FreeCourseSite com Udemy TensorFlow Developer Certificate in 2023 Zero to Mastery

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/01 - Introduction/

001 Course Outline.mp4

001 Course Outline_en.srt

002 Join Our Online Classroom!.mp4

002 Join Our Online Classroom!_en.srt

003 Exercise Meet Your Classmates & Instructor.html

004 All Course Resources + Asking Questions + Getting Help.html

004 TensorFlow-for-Deep-Learning-Book.url

004 Zero-to-Mastery-TensorFlow-Deep-Learning-on-GitHub.url

005 LinkedIn-Group.url

005 ZTM Resources.mp4

005 ZTM Resources_en.srt

005 ZTM-Youtube.url

005 zerotomastery.io.url

external-links.txt

/02 - Deep Learning and TensorFlow Fundamentals/

001 All-course-materials-and-links-.url

001 What is deep learning.mp4

001 What is deep learning_en.srt

002 Why use deep learning.mp4

002 Why use deep learning_en.srt

003 What are neural networks.mp4

003 What are neural networks_en.srt

004 Python + Machine Learning Monthly.html

005 What is deep learning already being used for.mp4

005 What is deep learning already being used for_en.srt

006 What is and why use TensorFlow.mp4

006 What is and why use TensorFlow_en.srt

007 What is a Tensor.mp4

007 What is a Tensor_en.srt

008 What we're going to cover throughout the course.mp4

008 What we're going to cover throughout the course_en.srt

009 How to approach this course.mp4

009 How to approach this course_en.srt

010 Need A Refresher.html

011 Creating your first tensors with TensorFlow and tf.constant().mp4

011 Creating your first tensors with TensorFlow and tf.constant()_en.srt

012 Creating tensors with TensorFlow and tf.Variable().mp4

012 Creating tensors with TensorFlow and tf.Variable()_en.srt

013 Creating random tensors with TensorFlow.mp4

013 Creating random tensors with TensorFlow_en.srt

014 Shuffling the order of tensors.mp4

014 Shuffling the order of tensors_en.srt

015 Creating tensors from NumPy arrays.mp4

015 Creating tensors from NumPy arrays_en.srt

016 Getting information from your tensors (tensor attributes).mp4

016 Getting information from your tensors (tensor attributes)_en.srt

017 Indexing and expanding tensors.mp4

017 Indexing and expanding tensors_en.srt

018 Manipulating tensors with basic operations.mp4

018 Manipulating tensors with basic operations_en.srt

019 Matrix multiplication with tensors part 1.mp4

019 Matrix multiplication with tensors part 1_en.srt

020 Matrix multiplication with tensors part 2.mp4

020 Matrix multiplication with tensors part 2_en.srt

021 Matrix multiplication with tensors part 3.mp4

021 Matrix multiplication with tensors part 3_en.srt

022 Changing the datatype of tensors.mp4

022 Changing the datatype of tensors_en.srt

023 Tensor aggregation (finding the min, max, mean & more).mp4

023 Tensor aggregation (finding the min, max, mean & more)_en.srt

024 Tensor troubleshooting example (updating tensor datatypes).mp4

024 Tensor troubleshooting example (updating tensor datatypes)_en.srt

025 Finding the positional minimum and maximum of a tensor (argmin and argmax).mp4

025 Finding the positional minimum and maximum of a tensor (argmin and argmax)_en.srt

026 Squeezing a tensor (removing all 1-dimension axes).mp4

026 Squeezing a tensor (removing all 1-dimension axes)_en.srt

027 One-hot encoding tensors.mp4

027 One-hot encoding tensors_en.srt

028 Trying out more tensor math operations.mp4

028 Trying out more tensor math operations_en.srt

029 Exploring TensorFlow and NumPy's compatibility.mp4

029 Exploring TensorFlow and NumPy's compatibility_en.srt

030 Making sure our tensor operations run really fast on GPUs.mp4

030 Making sure our tensor operations run really fast on GPUs_en.srt

031 TensorFlow Fundamentals challenge, exercises & extra-curriculum.html

032 Monthly Coding Challenges, Free Resources and Guides.html

033 LinkedIn Endorsements.html

external-links.txt

/.../0. Websites you may like/

[CourseClub.Me].url

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/03 - Neural network regression with TensorFlow/

001 All-course-materials-and-links-notebooks-code-data-slides-on-GitHub.url

001 Introduction to Neural Network Regression with TensorFlow.mp4

001 Introduction to Neural Network Regression with TensorFlow_en.srt

002 Inputs and outputs of a neural network regression model.mp4

002 Inputs and outputs of a neural network regression model_en.srt

003 Anatomy and architecture of a neural network regression model.mp4

003 Anatomy and architecture of a neural network regression model_en.srt

004 Creating sample regression data (so we can model it).mp4

004 Creating sample regression data (so we can model it)_en.srt

005 Note Code update for upcoming lecture(s) for TensorFlow 2.7.0+ fix.html

006 The major steps in modelling with TensorFlow.mp4

006 The major steps in modelling with TensorFlow_en.srt

007 Steps in improving a model with TensorFlow part 1.mp4

007 Steps in improving a model with TensorFlow part 1_en.srt

008 Steps in improving a model with TensorFlow part 2.mp4

008 Steps in improving a model with TensorFlow part 2_en.srt

009 Steps in improving a model with TensorFlow part 3.mp4

009 Steps in improving a model with TensorFlow part 3_en.srt

010 Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).mp4

010 Evaluating a TensorFlow model part 1 (visualise, visualise, visualise)_en.srt

011 Evaluating a TensorFlow model part 2 (the three datasets).mp4

011 Evaluating a TensorFlow model part 2 (the three datasets)_en.srt

012 Evaluating a TensorFlow model part 3 (getting a model summary).mp4

012 Evaluating a TensorFlow model part 3 (getting a model summary)_en.srt

013 Evaluating a TensorFlow model part 4 (visualising a model's layers).mp4

013 Evaluating a TensorFlow model part 4 (visualising a model's layers)_en.srt

014 Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp4

014 Evaluating a TensorFlow model part 5 (visualising a model's predictions)_en.srt

015 Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp4

015 Evaluating a TensorFlow model part 6 (common regression evaluation metrics)_en.srt

016 Evaluating a TensorFlow regression model part 7 (mean absolute error).mp4

016 Evaluating a TensorFlow regression model part 7 (mean absolute error)_en.srt

017 Evaluating a TensorFlow regression model part 7 (mean square error).mp4

017 Evaluating a TensorFlow regression model part 7 (mean square error)_en.srt

018 Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4

018 Setting up TensorFlow modelling experiments part 1 (start with a simple model)_en.srt

019 Setting up TensorFlow modelling experiments part 2 (increasing complexity).mp4

019 Setting up TensorFlow modelling experiments part 2 (increasing complexity)_en.srt

020 Comparing and tracking your TensorFlow modelling experiments.mp4

020 Comparing and tracking your TensorFlow modelling experiments_en.srt

021 How to save a TensorFlow model.mp4

021 How to save a TensorFlow model_en.srt

022 How to load and use a saved TensorFlow model.mp4

022 How to load and use a saved TensorFlow model_en.srt

023 (Optional) How to save and download files from Google Colab.mp4

023 (Optional) How to save and download files from Google Colab_en.srt

024 Putting together what we've learned part 1 (preparing a dataset).mp4

024 Putting together what we've learned part 1 (preparing a dataset)_en.srt

025 Putting together what we've learned part 2 (building a regression model).mp4

025 Putting together what we've learned part 2 (building a regression model)_en.srt

026 Putting together what we've learned part 3 (improving our regression model).mp4

026 Putting together what we've learned part 3 (improving our regression model)_en.srt

027 Preprocessing data with feature scaling part 1 (what is feature scaling).mp4

027 Preprocessing data with feature scaling part 1 (what is feature scaling)_en.srt

028 Preprocessing data with feature scaling part 2 (normalising our data).mp4

028 Preprocessing data with feature scaling part 2 (normalising our data)_en.srt

029 Preprocessing data with feature scaling part 3 (fitting a model on scaled data).mp4

029 Preprocessing data with feature scaling part 3 (fitting a model on scaled data)_en.srt

030 TensorFlow Regression challenge, exercises & extra-curriculum.html

031 Learning Guideline.html

external-links.txt

/04 - Neural network classification in TensorFlow/

001 All-course-materials-and-links-notebooks-code-data-slides-on-GitHub.url

001 Introduction to neural network classification in TensorFlow.mp4

001 Introduction to neural network classification in TensorFlow_en.srt

002 Example classification problems (and their inputs and outputs).mp4

002 Example classification problems (and their inputs and outputs)_en.srt

003 Input and output tensors of classification problems.mp4

003 Input and output tensors of classification problems_en.srt

004 Typical architecture of neural network classification models with TensorFlow.mp4

004 Typical architecture of neural network classification models with TensorFlow_en.srt

005 Creating and viewing classification data to model.mp4

005 Creating and viewing classification data to model_en.srt

006 Checking the input and output shapes of our classification data.mp4

006 Checking the input and output shapes of our classification data_en.srt

007 Building a not very good classification model with TensorFlow.mp4

007 Building a not very good classification model with TensorFlow_en.srt

008 Trying to improve our not very good classification model.mp4

008 Trying to improve our not very good classification model_en.srt

009 Creating a function to view our model's not so good predictions.mp4

009 Creating a function to view our model's not so good predictions_en.srt

010 Note Updates for TensorFlow 2.7.0.html

011 Make our poor classification model work for a regression dataset.mp4

011 Make our poor classification model work for a regression dataset_en.srt

012 Non-linearity part 1 Straight lines and non-straight lines.mp4

012 Non-linearity part 1 Straight lines and non-straight lines_en.srt

013 Non-linearity part 2 Building our first neural network with non-linearity.mp4

013 Non-linearity part 2 Building our first neural network with non-linearity_en.srt

014 Non-linearity part 3 Upgrading our non-linear model with more layers.mp4

014 Non-linearity part 3 Upgrading our non-linear model with more layers_en.srt

015 Non-linearity part 4 Modelling our non-linear data once and for all.mp4

015 Non-linearity part 4 Modelling our non-linear data once and for all_en.srt

016 Non-linearity part 5 Replicating non-linear activation functions from scratch.mp4

016 Non-linearity part 5 Replicating non-linear activation functions from scratch_en.srt

017 Getting great results in less time by tweaking the learning rate.mp4

017 Getting great results in less time by tweaking the learning rate_en.srt

018 Using the TensorFlow History object to plot a model's loss curves.mp4

018 Using the TensorFlow History object to plot a model's loss curves_en.srt

019 Using callbacks to find a model's ideal learning rate.mp4

019 Using callbacks to find a model's ideal learning rate_en.srt

020 Training and evaluating a model with an ideal learning rate.mp4

020 Training and evaluating a model with an ideal learning rate_en.srt

021 Introducing more classification evaluation methods.mp4

021 Introducing more classification evaluation methods_en.srt

022 Finding the accuracy of our classification model.mp4

022 Finding the accuracy of our classification model_en.srt

023 Creating our first confusion matrix (to see where our model is getting confused).mp4

023 Creating our first confusion matrix (to see where our model is getting confused)_en.srt

024 Making our confusion matrix prettier.mp4

024 Making our confusion matrix prettier_en.srt

025 Putting things together with multi-class classification part 1 Getting the data.mp4

025 Putting things together with multi-class classification part 1 Getting the data_en.srt

026 Multi-class classification part 2 Becoming one with the data.mp4

026 Multi-class classification part 2 Becoming one with the data_en.srt

027 Multi-class classification part 3 Building a multi-class classification model.mp4

027 Multi-class classification part 3 Building a multi-class classification model_en.srt

028 Multi-class classification part 4 Improving performance with normalisation.mp4

028 Multi-class classification part 4 Improving performance with normalisation_en.srt

029 Multi-class classification part 5 Comparing normalised and non-normalised data.mp4

029 Multi-class classification part 5 Comparing normalised and non-normalised data_en.srt

030 Multi-class classification part 6 Finding the ideal learning rate.mp4

030 Multi-class classification part 6 Finding the ideal learning rate_en.srt

031 Multi-class classification part 7 Evaluating our model.mp4

031 Multi-class classification part 7 Evaluating our model_en.srt

032 Multi-class classification part 8 Creating a confusion matrix.mp4

032 Multi-class classification part 8 Creating a confusion matrix_en.srt

033 Multi-class classification part 9 Visualising random model predictions.mp4

033 Multi-class classification part 9 Visualising random model predictions_en.srt

034 What patterns is our model learning.mp4

034 What patterns is our model learning_en.srt

035 TensorFlow classification challenge, exercises & extra-curriculum.html

external-links.txt

/05 - Computer Vision and Convolutional Neural Networks in TensorFlow/

001 All-course-materials-and-links-notebooks-code-data-slides-on-GitHub.url

001 Introduction to Computer Vision with TensorFlow.mp4

001 Introduction to Computer Vision with TensorFlow_en.srt

002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.mp4

002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow_en.srt

003 Downloading an image dataset for our first Food Vision model.mp4

003 Downloading an image dataset for our first Food Vision model_en.srt

004 Becoming One With Data.mp4

004 Becoming One With Data_en.srt

005 Becoming One With Data Part 2.mp4

005 Becoming One With Data Part 2_en.srt

006 Becoming One With Data Part 3.mp4

006 Becoming One With Data Part 3_en.srt

007 Building an end to end CNN Model.mp4

007 Building an end to end CNN Model_en.srt

008 Using a GPU to run our CNN model 5x faster.mp4

008 Using a GPU to run our CNN model 5x faster_en.srt

009 Trying a non-CNN model on our image data.mp4

009 Trying a non-CNN model on our image data_en.srt

010 Improving our non-CNN model by adding more layers.mp4

010 Improving our non-CNN model by adding more layers_en.srt

011 Breaking our CNN model down part 1 Becoming one with the data.mp4

011 Breaking our CNN model down part 1 Becoming one with the data_en.srt

012 Breaking our CNN model down part 2 Preparing to load our data.mp4

012 Breaking our CNN model down part 2 Preparing to load our data_en.srt

013 Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.mp4

013 Breaking our CNN model down part 3 Loading our data with ImageDataGenerator_en.srt

014 Breaking our CNN model down part 4 Building a baseline CNN model.mp4

014 Breaking our CNN model down part 4 Building a baseline CNN model_en.srt

015 Breaking our CNN model down part 5 Looking inside a Conv2D layer.mp4

015 Breaking our CNN model down part 5 Looking inside a Conv2D layer_en.srt

015 CNN-Explainer-website.url

016 Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.mp4

016 Breaking our CNN model down part 6 Compiling and fitting our baseline CNN_en.srt

017 Breaking our CNN model down part 7 Evaluating our CNN's training curves.mp4

017 Breaking our CNN model down part 7 Evaluating our CNN's training curves_en.srt

018 Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.mp4

018 Breaking our CNN model down part 8 Reducing overfitting with Max Pooling_en.srt

019 Breaking our CNN model down part 9 Reducing overfitting with data augmentation.mp4

019 Breaking our CNN model down part 9 Reducing overfitting with data augmentation_en.srt

020 Breaking our CNN model down part 10 Visualizing our augmented data.mp4

020 Breaking our CNN model down part 10 Visualizing our augmented data_en.srt

021 Breaking our CNN model down part 11 Training a CNN model on augmented data.mp4

021 Breaking our CNN model down part 11 Training a CNN model on augmented data_en.srt

022 Breaking our CNN model down part 12 Discovering the power of shuffling data.mp4

022 Breaking our CNN model down part 12 Discovering the power of shuffling data_en.srt

023 Breaking our CNN model down part 13 Exploring options to improve our model.mp4

023 Breaking our CNN model down part 13 Exploring options to improve our model_en.srt

024 Downloading a custom image to make predictions on.mp4

024 Downloading a custom image to make predictions on_en.srt

025 Writing a helper function to load and preprocessing custom images.mp4

025 Writing a helper function to load and preprocessing custom images_en.srt

026 Making a prediction on a custom image with our trained CNN.mp4

026 Making a prediction on a custom image with our trained CNN_en.srt

027 Multi-class CNN's part 1 Becoming one with the data.mp4

027 Multi-class CNN's part 1 Becoming one with the data_en.srt

028 Multi-class CNN's part 2 Preparing our data (turning it into tensors).mp4

028 Multi-class CNN's part 2 Preparing our data (turning it into tensors)_en.srt

029 Multi-class CNN's part 3 Building a multi-class CNN model.mp4

029 Multi-class CNN's part 3 Building a multi-class CNN model_en.srt

030 Multi-class CNN's part 4 Fitting a multi-class CNN model to the data.mp4

030 Multi-class CNN's part 4 Fitting a multi-class CNN model to the data_en.srt

031 Multi-class CNN's part 5 Evaluating our multi-class CNN model.mp4

031 Multi-class CNN's part 5 Evaluating our multi-class CNN model_en.srt

032 Multi-class CNN's part 6 Trying to fix overfitting by removing layers.mp4

032 Multi-class CNN's part 6 Trying to fix overfitting by removing layers_en.srt

033 Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.mp4

033 Multi-class CNN's part 7 Trying to fix overfitting with data augmentation_en.srt

034 Multi-class CNN's part 8 Things you could do to improve your CNN model.mp4

034 Multi-class CNN's part 8 Things you could do to improve your CNN model_en.srt

035 Multi-class CNN's part 9 Making predictions with our model on custom images.mp4

035 Multi-class CNN's part 9 Making predictions with our model on custom images_en.srt

036 Saving and loading our trained CNN model.mp4

036 Saving and loading our trained CNN model_en.srt

037 TensorFlow computer vision and CNNs challenge, exercises & extra-curriculum.html

external-links.txt

/06 - Transfer Learning in TensorFlow Part 1 Feature extraction/

001 All-course-materials-and-links-notebooks-code-data-slides-on-GitHub.url

001 What is and why use transfer learning.mp4

001 What is and why use transfer learning_en.srt

002 Downloading and preparing data for our first transfer learning model.mp4

002 Downloading and preparing data for our first transfer learning model_en.srt

003 Introducing Callbacks in TensorFlow and making a callback to track our models.mp4

003 Introducing Callbacks in TensorFlow and making a callback to track our models_en.srt

004 Exploring the TensorFlow Hub website for pretrained models.mp4

004 Exploring the TensorFlow Hub website for pretrained models_en.srt

005 Building and compiling a TensorFlow Hub feature extraction model.mp4

005 Building and compiling a TensorFlow Hub feature extraction model_en.srt

006 Blowing our previous models out of the water with transfer learning.mp4

006 Blowing our previous models out of the water with transfer learning_en.srt

007 Plotting the loss curves of our ResNet feature extraction model.mp4

007 Plotting the loss curves of our ResNet feature extraction model_en.srt

008 Building and training a pre-trained EfficientNet model on our data.mp4

008 Building and training a pre-trained EfficientNet model on our data_en.srt

009 Different Types of Transfer Learning.mp4

009 Different Types of Transfer Learning_en.srt

010 Comparing Our Model's Results.mp4

010 Comparing Our Model's Results_en.srt

011 TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum.html

012 Exercise Imposter Syndrome.mp4

012 Exercise Imposter Syndrome_en.srt

external-links.txt

/07 - Transfer Learning in TensorFlow Part 2 Fine tuning/

001 Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.mp4

001 Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning_en.srt

002 Importing a script full of helper functions (and saving lots of space).mp4

002 Importing a script full of helper functions (and saving lots of space)_en.srt

003 Downloading and turning our images into a TensorFlow BatchDataset.mp4

003 Downloading and turning our images into a TensorFlow BatchDataset_en.srt

004 Discussing the four (actually five) modelling experiments we're running.mp4

004 Discussing the four (actually five) modelling experiments we're running_en.srt

005 Comparing the TensorFlow Keras Sequential API versus the Functional API.mp4

005 Comparing the TensorFlow Keras Sequential API versus the Functional API_en.srt

006 Creating our first model with the TensorFlow Keras Functional API.mp4

006 Creating our first model with the TensorFlow Keras Functional API_en.srt

007 Compiling and fitting our first Functional API model.mp4

007 Compiling and fitting our first Functional API model_en.srt

008 Getting a feature vector from our trained model.mp4

008 Getting a feature vector from our trained model_en.srt

009 Drilling into the concept of a feature vector (a learned representation).mp4

009 Drilling into the concept of a feature vector (a learned representation)_en.srt

010 Downloading and preparing the data for Model 1 (1 percent of training data).mp4

010 Downloading and preparing the data for Model 1 (1 percent of training data)_en.srt

011 Building a data augmentation layer to use inside our model.mp4

011 Building a data augmentation layer to use inside our model_en.srt

012 Note Small fix for next video, for images not augmenting.html

013 Visualizing what happens when images pass through our data augmentation layer.mp4

013 Visualizing what happens when images pass through our data augmentation layer_en.srt

014 Building Model 1 (with a data augmentation layer and 1% of training data).mp4

014 Building Model 1 (with a data augmentation layer and 1% of training data)_en.srt

015 Building Model 2 (with a data augmentation layer and 10% of training data).mp4

015 Building Model 2 (with a data augmentation layer and 10% of training data)_en.srt

016 Creating a ModelCheckpoint to save our model's weights during training.mp4

016 Creating a ModelCheckpoint to save our model's weights during training_en.srt

017 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp4

017 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint)_en.srt

018 Loading and comparing saved weights to our existing trained Model 2.mp4

018 Loading and comparing saved weights to our existing trained Model 2_en.srt

019 Preparing Model 3 (our first fine-tuned model).mp4

019 Preparing Model 3 (our first fine-tuned model)_en.srt

020 Fitting and evaluating Model 3 (our first fine-tuned model).mp4

020 Fitting and evaluating Model 3 (our first fine-tuned model)_en.srt

021 Comparing our model's results before and after fine-tuning.mp4

021 Comparing our model's results before and after fine-tuning_en.srt

022 Downloading and preparing data for our biggest experiment yet (Model 4).mp4

022 Downloading and preparing data for our biggest experiment yet (Model 4)_en.srt

023 Preparing our final modelling experiment (Model 4).mp4

023 Preparing our final modelling experiment (Model 4)_en.srt

024 Fine-tuning Model 4 on 100% of the training data and evaluating its results.mp4

024 Fine-tuning Model 4 on 100% of the training data and evaluating its results_en.srt

025 Comparing our modelling experiment results in TensorBoard.mp4

025 Comparing our modelling experiment results in TensorBoard_en.srt

026 How to view and delete previous TensorBoard experiments.mp4

026 How to view and delete previous TensorBoard experiments_en.srt

027 Transfer Learning in TensorFlow Part 2 challenge, exercises and extra-curriculum.html

/08 - Transfer Learning with TensorFlow Part 3 Scaling Up/

001 Introduction to Transfer Learning Part 3 Scaling Up.mp4

001 Introduction to Transfer Learning Part 3 Scaling Up_en.srt

002 Getting helper functions ready and downloading data to model.mp4

002 Getting helper functions ready and downloading data to model_en.srt

003 Outlining the model we're going to build and building a ModelCheckpoint callback.mp4

003 Outlining the model we're going to build and building a ModelCheckpoint callback_en.srt

004 Creating a data augmentation layer to use with our model.mp4

004 Creating a data augmentation layer to use with our model_en.srt

005 Creating a headless EfficientNetB0 model with data augmentation built in.mp4

005 Creating a headless EfficientNetB0 model with data augmentation built in_en.srt

006 Fitting and evaluating our biggest transfer learning model yet.mp4

006 Fitting and evaluating our biggest transfer learning model yet_en.srt

007 Unfreezing some layers in our base model to prepare for fine-tuning.mp4

007 Unfreezing some layers in our base model to prepare for fine-tuning_en.srt

008 Fine-tuning our feature extraction model and evaluating its performance.mp4

008 Fine-tuning our feature extraction model and evaluating its performance_en.srt

009 Saving and loading our trained model.mp4

009 Saving and loading our trained model_en.srt

010 Downloading a pretrained model to make and evaluate predictions with.mp4

010 Downloading a pretrained model to make and evaluate predictions with_en.srt

011 Making predictions with our trained model on 25,250 test samples.mp4

011 Making predictions with our trained model on 25,250 test samples_en.srt

012 Unravelling our test dataset for comparing ground truth labels to predictions.mp4

012 Unravelling our test dataset for comparing ground truth labels to predictions_en.srt

013 Confirming our model's predictions are in the same order as the test labels.mp4

013 Confirming our model's predictions are in the same order as the test labels_en.srt

014 Creating a confusion matrix for our model's 101 different classes.mp4

014 Creating a confusion matrix for our model's 101 different classes_en.srt

015 Evaluating every individual class in our dataset.mp4

015 Evaluating every individual class in our dataset_en.srt

016 Plotting our model's F1-scores for each separate class.mp4

016 Plotting our model's F1-scores for each separate class_en.srt

017 Creating a function to load and prepare images for making predictions.mp4

017 Creating a function to load and prepare images for making predictions_en.srt

018 Making predictions on our test images and evaluating them.mp4

018 Making predictions on our test images and evaluating them_en.srt

019 Discussing the benefits of finding your model's most wrong predictions.mp4

019 Discussing the benefits of finding your model's most wrong predictions_en.srt

020 Writing code to uncover our model's most wrong predictions.mp4

020 Writing code to uncover our model's most wrong predictions_en.srt

021 Plotting and visualising the samples our model got most wrong.mp4

021 Plotting and visualising the samples our model got most wrong_en.srt

022 Making predictions on and plotting our own custom images.mp4

022 Making predictions on and plotting our own custom images_en.srt

023 Transfer Learning in TensorFlow Part 3 challenge, exercises and extra-curriculum.html

/.../0. Websites you may like/

[CourseClub.Me].url

[FreeCourseSite.com].url

[GigaCourse.Com].url

/09 - Milestone Project 1 Food Vision Big™/

001 Introduction to Milestone Project 1 Food Vision Big™.mp4

001 Introduction to Milestone Project 1 Food Vision Big™_en.srt

002 Making sure we have access to the right GPU for mixed precision training.mp4

002 Making sure we have access to the right GPU for mixed precision training_en.srt

003 Getting helper functions ready.mp4

003 Getting helper functions ready_en.srt

004 Introduction to TensorFlow Datasets (TFDS).mp4

004 Introduction to TensorFlow Datasets (TFDS)_en.srt

005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4

005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets)_en.srt

006 Creating a preprocessing function to prepare our data for modelling.mp4

006 Creating a preprocessing function to prepare our data for modelling_en.srt

007 Batching and preparing our datasets (to make them run fast).mp4

007 Batching and preparing our datasets (to make them run fast)_en.srt

008 Exploring what happens when we batch and prefetch our data.mp4

008 Exploring what happens when we batch and prefetch our data_en.srt

009 Creating modelling callbacks for our feature extraction model.mp4

009 Creating modelling callbacks for our feature extraction model_en.srt

010 Note Mixed Precision producing errors for TensorFlow 2.5+.html

011 Turning on mixed precision training with TensorFlow.mp4

011 Turning on mixed precision training with TensorFlow_en.srt

012 Creating a feature extraction model capable of using mixed precision training.mp4

012 Creating a feature extraction model capable of using mixed precision training_en.srt

013 Checking to see if our model is using mixed precision training layer by layer.mp4

013 Checking to see if our model is using mixed precision training layer by layer_en.srt

014 Training and evaluating a feature extraction model (Food Vision Big™).mp4

014 Training and evaluating a feature extraction model (Food Vision Big™)_en.srt

015 Introducing your Milestone Project 1 challenge build a model to beat DeepFood.mp4

015 Introducing your Milestone Project 1 challenge build a model to beat DeepFood_en.srt

016 Milestone Project 1 Food Vision Big™, exercises and extra-curriculum.html

/10 - NLP Fundamentals in TensorFlow/

001 Welcome to natural language processing with TensorFlow!.html

002 Introduction to Natural Language Processing (NLP) and Sequence Problems.mp4

002 Introduction to Natural Language Processing (NLP) and Sequence Problems_en.srt

003 Example NLP inputs and outputs.mp4

003 Example NLP inputs and outputs_en.srt

004 The typical architecture of a Recurrent Neural Network (RNN).mp4

004 The typical architecture of a Recurrent Neural Network (RNN)_en.srt

005 Preparing a notebook for our first NLP with TensorFlow project.mp4

005 Preparing a notebook for our first NLP with TensorFlow project_en.srt

006 Becoming one with the data and visualising a text dataset.mp4

006 Becoming one with the data and visualising a text dataset_en.srt

007 Splitting data into training and validation sets.mp4

007 Splitting data into training and validation sets_en.srt

008 Converting text data to numbers using tokenisation and embeddings (overview).mp4

008 Converting text data to numbers using tokenisation and embeddings (overview)_en.srt

009 Setting up a TensorFlow TextVectorization layer to convert text to numbers.mp4

009 Setting up a TensorFlow TextVectorization layer to convert text to numbers_en.srt

010 Mapping the TextVectorization layer to text data and turning it into numbers.mp4

010 Mapping the TextVectorization layer to text data and turning it into numbers_en.srt

011 Creating an Embedding layer to turn tokenised text into embedding vectors.mp4

011 Creating an Embedding layer to turn tokenised text into embedding vectors_en.srt

012 Discussing the various modelling experiments we're going to run.mp4

012 Discussing the various modelling experiments we're going to run_en.srt

013 Model 0 Building a baseline model to try and improve upon.mp4

013 Model 0 Building a baseline model to try and improve upon_en.srt

014 Creating a function to track and evaluate our model's results.mp4

014 Creating a function to track and evaluate our model's results_en.srt

015 Model 1 Building, fitting and evaluating our first deep model on text data.mp4

015 Model 1 Building, fitting and evaluating our first deep model on text data_en.srt

016 Visualising our model's learned word embeddings with TensorFlow's projector tool.mp4

016 Visualising our model's learned word embeddings with TensorFlow's projector tool_en.srt

017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more.mp4

017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more_en.srt

018 Model 2 Building, fitting and evaluating our first TensorFlow RNN model (LSTM).mp4

018 Model 2 Building, fitting and evaluating our first TensorFlow RNN model (LSTM)_en.srt

019 Model 3 Building, fitting and evaluating a GRU-cell powered RNN.mp4

019 Model 3 Building, fitting and evaluating a GRU-cell powered RNN_en.srt

020 Model 4 Building, fitting and evaluating a bidirectional RNN model.mp4

020 Model 4 Building, fitting and evaluating a bidirectional RNN model_en.srt

021 Discussing the intuition behind Conv1D neural networks for text and sequences.mp4

021 Discussing the intuition behind Conv1D neural networks for text and sequences_en.srt

022 Model 5 Building, fitting and evaluating a 1D CNN for text.mp4

022 Model 5 Building, fitting and evaluating a 1D CNN for text_en.srt

023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP).mp4

023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP)_en.srt

024 Model 6 Building, training and evaluating a transfer learning model for NLP.mp4

024 Model 6 Building, training and evaluating a transfer learning model for NLP_en.srt

025 Preparing subsets of data for model 7 (same as model 6 but 10% of data).mp4

025 Preparing subsets of data for model 7 (same as model 6 but 10% of data)_en.srt

026 Model 7 Building, training and evaluating a transfer learning model on 10% data.mp4

026 Model 7 Building, training and evaluating a transfer learning model on 10% data_en.srt

027 Fixing our data leakage issue with model 7 and retraining it.mp4

027 Fixing our data leakage issue with model 7 and retraining it_en.srt

028 Comparing all our modelling experiments evaluation metrics.mp4

028 Comparing all our modelling experiments evaluation metrics_en.srt

029 Uploading our model's training logs to TensorBoard and comparing them.mp4

029 Uploading our model's training logs to TensorBoard and comparing them_en.srt

030 Saving and loading in a trained NLP model with TensorFlow.mp4

030 Saving and loading in a trained NLP model with TensorFlow_en.srt

031 Downloading a pretrained model and preparing data to investigate predictions.mp4

031 Downloading a pretrained model and preparing data to investigate predictions_en.srt

032 Visualising our model's most wrong predictions.mp4

032 Visualising our model's most wrong predictions_en.srt

033 Making and visualising predictions on the test dataset.mp4

033 Making and visualising predictions on the test dataset_en.srt

034 Understanding the concept of the speedscore tradeoff.mp4

034 Understanding the concept of the speedscore tradeoff_en.srt

035 NLP Fundamentals in TensorFlow challenge, exercises and extra-curriculum.html

/11 - Milestone Project 2 SkimLit/

001 Introduction to Milestone Project 2 SkimLit.mp4

001 Introduction to Milestone Project 2 SkimLit_en.srt

002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts).mp4

002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts)_en.srt

003 SkimLit inputs and outputs.mp4

003 SkimLit inputs and outputs_en.srt

004 Setting up our notebook for Milestone Project 2 (getting the data).mp4

004 Setting up our notebook for Milestone Project 2 (getting the data)_en.srt

005 Visualising examples from the dataset (becoming one with the data).mp4

005 Visualising examples from the dataset (becoming one with the data)_en.srt

006 Writing a preprocessing function to structure our data for modelling.mp4

006 Writing a preprocessing function to structure our data for modelling_en.srt

007 Performing visual data analysis on our preprocessed text.mp4

007 Performing visual data analysis on our preprocessed text_en.srt

008 Turning our target labels into numbers (ML models require numbers).mp4

008 Turning our target labels into numbers (ML models require numbers)_en.srt

009 Model 0 Creating, fitting and evaluating a baseline model for SkimLit.mp4

009 Model 0 Creating, fitting and evaluating a baseline model for SkimLit_en.srt

010 Preparing our data for deep sequence models.mp4

010 Preparing our data for deep sequence models_en.srt

011 Creating a text vectoriser to map our tokens (text) to numbers.mp4

011 Creating a text vectoriser to map our tokens (text) to numbers_en.srt

012 Creating a custom token embedding layer with TensorFlow.mp4

012 Creating a custom token embedding layer with TensorFlow_en.srt

013 Creating fast loading dataset with the TensorFlow tf.data API.mp4

013 Creating fast loading dataset with the TensorFlow tf.data API_en.srt

014 Model 1 Building, fitting and evaluating a Conv1D with token embeddings.mp4

014 Model 1 Building, fitting and evaluating a Conv1D with token embeddings_en.srt

015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2.mp4

015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2_en.srt

016 Model 2 Building, fitting and evaluating a Conv1D model with token embeddings.mp4

016 Model 2 Building, fitting and evaluating a Conv1D model with token embeddings_en.srt

017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer.mp4

017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer_en.srt

018 Creating a character-level embedding layer with tf.keras.layers.Embedding.mp4

018 Creating a character-level embedding layer with tf.keras.layers.Embedding_en.srt

019 Model 3 Building, fitting and evaluating a Conv1D model on character embeddings.mp4

019 Model 3 Building, fitting and evaluating a Conv1D model on character embeddings_en.srt

020 Discussing how we're going to build Model 4 (character + token embeddings).mp4

020 Discussing how we're going to build Model 4 (character + token embeddings)_en.srt

021 Model 4 Building a multi-input model (hybrid token + character embeddings).mp4

021 Model 4 Building a multi-input model (hybrid token + character embeddings)_en.srt

022 Model 4 Plotting and visually exploring different data inputs.mp4

022 Model 4 Plotting and visually exploring different data inputs_en.srt

023 Crafting multi-input fast loading tf.data datasets for Model 4.mp4

023 Crafting multi-input fast loading tf.data datasets for Model 4_en.srt

024 Model 4 Building, fitting and evaluating a hybrid embedding model.mp4

024 Model 4 Building, fitting and evaluating a hybrid embedding model_en.srt

025 Model 5 Adding positional embeddings via feature engineering (overview).mp4

025 Model 5 Adding positional embeddings via feature engineering (overview)_en.srt

026 Encoding the line number feature to used with Model 5.mp4

026 Encoding the line number feature to used with Model 5_en.srt

027 Encoding the total lines feature to be used with Model 5.mp4

027 Encoding the total lines feature to be used with Model 5_en.srt

028 Model 5 Building the foundations of a tribrid embedding model.mp4

028 Model 5 Building the foundations of a tribrid embedding model_en.srt

029 Model 5 Completing the build of a tribrid embedding model for sequences.mp4

029 Model 5 Completing the build of a tribrid embedding model for sequences_en.srt

030 Visually inspecting the architecture of our tribrid embedding model.mp4

030 Visually inspecting the architecture of our tribrid embedding model_en.srt

031 Creating multi-level data input pipelines for Model 5 with the tf.data API.mp4

031 Creating multi-level data input pipelines for Model 5 with the tf.data API_en.srt

032 Bringing SkimLit to life!!! (fitting and evaluating Model 5).mp4

032 Bringing SkimLit to life!!! (fitting and evaluating Model 5)_en.srt

033 Comparing the performance of all of our modelling experiments.mp4

033 Comparing the performance of all of our modelling experiments_en.srt

034 Saving, loading & testing our best performing model.mp4

034 Saving, loading & testing our best performing model_en.srt

035 Congratulations and your challenge before heading to the next module.mp4

035 Congratulations and your challenge before heading to the next module_en.srt

036 Milestone Project 2 (SkimLit) challenge, exercises and extra-curriculum.html

/12 - Time Series fundamentals in TensorFlow + Milestone Project 3 BitPredict/

001 Welcome to time series fundamentals with TensorFlow + Milestone Project 3!.html

002 Introduction to Milestone Project 3 (BitPredict) & where you can get help.mp4

002 Introduction to Milestone Project 3 (BitPredict) & where you can get help_en.srt

003 What is a time series problem and example forecasting problems at Uber.mp4

003 What is a time series problem and example forecasting problems at Uber_en.srt

004 Example forecasting problems in daily life.mp4

004 Example forecasting problems in daily life_en.srt

005 What can be forecast.mp4

005 What can be forecast_en.srt

006 What we're going to cover (broadly).mp4

006 What we're going to cover (broadly)_en.srt

007 Time series forecasting inputs and outputs.mp4

007 Time series forecasting inputs and outputs_en.srt

008 Downloading and inspecting our Bitcoin historical dataset.mp4

008 Downloading and inspecting our Bitcoin historical dataset_en.srt

009 Different kinds of time series patterns & different amounts of feature variables.mp4

009 Different kinds of time series patterns & different amounts of feature variables_en.srt

010 Visualizing our Bitcoin historical data with pandas.mp4

010 Visualizing our Bitcoin historical data with pandas_en.srt

011 Reading in our Bitcoin data with Python's CSV module.mp4

011 Reading in our Bitcoin data with Python's CSV module_en.srt

012 Creating train and test splits for time series (the wrong way).mp4

012 Creating train and test splits for time series (the wrong way)_en.srt

013 Creating train and test splits for time series (the right way).mp4

013 Creating train and test splits for time series (the right way)_en.srt

014 Creating a plotting function to visualize our time series data.mp4

014 Creating a plotting function to visualize our time series data_en.srt

015 Discussing the various modelling experiments were going to be running.mp4

015 Discussing the various modelling experiments were going to be running_en.srt

016 Model 0 Making and visualizing a naive forecast model.mp4

016 Model 0 Making and visualizing a naive forecast model_en.srt

017 Discussing some of the most common time series evaluation metrics.mp4

017 Discussing some of the most common time series evaluation metrics_en.srt

018 Implementing MASE with TensorFlow.mp4

018 Implementing MASE with TensorFlow_en.srt

019 Creating a function to evaluate our model's forecasts with various metrics.mp4

019 Creating a function to evaluate our model's forecasts with various metrics_en.srt

020 Discussing other non-TensorFlow kinds of time series forecasting models.mp4

020 Discussing other non-TensorFlow kinds of time series forecasting models_en.srt

021 Formatting data Part 2 Creating a function to label our windowed time series.mp4

021 Formatting data Part 2 Creating a function to label our windowed time series_en.srt

022 Discussing the use of windows and horizons in time series data.mp4

022 Discussing the use of windows and horizons in time series data_en.srt

023 Writing a preprocessing function to turn time series data into windows & labels.mp4

023 Writing a preprocessing function to turn time series data into windows & labels_en.srt

024 Turning our windowed time series data into training and test sets.mp4

024 Turning our windowed time series data into training and test sets_en.srt

025 Creating a modelling checkpoint callback to save our best performing model.mp4

025 Creating a modelling checkpoint callback to save our best performing model_en.srt

026 Model 1 Building, compiling and fitting a deep learning model on Bitcoin data.mp4

026 Model 1 Building, compiling and fitting a deep learning model on Bitcoin data_en.srt

027 Creating a function to make predictions with our trained models.mp4

027 Creating a function to make predictions with our trained models_en.srt

028 Model 2 Building, fitting and evaluating a deep model with a larger window size.mp4

028 Model 2 Building, fitting and evaluating a deep model with a larger window size_en.srt

029 Model 3 Building, fitting and evaluating a model with a larger horizon size.mp4

029 Model 3 Building, fitting and evaluating a model with a larger horizon size_en.srt

030 Adjusting the evaluation function to work for predictions with larger horizons.mp4

030 Adjusting the evaluation function to work for predictions with larger horizons_en.srt

031 Model 3 Visualizing the results.mp4

031 Model 3 Visualizing the results_en.srt

032 Comparing our modelling experiments so far and discussing autocorrelation.mp4

032 Comparing our modelling experiments so far and discussing autocorrelation_en.srt

033 Preparing data for building a Conv1D model.mp4

033 Preparing data for building a Conv1D model_en.srt

034 Model 4 Building, fitting and evaluating a Conv1D model on our Bitcoin data.mp4

034 Model 4 Building, fitting and evaluating a Conv1D model on our Bitcoin data_en.srt

035 Model 5 Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data.mp4

035 Model 5 Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data_en.srt

036 Investigating how to turn our univariate time series into multivariate.mp4

036 Investigating how to turn our univariate time series into multivariate_en.srt

037 Creating and plotting a multivariate time series with BTC price and block reward.mp4

037 Creating and plotting a multivariate time series with BTC price and block reward_en.srt

038 Preparing our multivariate time series for a model.mp4

038 Preparing our multivariate time series for a model_en.srt

039 Model 6 Building, fitting and evaluating a multivariate time series model.mp4

039 Model 6 Building, fitting and evaluating a multivariate time series model_en.srt

040 Model 7 Discussing what we're going to be doing with the N-BEATS algorithm.mp4

040 Model 7 Discussing what we're going to be doing with the N-BEATS algorithm_en.srt

041 Model 7 Replicating the N-BEATS basic block with TensorFlow layer subclassing.mp4

041 Model 7 Replicating the N-BEATS basic block with TensorFlow layer subclassing_en.srt

042 Model 7 Testing our N-BEATS block implementation with dummy data inputs.mp4

042 Model 7 Testing our N-BEATS block implementation with dummy data inputs_en.srt

043 Model 7 Creating a performant data pipeline for the N-BEATS model with tf.data.mp4

043 Model 7 Creating a performant data pipeline for the N-BEATS model with tf.data_en.srt

044 Model 7 Setting up hyperparameters for the N-BEATS algorithm.mp4

044 Model 7 Setting up hyperparameters for the N-BEATS algorithm_en.srt

045 Model 7 Getting ready for residual connections.mp4

045 Model 7 Getting ready for residual connections_en.srt

046 Model 7 Outlining the steps we're going to take to build the N-BEATS model.mp4

046 Model 7 Outlining the steps we're going to take to build the N-BEATS model_en.srt

047 Model 7 Putting together the pieces of the puzzle of the N-BEATS model.mp4

047 Model 7 Putting together the pieces of the puzzle of the N-BEATS model_en.srt

048 Model 7 Plotting the N-BEATS algorithm we've created and admiring its beauty.mp4

048 Model 7 Plotting the N-BEATS algorithm we've created and admiring its beauty_en.srt

049 Model 8 Ensemble model overview.mp4

049 Model 8 Ensemble model overview_en.srt

050 Model 8 Building, compiling and fitting an ensemble of models.mp4

050 Model 8 Building, compiling and fitting an ensemble of models_en.srt

051 Model 8 Making and evaluating predictions with our ensemble model.mp4

051 Model 8 Making and evaluating predictions with our ensemble model_en.srt

052 Discussing the importance of prediction intervals in forecasting.mp4

052 Discussing the importance of prediction intervals in forecasting_en.srt

053 Getting the upper and lower bounds of our prediction intervals.mp4

053 Getting the upper and lower bounds of our prediction intervals_en.srt

054 Plotting the prediction intervals of our ensemble model predictions.mp4

054 Plotting the prediction intervals of our ensemble model predictions_en.srt

055 (Optional) Discussing the types of uncertainty in machine learning.mp4

055 (Optional) Discussing the types of uncertainty in machine learning_en.srt

056 Model 9 Preparing data to create a model capable of predicting into the future.mp4

056 Model 9 Preparing data to create a model capable of predicting into the future_en.srt

057 Model 9 Building, compiling and fitting a future predictions model.mp4

057 Model 9 Building, compiling and fitting a future predictions model_en.srt

058 Model 9 Discussing what's required for our model to make future predictions.mp4

058 Model 9 Discussing what's required for our model to make future predictions_en.srt