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Course Outline
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What is deep learning?
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Why use deep learning?
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What are neural networks?
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What is deep learning already being used for?
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What is and why use TensorFlow?
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What is a Tensor?
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What we're going to cover throughout the course
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How to approach this course
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Creating your first tensors with TensorFlow and tf.constant()
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Creating tensors with TensorFlow and tf.Variable()
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Creating random tensors with TensorFlow
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Shuffling the order of tensors
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Creating tensors from NumPy arrays
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Getting information from your tensors (tensor attributes
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Indexing and expanding tensors
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Manipulating tensors with basic operations
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Matrix multiplication with tensors part 1
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Matrix multiplication with tensors part 2
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Matrix multiplication with tensors part 3
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Changing the datatype of tensors
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Tensor aggregation (finding the min, max, mean & more)
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Tensor troubleshooting example (updating tensor datatypes)
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Finding the positional minimum and maximum of a tensor (argmin and argmax) (9:31)
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Squeezing a tensor (removing all 1-dimension axes)
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One-hot encoding tensors
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Trying out more tensor math operations
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Exploring TensorFlow and NumPy's compatibility
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Making sure our tensor operations run really fast on GPUs
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Introduction to Neural Network Regression with TensorFlow
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Inputs and outputs of a neural network regression model
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Anatomy and architecture of a neural network regression model
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Creating sample regression data (so we can model it)
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The major steps in modelling with TensorFlow
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Steps in improving a model with TensorFlow part 1
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Steps in improving a model with TensorFlow part 2
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Steps in improving a model with TensorFlow part 3
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Evaluating a TensorFlow model part 1 ("visualise, visualise, visualise")
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Evaluating a TensorFlow model part 2 (the three datasets)
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Evaluating a TensorFlow model part 3 (getting a model summary)
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Evaluating a TensorFlow model part 4 (visualising a model's layers)
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Evaluating a TensorFlow model part 5 (visualising a model's predictions)
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Evaluating a TensorFlow model part 6 (common regression evaluation metrics)
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Evaluating a TensorFlow regression model part 7 (mean absolute error)
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Evaluating a TensorFlow regression model part 7 (mean square error)
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Setting up TensorFlow modelling experiments part 1 (start with a simple model)
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Setting up TensorFlow modelling experiments part 2 (increasing complexity)
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Comparing and tracking your TensorFlow modelling experiments
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How to save a TensorFlow model
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How to load and use a saved TensorFlow model
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Putting together what we've learned part 1 (preparing a dataset)
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Putting together what we've learned part 2 (building a regression model)
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Putting together what we've learned part 3 (improving our regression model)
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Preprocessing data with feature scaling part 1 (what is feature scaling?)
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Preprocessing data with feature scaling part 2 (normalising our data)
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Preprocessing data with feature scaling part 3 (fitting a model on scaled data)
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Introduction to neural network classification in TensorFlow
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Example classification problems (and their inputs and outputs)
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Input and output tensors of classification problems
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Typical architecture of neural network classification models with TensorFlow
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Creating and viewing classification data to model
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Checking the input and output shapes of our classification data
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Building a not very good classification model with TensorFlow
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Trying to improve our not very good classification model
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Creating a function to view our model's not so good predictions
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Make our poor classification model work for a regression dataset
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Non-linearity part 1: Straight lines and non-straight lines
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Non-linearity part 2: Building our first neural network with non-linearity
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Non-linearity part 3: Upgrading our non-linear model with more layers
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Non-linearity part 4: Modelling our non-linear data once and for all
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Non-linearity part 5: Replicating non-linear activation functions from scratch
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Getting great results in less time by tweaking the learning rate
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Using the TensorFlow History object to plot a model's loss curves
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Using callbacks to find a model's ideal learning rate
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Training and evaluating a model with an ideal learning rate
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Introducing more classification evaluation methods
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Finding the accuracy of our classification model
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Creating our first confusion matrix (to see where our model is getting confused)
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Making our confusion matrix prettier
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Putting things together with multi-class classification part 1: Getting the data
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Multi-class classification part 2: Becoming one with the data
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Multi-class classification part 3: Building a multi-class classification model
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Multi-class classification part 4: Improving performance with normalisation
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Multi-class classification part 5: Comparing normalised and non-normalised data
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Multi-class classification part 6: Finding the ideal learning rate
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Multi-class classification part 7: Evaluating our model
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Multi-class classification part 8: Creating a confusion matrix
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Multi-class classification part 9: Visualising random model predictions
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What "patterns" is our model learning?
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Introduction to Computer Vision with TensorFlow
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Introduction to Convolutional Neural Networks (CNNs) with TensorFlow
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Becoming One With Data
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Becoming One With Data Part 2
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Becoming One With Data Part 3
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Building an end to end CNN Model
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Using a GPU to run our CNN model 5x faster
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Trying a non-CNN model on our image data
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Improving our non-CNN model by adding more layers
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Breaking our CNN model down part 1: Becoming one with the data
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Breaking our CNN model down part 2: Preparing to load our data
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Breaking our CNN model down part 3: Loading our data with ImageDataGenerator
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Breaking our CNN model down part 4: Building a baseline CNN model
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Breaking our CNN model down part 5: Looking inside a Conv2D layer
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Breaking our CNN model down part 6: Compiling and fitting our baseline CNN
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Breaking our CNN model down part 7: Evaluating our CNN's training curves
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Breaking our CNN model down part 8: Reducing overfitting with Max Pooling
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Breaking our CNN model down part 9: Reducing overfitting with data augmentation
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Breaking our CNN model down part 10: Visualizing our augmented data
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Breaking our CNN model down part 11: Training a CNN model on augmented data
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Breaking our CNN model down part 12: Discovering the power of shuffling data
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Breaking our CNN model down part 13: Exploring options to improve our model
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Writing a helper function to load and preprocessing custom images
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Making a prediction on a custom image with our trained CNN
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Multi-class CNN's part 1: Becoming one with the data
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Multi-class CNN's part 2: Preparing our data (turning it into tensors)
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Multi-class CNN's part 3: Building a multi-class CNN model
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Multi-class CNN's part 4: Fitting a multi-class CNN model to the data
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Multi-class CNN's part 5: Evaluating our multi-class CNN model (
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Multi-class CNN's part 6: Trying to fix overfitting by removing layers
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Multi-class CNN's part 7: Trying to fix overfitting with data augmentation
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Multi-class CNN's part 8: Things you could do to improve your CNN model
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Multi-class CNN's part 9: Making predictions with our model on custom images
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What is and why use transfer learning?
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Introducing Callbacks in TensorFlow and making a callback to track our models
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Exploring the TensorFlow Hub website for pretrained models
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Building and compiling a TensorFlow Hub feature extraction model
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Blowing our previous models out of the water with transfer learning
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Plotting the loss curves of our ResNet feature extraction model
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Building and training a pre-trained EfficientNet model on our data
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Different Types of Transfer Learning
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Comparing Our Model's Results
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Introduction to Transfer Learning in TensorFlow Part 2: Fine-tuning
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Importing a script full of helper functions (and saving lots of space)
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Discussing the four (actually five) modelling experiments we're running
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Comparing the TensorFlow Keras Sequential API versus the Functional API
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Creating our first model with the TensorFlow Keras Functional API
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Compiling and fitting our first Functional API model
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Getting a feature vector from our trained model
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Drilling into the concept of a feature vector (a learned representation)
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Building a data augmentation layer to use inside our model
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Visualising what happens when images pass through our data augmentation layer
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Building Model 1 (with a data augmentation layer and 1% of training data)
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Building Model 2 (with a data augmentation layer and 10% of training data)
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Creating a ModelCheckpoint to save our model's weights during training
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Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint)
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Preparing Model 3 (our first fine-tuned model)
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Fitting and evaluating Model 3 (our first fine-tuned model)
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Comparing our model's results before and after fine-tuning
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Preparing our final modelling experiment (Model 4)
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Fine-tuning Model 4 on 100% of the training data and evaluating its results
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Comparing our modelling experiment results in TensorBoard
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How to view and delete previous TensorBoard experiments
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Introduction to Transfer Learning Part 3: Scaling Up
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Outlining the model we're going to build and building a ModelCheckpoint callback
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Creating a data augmentation layer to use with our model
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Creating a headless EfficientNetB0 model with data augmentation built in
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Fitting and evaluating our biggest transfer learning model yet
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Unfreezing some layers in our base model to prepare for fine-tuning
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Fine-tuning our feature extraction model and evaluating its performance
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Making predictions with our trained model on 25,250 test samples
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Unravelling our test dataset for comparing ground truth labels to predictions
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Confirming our model's predictions are in the same order as the test labels
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Creating a confusion matrix for our model's 101 different classes
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Evaluating every individual class in our dataset
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Plotting our model's F1-scores for each separate class
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Creating a function to load and prepare images for making predictions
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Making predictions on our test images and evaluating them
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Discussing the benefits of finding your model's most wrong predictions
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Writing code to uncover our model's most wrong predictions
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Plotting and visualizing the samples our model got most wrong
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Making predictions on and plotting our own custom images
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Introduction to Milestone Project 1: Food Vision Big™
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Making sure we have access to the right GPU for mixed precision training
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Introduction to TensorFlow Datasets (TFDS)
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Exploring and becoming one with the data (Food101 from TensorFlow Datasets)
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Creating a preprocessing function to prepare our data for modelling
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Batching and preparing our datasets (to make them run fast)
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Exploring what happens when we batch and prefetch our data
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Creating modelling callbacks for our feature extraction model
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Turning on mixed precision training with TensorFlow
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Creating a feature extraction model capable of using mixed precision training
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Checking to see if our model is using mixed precision training layer by layer
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Training and evaluating a feature extraction model (Food Vision Big™)
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Introducing your Milestone Project 1 challenge: build a model to beat DeepFood
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Introduction to Natural Language Processing (NLP) and Sequence Problems
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Example NLP inputs and outputs
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The typical architecture of a Recurrent Neural Network (RNN)
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Preparing a notebook for our first NLP with TensorFlow project
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Becoming one with the data and visualizing a text dataset
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Splitting data into training and validation sets
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Converting text data to numbers using tokenisation and embeddings (overview)
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Setting up a TensorFlow TextVectorization layer to convert text to numbers
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Mapping the TextVectorization layer to text data and turning it into numbers
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Creating an Embedding layer to turn tokenised text into embedding vectors
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Discussing the various modelling experiments we're going to run
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Model 0: Building a baseline model to try and improve upon
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Creating a function to track and evaluate our model's results
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Model 1: Building, fitting and evaluating our first deep model on text data
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Visualizing our model's learned word embeddings with TensorFlow's projector tool
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High-level overview of Recurrent Neural Networks (RNNs) + where to learn more
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Model 2: Building, fitting and evaluating our first TensorFlow RNN model (LSTM)
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Model 3: Building, fitting and evaluating a GRU-cell powered RNN
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Model 4: Building, fitting and evaluating a bidirectional RNN model
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Discussing the intuition behind Conv1D neural networks for text and sequences
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Model 5: Building, fitting and evaluating a 1D CNN for text
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Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP)
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Model 6: Building, training and evaluating a transfer learning model for NLP
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Preparing subsets of data for model 7 (same as model 6 but 10% of data)
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Model 7: Building, training and evaluating a transfer learning model on 10% data
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Fixing our data leakage issue with model 7 and retraining it
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Comparing all our modelling experiments evaluation metrics
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Visualizing our model's most wrong predictions
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Making and visualizing predictions on the test dataset
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Understanding the concept of the speed/score tradeoff
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Introduction to Milestone Project 2: SkimLit
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What we're going to cover in Milestone Project 2 (NLP for medical abstracts)
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SkimLit inputs and outputs
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Setting up our notebook for Milestone Project 2 (getting the data)
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Visualizing examples from the dataset (becoming one with the data)
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Writing a preprocessing function to structure our data for modelling
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Performing visual data analysis on our preprocessed text
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Turning our target labels into numbers (ML models require numbers)
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Model 0: Creating, fitting and evaluating a baseline model for SkimLit
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Preparing our data for deep sequence models
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Creating a text vectoriser to map our tokens (text) to numbers
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Creating a custom token embedding layer with TensorFlow
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Model 1: Building, fitting and evaluating a Conv1D with token embeddings
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Preparing a pretrained embedding layer from TensorFlow Hub for Model 2
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Model 2: Building, fitting and evaluating a Conv1D model with token embeddings
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Creating a character-level tokeniser with TensorFlow's TextVectorization layer
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Creating a character-level embedding layer with tf.keras.layers.Embedding
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Model 3: Building, fitting and evaluating a Conv1D model on character embeddings
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Discussing how we're going to build Model 4 (character + token embeddings)
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Model 4: Building a multi-input model (hybrid token + character embeddings)
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Model 4: Plotting and visually exploring different data inputs
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Model 4: Building, fitting and evaluating a hybrid embedding model
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Model 5: Adding positional embeddings via feature engineering (overview)
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Encoding the line number feature to used with Model 5
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Encoding the total lines feature to be used with Model 5
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Model 5: Building the foundations of a tribrid embedding model
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Model 5: Completing the build of a tribrid embedding model for sequences
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Visually inspecting the architecture of our tribrid embedding model
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Creating multi-level data input pipelines for Model 5 with the tf.data API
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Bringing SkimLit to life!!! (fitting and evaluating Model 5)
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Comparing the performance of all of our modelling experiments
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Introduction to Milestone Project 3 (BitPredict) & where you can get help
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What is a time series problem and example forecasting problems at Uber
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Example forecasting problems in daily life
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What can be forecast?
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What we're going to cover (broadly)
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Time series forecasting inputs and outputs
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Different kinds of time series patterns & different amounts of feature variables
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Visualizing our Bitcoin historical data with pandas
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Reading in our Bitcoin data with Python's CSV module
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Creating train and test splits for time series (the wrong way)
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Creating train and test splits for time series (the right way)
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Creating a plotting function to visualize our time series data
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Discussing the various modelling experiments were going to be running
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Model 0: Making and visualizing a naive forecast model
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Discussing some of the most common time series evaluation metrics
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Implementing MASE with TensorFlow
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Creating a function to evaluate our model's forecasts with various metrics
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Discussing other non-TensorFlow kinds of time series forecasting models
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Formatting data Part 2: Creating a function to label our windowed time series
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Discussing the use of windows and horizons in time series data
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Writing a preprocessing function to turn time series data into windows & labels
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Turning our windowed time series data into training and test sets
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Creating a modelling checkpoint callback to save our best performing model
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Model 1: Building, compiling and fitting a deep learning model on Bitcoin data
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Creating a function to make predictions with our trained models
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Model 2: Building, fitting and evaluating a deep model with a larger window size-27
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Model 3: Building, fitting and evaluating a model with a larger horizon size
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Adjusting the evaluation function to work for predictions with larger horizons
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Model 3: Visualizing the results
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Comparing our modelling experiments so far and discussing autocorrelation
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Preparing data for building a Conv1D model
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Model 4: Building, fitting and evaluating a Conv1D model on our Bitcoin data
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Model 5: Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data
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Investigating how to turn our univariate time series into multivariate
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Creating and plotting a multivariate time series with BTC price and block reward
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Preparing our multivariate time series for a model
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Model 6: Building, fitting and evaluating a multivariate time series model
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Model 7: Discussing what we're going to be doing with the N-BEATS algorithm
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Model 7: Replicating the N-BEATS basic block with TensorFlow layer subclassing
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Model 7: Testing our N-BEATS block implementation with dummy data inputs
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Model 7: Setting up hyperparameters for the N-BEATS algorithm
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Model 7: Getting ready for residual connections
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Model 7: Outlining the steps we're going to take to build the N-BEATS model
• Урок 310. 00:22:23
Model 7: Putting together the pieces of the puzzle of the N-BEATS model
• Урок 311. 00:06:47
Model 7: Plotting the N-BEATS algorithm we've created and admiring its beauty
• Урок 312. 00:04:44
Model 8: Ensemble model overview
• Урок 313. 00:20:05
Model 8: Building, compiling and fitting an ensemble of models
• Урок 314. 00:16:10
Model 8: Making and evaluating predictions with our ensemble model
• Урок 315. 00:12:57
Discussing the importance of prediction intervals in forecasting
• Урок 316. 00:07:58
Getting the upper and lower bounds of our prediction intervals
• Урок 317. 00:13:03
Plotting the prediction intervals of our ensemble model predictions
• Урок 318. 00:13:42
(Optional) Discussing the types of uncertainty in machine learning
• Урок 319. 00:08:25
Model 9: Preparing data to create a model capable of predicting into the future
• Урок 320. 00:05:02
Model 9: Building, compiling and fitting a future predictions model
• Урок 321. 00:08:31
Model 9: Discussing what's required for our model to make future predictions
• Урок 322. 00:12:09
Model 9: Creating a function to make forecasts into the future
• Урок 323. 00:13:10
Model 9: Plotting our model's future forecasts
• Урок 324. 00:14:16
Model 10: Introducing the turkey problem and making data for it
• Урок 325. 00:13:39
Model 10: Building a model to predict on turkey data (why forecasting is BS)
• Урок 326. 00:13:00
Comparing the results of all of our models and discussing where to go next
• Урок 327. 00:05:29
What is the TensorFlow Developer Certification?
• Урок 328. 00:06:58
Why the TensorFlow Developer Certification?
• Урок 329. 00:08:15
How to prepare (your brain) for the TensorFlow Developer Certification
• Урок 330. 00:12:44
How to prepare (your computer) for the TensorFlow Developer Certification
• Урок 331. 00:02:14
What to do after the TensorFlow Developer Certification exam
• Урок 332. 00:04:52
What is Machine Learning?
• Урок 333. 00:06:53
AI/Machine Learning/Data Science
• Урок 334. 00:06:17
Exercise: Machine Learning Playground
• Урок 335. 00:06:04
How Did We Get Here?
• Урок 336. 00:04:25
• Урок 337. 00:04:42
Types of Machine Learning
• Урок 338. 00:04:45
What Is Machine Learning? Round 2
• Урок 339. 00:01:49
Section Review
• Урок 340. 00:02:39
Section Overview
• Урок 341. 00:03:09
Introducing Our Framework
• Урок 342. 00:05:00
6 Step Machine Learning Framework
• Урок 343. 00:10:33
Types of Machine Learning Problems
• Урок 344. 00:04:51
Types of Data
• Урок 345. 00:03:32
Types of Evaluation
• Урок 346. 00:05:59
Features In Data
• Урок 347. 00:05:23
Modelling - Splitting Data
• Урок 348. 00:04:36
Modelling - Picking the Model
• Урок 349. 00:03:18
Modelling - Tuning
• Урок 350. 00:03:36
Modelling - Comparison
• Урок 351. 00:09:33
Experimentation
• Урок 352. 00:04:01
Tools We Will Use
• Урок 353. 00:02:28
Section Overview
• Урок 354. 00:04:30
Pandas Introduction
• Урок 355. 00:13:22
Series, Data Frames and CSVs
• Урок 356. 00:09:49
Describing Data with Pandas
• Урок 357. 00:11:09
Selecting and Viewing Data with Pandas
• Урок 358. 00:13:07
Selecting and Viewing Data with Pandas Part 2
• Урок 359. 00:13:57
Manipulating Data
• Урок 360. 00:09:57
Manipulating Data 2
• Урок 361. 00:10:13
Manipulating Data 3
• Урок 362. 00:02:41
• Урок 363. 00:07:44
Section Overview
• Урок 364. 00:05:18
NumPy Introduction
• Урок 365. 00:14:06
NumPy DataTypes and Attributes
• Урок 366. 00:09:23
Creating NumPy Arrays
• Урок 367. 00:07:18
NumPy Random Seed
• Урок 368. 00:09:36
Viewing Arrays and Matrices
• Урок 369. 00:11:32
Manipulating Arrays
• Урок 370. 00:09:45
Manipulating Arrays 2
• Урок 371. 00:07:11
Standard Deviation and Variance
• Урок 372. 00:07:27
Reshape and Transpose
• Урок 373. 00:11:46
Dot Product vs Element Wise
• Урок 374. 00:03:34
Exercise: Nut Butter Store Sales
• Урок 375. 00:13:05
Comparison Operators
• Урок 376. 00:06:20
Sorting Arrays
• Урок 377. 00:07:38
Turn Images Into NumPy Arrays