 Food Vision Big™.mp4 44.4 MB
  • 4. Neural network classification in TensorFlow/20. Introducing more classification evaluation methods.mp4 44.3 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/1. Introduction to Transfer Learning Part 3 Scaling Up.mp4 43.5 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/31. Multi-class CNN's part 5 Evaluating our multi-class CNN model.mp4 43.0 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/3. Outlining the model we're going to build and building a ModelCheckpoint callback.mp4 42.6 MB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/4. Creating a data augmentation layer to use with our model.mp4 42.5 MB
  • 4. Neural network classification in TensorFlow/31. Multi-class classification part 8 Creating a confusion matrix.mp4 42.5 MB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/6. Becoming One With Data Part 3.mp4 41.8 MB
  • 4. Neural network classification in TensorFlow/6. Checking the input and output shapes of our classification data.mp4 40.0 MB
  • 17. Appendix Machine Learning and Data Science Framework/8. Features In Data.mp4 38.6 MB
  • 2. Deep Learning and TensorFlow Fundamentals/1. What is deep learning.mp4 35.8 MB
  • 4. Neural network classification in TensorFlow/21. Finding the accuracy of our classification model.mp4 35.7 MB
  • 19. Appendix NumPy/16. Sorting Arrays.mp4 34.4 MB
  • 3. Neural network regression with TensorFlow/16. Evaluating a TensorFlow regression model part 7 (mean square error).mp4 33.9 MB
  • 9. Milestone Project 1 Food Vision Big™/3. Getting helper functions ready.mp4 32.6 MB
  • 16. Appendix Machine Learning Primer/5. How Did We Get Here.mp4 32.0 MB
  • 2. Deep Learning and TensorFlow Fundamentals/25. Squeezing a tensor (removing all 1-dimension axes).mp4 31.7 MB
  • 2. Deep Learning and TensorFlow Fundamentals/7. What we're going to cover throughout the course.mp4 30.8 MB
  • 17. Appendix Machine Learning and Data Science Framework/6. Types of Data.mp4 30.7 MB
  • 16. Appendix Machine Learning Primer/2. What is Machine Learning.mp4 29.7 MB
  • 2. Deep Learning and TensorFlow Fundamentals/6. What is a Tensor.mp4 28.9 MB
  • 17. Appendix Machine Learning and Data Science Framework/9. Modelling - Splitting Data.mp4 28.9 MB
  • 18. Appendix Pandas for Data Analysis/4. Pandas Introduction.mp4 28.8 MB
  • 17. Appendix Machine Learning and Data Science Framework/15. Tools We Will Use.mp4 28.7 MB
  • 19. Appendix NumPy/3. NumPy Introduction.mp4 28.2 MB
  • 4. Neural network classification in TensorFlow/28. Multi-class classification part 5 Comparing normalised and non-normalised data.mp4 28.1 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/5. Comparing the TensorFlow Keras Sequential API versus the Functional API.mp4 27.7 MB
  • 19. Appendix NumPy/15. Comparison Operators.mp4 27.7 MB
  • 2. Deep Learning and TensorFlow Fundamentals/8. How to approach this course.mp4 27.4 MB
  • 16. Appendix Machine Learning Primer/9. What Is Machine Learning Round 2.mp4 26.8 MB
  • 17. Appendix Machine Learning and Data Science Framework/4. 6 Step Machine Learning Framework.mp4 24.6 MB
  • 17. Appendix Machine Learning and Data Science Framework/10. Modelling - Picking the Model.mp4 24.4 MB
  • 16. Appendix Machine Learning Primer/7. Types of Machine Learning.mp4 23.9 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/25. How to view and delete previous TensorBoard experiments.mp4 23.0 MB
  • 17. Appendix Machine Learning and Data Science Framework/14. Experimentation.mp4 22.3 MB
  • 16. Appendix Machine Learning Primer/3. AIMachine LearningData Science.mp4 20.6 MB
  • 16. Appendix Machine Learning Primer/6. Exercise YouTube Recommendation Engine.mp4 20.4 MB
  • 17. Appendix Machine Learning and Data Science Framework/7. Types of Evaluation.mp4 18.6 MB
  • 17. Appendix Machine Learning and Data Science Framework/11. Modelling - Tuning.mp4 16.8 MB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/4. Discussing the four (actually five) modelling experiments we're running.mp4 16.6 MB
  • 19. Appendix NumPy/2. Section Overview.mp4 14.0 MB
  • 17. Appendix Machine Learning and Data Science Framework/2. Section Overview.mp4 14.0 MB
  • 17. Appendix Machine Learning and Data Science Framework/3. Introducing Our Framework.mp4 11.9 MB
  • 18. Appendix Pandas for Data Analysis/2. Section Overview.mp4 11.4 MB
  • 19. Appendix NumPy/17.1 numpy-images.zip 7.6 MB
  • 16. Appendix Machine Learning Primer/10. Section Review.mp4 5.8 MB
  • 18. Appendix Pandas for Data Analysis/11.1 pandas-anatomy-of-a-dataframe.png 341.2 kB
  • 18. Appendix Pandas for Data Analysis/5.1 pandas-anatomy-of-a-dataframe.png 341.2 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/7. Building an end to end CNN Model.srt 26.6 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/18. Preparing Model 3 (our first fine-tuned model).srt 26.5 kB
  • 3. Neural network regression with TensorFlow/5. The major steps in modelling with TensorFlow.srt 26.4 kB
  • 4. Neural network classification in TensorFlow/18. Using callbacks to find a model's ideal learning rate.srt 25.5 kB
  • 2. Deep Learning and TensorFlow Fundamentals/10. Creating your first tensors with TensorFlow and tf.constant().srt 25.3 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/18. Making predictions on our test images and evaluating them.srt 24.0 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/14. Building Model 2 (with a data augmentation layer and 10% of training data).srt 24.0 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/15. Breaking our CNN model down part 5 Looking inside a Conv2D layer.srt 23.3 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/27. Multi-class CNN's part 1 Becoming one with the data.srt 23.2 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/13. Building Model 1 (with a data augmentation layer and 1% of training data).srt 23.0 kB
  • 9. Milestone Project 1 Food Vision Big™/5. Exploring and becoming one with the data (Food101 from TensorFlow Datasets).srt 22.9 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/3. Downloading and turning our images into a TensorFlow BatchDataset.srt 22.5 kB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/10. Comparing Our Model's Results.srt 22.1 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/20. Breaking our CNN model down part 10 Visualizing our augmented data.srt 22.1 kB
  • 3. Neural network regression with TensorFlow/11. Evaluating a TensorFlow model part 3 (getting a model summary).srt 22.0 kB
  • 4. Neural network classification in TensorFlow/26. Multi-class classification part 3 Building a multi-class classification model.srt 21.6 kB
  • 4. Neural network classification in TensorFlow/33. What patterns is our model learning.srt 21.3 kB
  • 19. Appendix NumPy/5. NumPy DataTypes and Attributes.srt 20.5 kB
  • 4. Neural network classification in TensorFlow/16. Getting great results in less time by tweaking the learning rate.srt 19.8 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/15. Evaluating every individual class in our dataset.srt 19.8 kB
  • 9. Milestone Project 1 Food Vision Big™/7. Batching and preparing our datasets (to make them run fast).srt 19.7 kB
  • 4. Neural network classification in TensorFlow/9. Creating a function to view our model's not so good predictions.srt 19.4 kB
  • 18. Appendix Pandas for Data Analysis/9. Selecting and Viewing Data with Pandas Part 2.srt 19.4 kB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/5. Building and compiling a TensorFlow Hub feature extraction model.srt 19.4 kB
  • 9. Milestone Project 1 Food Vision Big™/6. Creating a preprocessing function to prepare our data for modelling.srt 19.3 kB
  • 3. Neural network regression with TensorFlow/25. Putting together what we've learned part 3 (improving our regression model).srt 19.3 kB
  • 3. Neural network regression with TensorFlow/23. Putting together what we've learned part 1 (preparing a dataset).srt 19.1 kB
  • 18. Appendix Pandas for Data Analysis/10. Manipulating Data.srt 19.0 kB
  • 18. Appendix Pandas for Data Analysis/5. Series, Data Frames and CSVs.srt 18.9 kB
  • 4. Neural network classification in TensorFlow/15. Non-linearity part 5 Replicating non-linear activation functions from scratch.srt 18.7 kB
  • 4. Neural network classification in TensorFlow/23. Making our confusion matrix prettier.srt 18.7 kB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/2. Downloading and preparing data for our first transfer learning model.srt 18.5 kB
  • 3. Neural network regression with TensorFlow/24. Putting together what we've learned part 2 (building a regression model).srt 18.4 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/8. Getting a feature vector from our trained model.srt 18.2 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/2. Getting helper functions ready and downloading data to model.srt 18.2 kB
  • 9. Milestone Project 1 Food Vision Big™/4. Introduction to TensorFlow Datasets (TFDS).srt 18.0 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/14. Creating a confusion matrix for our model's 101 different classes.srt 17.9 kB
  • 3. Neural network regression with TensorFlow/17. Setting up TensorFlow modelling experiments part 1 (start with a simple model).srt 17.9 kB
  • 9. Milestone Project 1 Food Vision Big™/11. Creating a feature extraction model capable of using mixed precision training.srt 17.8 kB
  • 19. Appendix NumPy/14. Exercise Nut Butter Store Sales.srt 17.8 kB
  • 2. Deep Learning and TensorFlow Fundamentals/19. Matrix multiplication with tensors part 2.srt 17.8 kB
  • 19. Appendix NumPy/9. Manipulating Arrays.srt 17.6 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/20. Writing code to uncover our model's most wrong predictions.srt 17.4 kB
  • 2. Deep Learning and TensorFlow Fundamentals/15. Getting information from your tensors (tensor attributes).srt 17.4 kB
  • 4. Neural network classification in TensorFlow/30. Multi-class classification part 7 Evaluating our model.srt 17.4 kB
  • 2. Deep Learning and TensorFlow Fundamentals/16. Indexing and expanding tensors.srt 17.4 kB
  • 3. Neural network regression with TensorFlow/8. Steps in improving a model with TensorFlow part 3.srt 17.2 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/7. Unfreezing some layers in our base model to prepare for fine-tuning.srt 17.0 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/12. Breaking our CNN model down part 2 Preparing to load our data.srt 16.9 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/32. Multi-class CNN's part 6 Trying to fix overfitting by removing layers.srt 16.8 kB
  • 4. Neural network classification in TensorFlow/10. Make our poor classification model work for a regression dataset.srt 16.7 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/11. Making predictions with our trained model on 25,250 test samples.srt 16.6 kB
  • 4. Neural network classification in TensorFlow/27. Multi-class classification part 4 Improving performance with normalisation.srt 16.6 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/11. Building a data augmentation layer to use inside our model.srt 16.5 kB
  • 3. Neural network regression with TensorFlow/4. Creating sample regression data (so we can model it).srt 16.5 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/5. Becoming One With Data Part 2.srt 16.4 kB
  • 4. Neural network classification in TensorFlow/7. Building a not very good classification model with TensorFlow.srt 16.4 kB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/1. What is and why use transfer learning.srt 16.3 kB
  • 19. Appendix NumPy/13. Dot Product vs Element Wise.srt 16.3 kB
  • 3. Neural network regression with TensorFlow/18. Setting up TensorFlow modelling experiments part 2 (increasing complexity).srt 16.2 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/6. Creating our first model with the TensorFlow Keras Functional API.srt 16.2 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/17. Creating a function to load and prepare images for making predictions.srt 16.2 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/7. Compiling and fitting our first Functional API model.srt 16.1 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/24. Comparing our modelling experiment results in TensorBoard.srt 16.1 kB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/9. Different Types of Transfer Learning.srt 16.1 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/26. Making a prediction on a custom image with our trained CNN.srt 15.8 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/21. Plotting and visualising the samples our model got most wrong.srt 15.8 kB
  • 18. Appendix Pandas for Data Analysis/8. Selecting and Viewing Data with Pandas.srt 15.6 kB
  • 2. Deep Learning and TensorFlow Fundamentals/18. Matrix multiplication with tensors part 1.srt 15.6 kB
  • 2. Deep Learning and TensorFlow Fundamentals/14. Creating tensors from NumPy arrays.srt 15.4 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/1. Introduction to Computer Vision with TensorFlow.srt 15.4 kB
  • 4. Neural network classification in TensorFlow/29. Multi-class classification part 6 Finding the ideal learning rate.srt 15.3 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/22. Preparing our final modelling experiment (Model 4).srt 15.2 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/23. Fine-tuning Model 4 on 100% of the training data and evaluating its results.srt 15.2 kB
  • 18. Appendix Pandas for Data Analysis/11. Manipulating Data 2.srt 15.2 kB
  • 2. Deep Learning and TensorFlow Fundamentals/3. What are neural networks.srt 15.0 kB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/4. Exploring the TensorFlow Hub website for pretrained models.srt 15.0 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/22. Making predictions on and plotting our own custom images.srt 15.0 kB
  • 4. Neural network classification in TensorFlow/4. Typical architecture of neural network classification models with TensorFlow.srt 15.0 kB
  • 17. Appendix Machine Learning and Data Science Framework/5. Types of Machine Learning Problems.srt 14.8 kB
  • 2. Deep Learning and TensorFlow Fundamentals/29. Making sure our tensor operations run really fast on GPUs.srt 14.8 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/12. Visualising what happens when images pass through our data augmentation layer.srt 14.7 kB
  • 4. Neural network classification in TensorFlow/5. Creating and viewing classification data to model.srt 14.7 kB
  • 4. Neural network classification in TensorFlow/13. Non-linearity part 3 Upgrading our non-linear model with more layers.srt 14.7 kB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/8. Building and training a pre-trained EfficientNet model on our data.srt 14.6 kB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/3. Introducing Callbacks in TensorFlow and making a callback to track our models.srt 14.6 kB
  • 18. Appendix Pandas for Data Analysis/7. Describing Data with Pandas.srt 14.6 kB
  • 2. Deep Learning and TensorFlow Fundamentals/2. Why use deep learning.srt 14.5 kB
  • 9. Milestone Project 1 Food Vision Big™/13. Training and evaluating a feature extraction model (Food Vision Big™).srt 14.5 kB
  • 9. Milestone Project 1 Food Vision Big™/2. Making sure we have access to the right GPU for mixed precision training.srt 14.4 kB
  • 3. Neural network regression with TensorFlow/10. Evaluating a TensorFlow model part 2 (the three datasets).srt 14.4 kB
  • 18. Appendix Pandas for Data Analysis/12. Manipulating Data 3.srt 14.3 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/10. Improving our non-CNN model by adding more layers.srt 14.3 kB
  • 3. Neural network regression with TensorFlow/27. Preprocessing data with feature scaling part 2 (normalising our data).srt 14.3 kB
  • 9. Milestone Project 1 Food Vision Big™/10. Turning on mixed precision training with TensorFlow.srt 14.2 kB
  • 3. Neural network regression with TensorFlow/26. Preprocessing data with feature scaling part 1 (what is feature scaling).srt 14.2 kB
  • 19. Appendix NumPy/8. Viewing Arrays and Matrices.srt 14.2 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/20. Comparing our model's results before and after fine-tuning.srt 14.2 kB
  • 4. Neural network classification in TensorFlow/11. Non-linearity part 1 Straight lines and non-straight lines.srt 14.1 kB
  • 4. Neural network classification in TensorFlow/24. Putting things together with multi-class classification part 1 Getting the data.srt 14.1 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/25. Writing a helper function to load and preprocessing custom images.srt 14.1 kB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/6. Blowing our previous models out of the water with transfer learning.srt 14.0 kB
  • 4. Neural network classification in TensorFlow/32. Multi-class classification part 9 Visualising random model predictions.srt 13.8 kB
  • 2. Deep Learning and TensorFlow Fundamentals/4. What is deep learning already being used for.srt 13.8 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/5. Creating a headless EfficientNetB0 model with data augmentation built in.srt 13.8 kB
  • 17. Appendix Machine Learning and Data Science Framework/12. Modelling - Comparison.srt 13.6 kB
  • 2. Deep Learning and TensorFlow Fundamentals/20. Matrix multiplication with tensors part 3.srt 13.6 kB
  • 3. Neural network regression with TensorFlow/19. Comparing and tracking your TensorFlow modelling experiments.srt 13.5 kB
  • 3. Neural network regression with TensorFlow/2. Inputs and outputs of a neural network regression model.srt 13.4 kB
  • 3. Neural network regression with TensorFlow/7. Steps in improving a model with TensorFlow part 2.srt 13.4 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/8. Using a GPU to run our CNN model 5x faster.srt 13.4 kB
  • 2. Deep Learning and TensorFlow Fundamentals/12. Creating random tensors with TensorFlow.srt 13.3 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/11. Breaking our CNN model down part 1 Becoming one with the data.srt 13.3 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/10. Downloading and preparing the data for Model 1 (1 percent of training data).srt 13.3 kB
  • 2. Deep Learning and TensorFlow Fundamentals/22. Tensor aggregation (finding the min, max, mean & more).srt 13.2 kB
  • 3. Neural network regression with TensorFlow/21. How to load and use a saved TensorFlow model.srt 13.1 kB
  • 4. Neural network classification in TensorFlow/1. Introduction to neural network classification in TensorFlow.srt 13.1 kB
  • 4. Neural network classification in TensorFlow/8. Trying to improve our not very good classification model.srt 13.0 kB
  • 2. Deep Learning and TensorFlow Fundamentals/13. Shuffling the order of tensors.srt 12.9 kB
  • 19. Appendix NumPy/6. Creating NumPy Arrays.srt 12.8 kB
  • 2. Deep Learning and TensorFlow Fundamentals/24. Finding the positional minimum and maximum of a tensor (argmin and argmax).srt 12.7 kB
  • 3. Neural network regression with TensorFlow/3. Anatomy and architecture of a neural network regression model.srt 12.5 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/2. Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.srt 12.4 kB
  • 19. Appendix NumPy/10. Manipulating Arrays 2.srt 12.3 kB
  • 4. Neural network classification in TensorFlow/14. Non-linearity part 4 Modelling our non-linear data once and for all.srt 12.3 kB
  • 3. Neural network regression with TensorFlow/13. Evaluating a TensorFlow model part 5 (visualising a model's predictions).srt 12.2 kB
  • 4. Neural network classification in TensorFlow/19. Training and evaluating a model with an ideal learning rate.srt 12.2 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/8. Fine-tuning our feature extraction model and evaluating its performance.srt 12.2 kB
  • 2. Deep Learning and TensorFlow Fundamentals/5. What is and why use TensorFlow.srt 12.0 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/9. Trying a non-CNN model on our image data.srt 11.9 kB
  • 4. Neural network classification in TensorFlow/22. Creating our first confusion matrix (to see where our model is getting confused).srt 11.8 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/6. Fitting and evaluating our biggest transfer learning model yet.srt 11.7 kB
  • 3. Neural network regression with TensorFlow/1. Introduction to Neural Network Regression with TensorFlow.srt 11.7 kB
  • 3. Neural network regression with TensorFlow/20. How to save a TensorFlow model.srt 11.7 kB
  • 18. Appendix Pandas for Data Analysis/14. How To Download The Course Assignments.srt 11.5 kB
  • 9. Milestone Project 1 Food Vision Big™/14. Introducing your Milestone Project 1 challenge build a model to beat DeepFood.srt 11.5 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/14. Breaking our CNN model down part 4 Building a baseline CNN model.srt 11.5 kB
  • 3. Neural network regression with TensorFlow/14. Evaluating a TensorFlow model part 6 (common regression evaluation metrics).srt 11.4 kB
  • 3. Neural network regression with TensorFlow/28. Preprocessing data with feature scaling part 3 (fitting a model on scaled data).srt 11.2 kB
  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/7. Plotting the loss curves of our ResNet feature extraction model.srt 11.1 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/15. Creating a ModelCheckpoint to save our model's weights during training.srt 11.0 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/16. Plotting our model's F1-scores for each separate class.srt 10.9 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/29. Multi-class CNN's part 3 Building a multi-class CNN model.srt 10.9 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/19. Fitting and evaluating Model 3 (our first fine-tuned model).srt 10.9 kB
  • 19. Appendix NumPy/17. Turn Images Into NumPy Arrays.srt 10.9 kB
  • 19. Appendix NumPy/7. NumPy Random Seed.srt 10.7 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/3. Downloading an image dataset for our first Food Vision model.srt 10.6 kB
  • 9. Milestone Project 1 Food Vision Big™/12. Checking to see if our model is using mixed precision training layer by layer.srt 10.5 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/1. Introduction to Transfer Learning Part 3 Scaling Up.srt 10.4 kB
  • 4. Neural network classification in TensorFlow/25. Multi-class classification part 2 Becoming one with the data.srt 10.2 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/28. Multi-class CNN's part 2 Preparing our data (turning it into tensors).srt 10.2 kB
  • 2. Deep Learning and TensorFlow Fundamentals/11. Creating tensors with TensorFlow and tf.Variable().srt 10.1 kB
  • 4. Neural network classification in TensorFlow/2. Example classification problems (and their inputs and outputs).srt 10.1 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/16. Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).srt 10.1 kB
  • 9. Milestone Project 1 Food Vision Big™/9. Creating modelling callbacks for our feature extraction model.srt 10.1 kB
  • 19. Appendix NumPy/11. Standard Deviation and Variance.srt 10.1 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/1. Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.srt 10.0 kB
  • 3. Neural network regression with TensorFlow/9. Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).srt 10.0 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/2. Importing a script full of helper functions (and saving lots of space).srt 10.0 kB
  • 19. Appendix NumPy/12. Reshape and Transpose.srt 9.9 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/17. Loading and comparing saved weights to our existing trained Model 2.srt 9.9 kB
  • 9. Milestone Project 1 Food Vision Big™/8. Exploring what happens when we batch and prefetch our data.srt 9.6 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/19. Discussing the benefits of finding your model's most wrong predictions.srt 9.6 kB
  • 3. Neural network regression with TensorFlow/12. Evaluating a TensorFlow model part 4 (visualising a model's layers).srt 9.4 kB
  • 9. Milestone Project 1 Food Vision Big™/1. Introduction to Milestone Project 1 Food Vision Big™.srt 9.4 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/36. Saving and loading our trained CNN model.srt 9.3 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/9. Saving and loading our trained model.srt 9.2 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/21. Downloading and preparing data for our biggest experiment yet (Model 4).srt 9.2 kB
  • 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.srt 9.2 kB
  • 16. Appendix Machine Learning Primer/2. What is Machine Learning.srt 9.2 kB
  • 19. Appendix NumPy/16. Sorting Arrays.srt 9.2 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/10. Downloading a pretrained model to make and evaluate predictions with.srt 9.1 kB
  • 4. Neural network classification in TensorFlow/20. Introducing more classification evaluation methods.srt 9.1 kB
  • 4. Neural network classification in TensorFlow/3. Input and output tensors of classification problems.srt 9.1 kB
  • 2. Deep Learning and TensorFlow Fundamentals/21. Changing the datatype of tensors.srt 8.9 kB
  • 4. Neural network classification in TensorFlow/17. Using the TensorFlow History object to plot a model's loss curves.srt 8.6 kB
  • 2. Deep Learning and TensorFlow Fundamentals/8. How to approach this course.srt 8.4 kB
  • 16. Appendix Machine Learning Primer/4. Exercise Machine Learning Playground.srt 8.3 kB
  • 3. Neural network regression with TensorFlow/15. Evaluating a TensorFlow regression model part 7 (mean absolute error).srt 8.3 kB
  • 2. Deep Learning and TensorFlow Fundamentals/26. One-hot encoding tensors.srt 8.2 kB
  • 1. Introduction/1. Course Outline.srt 8.2 kB
  • 17. Appendix Machine Learning and Data Science Framework/9. Modelling - Splitting Data.srt 8.0 kB
  • 3. Neural network regression with TensorFlow/22. (Optional) How to save and download files from Google Colab.srt 8.0 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/12. Unravelling our test dataset for comparing ground truth labels to predictions.srt 7.9 kB
  • 3. Neural network regression with TensorFlow/6. Steps in improving a model with TensorFlow part 1.srt 7.8 kB
  • 19. Appendix NumPy/3. NumPy Introduction.srt 7.8 kB
  • 4. Neural network classification in TensorFlow/12. Non-linearity part 2 Building our first neural network with non-linearity.srt 7.8 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/3. Outlining the model we're going to build and building a ModelCheckpoint callback.srt 7.6 kB
  • 16. Appendix Machine Learning Primer/5. How Did We Get Here.srt 7.5 kB
  • 2. Deep Learning and TensorFlow Fundamentals/7. What we're going to cover throughout the course.srt 7.4 kB
  • 2. Deep Learning and TensorFlow Fundamentals/28. Exploring TensorFlow and NumPy's compatibility.srt 7.3 kB
  • 2. Deep Learning and TensorFlow Fundamentals/17. Manipulating tensors with basic operations.srt 7.1 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/24. Downloading a custom image to make predictions on.srt 7.1 kB
  • 18. Appendix Pandas for Data Analysis/4. Pandas Introduction.srt 7.1 kB
  • 17. Appendix Machine Learning and Data Science Framework/8. Features In Data.srt 7.0 kB
  • 17. Appendix Machine Learning and Data Science Framework/4. 6 Step Machine Learning Framework.srt 7.0 kB
  • 2. Deep Learning and TensorFlow Fundamentals/1. What is deep learning.srt 7.0 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/31. Multi-class CNN's part 5 Evaluating our multi-class CNN model.srt 7.0 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/13. Confirming our model's predictions are in the same order as the test labels.srt 6.9 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/4. Becoming One With Data.srt 6.9 kB
  • 4. Neural network classification in TensorFlow/31. Multi-class classification part 8 Creating a confusion matrix.srt 6.8 kB
  • 2. Deep Learning and TensorFlow Fundamentals/23. Tensor troubleshooting example (updating tensor datatypes).srt 6.8 kB
  • 4. Neural network classification in TensorFlow/6. Checking the input and output shapes of our classification data.srt 6.7 kB
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/6. Becoming One With Data Part 3.srt 6.7 kB
  • 17. Appendix Machine Learning and Data Science Framework/6. Types of Data.srt 6.6 kB
  • 16. Appendix Machine Learning Primer/3. AIMachine LearningData Science.srt 6.6 kB
  • 16. Appendix Machine Learning Primer/9. What Is Machine Learning Round 2.srt 6.4 kB
  • 8. Transfer Learning with TensorFlow Part 3 Scaling Up/4. Creating a data augmentation layer to use with our model.srt 6.4 kB
  • 17. Appendix Machine Learning and Data Science Framework/10. Modelling - Picking the Model.srt 6.4 kB
  • 2. Deep Learning and TensorFlow Fundamentals/27. Trying out more tensor math operations.srt 6.4 kB
  • 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.srt 6.3 kB
  • 17. Appendix Machine Learning and Data Science Framework/15. Tools We Will Use.srt 6.2 kB
  • 4. Neural network classification in TensorFlow/21. Finding the accuracy of our classification model.srt 5.8 kB
  • 16. Appendix Machine Learning Primer/6. Exercise YouTube Recommendation Engine.srt 5.7 kB
  • 16. Appendix Machine Learning Primer/7. Types of Machine Learning.srt 5.6 kB
  • 4. Neural network classification in TensorFlow/28. Multi-class classification part 5 Comparing normalised and non-normalised data.srt 5.6 kB
  • 7. Transfer Learning in TensorFlow Part 2 Fine tuning/9. Drilling into the concept of a feature vector (a learned representation).srt 5.5 kB
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  • 9. Milestone Project 1 Food Vision Big™/3. Getting helper functions ready.srt 4.0 kB
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  • 2. Deep Learning and TensorFlow Fundamentals/25. Squeezing a tensor (removing all 1-dimension axes).srt 3.9 kB
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  • 3. Neural network regression with TensorFlow/29. TensorFlow Regression challenge, exercises & extra-curriculum.html 2.0 kB
  • 17. Appendix Machine Learning and Data Science Framework/13. Overfitting and Underfitting Definitions.html 2.0 kB
  • 1. Introduction/4. All Course Resources + Notebooks.html 2.0 kB
  • 2. Deep Learning and TensorFlow Fundamentals/30. TensorFlow Fundamentals challenge, exercises & extra-curriculum.html 2.0 kB
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  • How you can help GetFreeCourses.Co.txt 182 Bytes
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  • 15. Where To Go From Here/5. The Final Challenge.html 176 Bytes
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  • 18. Appendix Pandas for Data Analysis/4.1 10 Minutes to pandas.html 127 Bytes
  • 19. Appendix NumPy/13.1 httpswww.mathsisfun.comalgebramatrix-multiplying.html.html 119 Bytes
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  • 6. Transfer Learning in TensorFlow Part 1 Feature extraction/1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html 114 Bytes
  • 18. Appendix Pandas for Data Analysis/14.1 Course Notes.html 108 Bytes
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/15.1 CNN Explainer website.html 102 Bytes
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  • 11. Milestone Project 2 SkimLit/1. More Videos Coming Soon!.html 41 Bytes
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  • 13. Milestone Project 3 BitPredict/1. More Videos Coming Soon!.html 41 Bytes
  • 14. Passing the TensorFlow Developer Certificate Exam/1. More Videos Coming Soon!.html 41 Bytes
  • 5. Computer Vision and Convolutional Neural Networks in TensorFlow/1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html 0 Bytes
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