Udemy TensorFlow for Deep Learning Bootcamp

Files Included :

001 Course Outline.mp4 (60.45 MB)
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002 Join Our Online Classroom!.mp4 (77.59 MB)
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005 ZTM Resources.mp4 (43.82 MB)
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001 What is deep learning.mp4 (36.22 MB)
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002 Why use deep learning.mp4 (26.16 MB)
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003 What are neural networks.mp4 (65.63 MB)
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005 What is deep learning already being used for.mp4 (64.61 MB)
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006 What is and why use TensorFlow.mp4 (69.29 MB)
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007 What is a Tensor.mp4 (19.38 MB)
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008 What we're going to cover throughout the course.mp4 (14.36 MB)
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009 How to approach this course.mp4 (24.96 MB)
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011 Creating your first tensors with TensorFlow and tf constant().mp4 (133.83 MB)
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012 Creating tensors with TensorFlow and tf Variable().mp4 (59.2 MB)
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013 Creating random tensors with TensorFlow.mp4 (88.82 MB)
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014 Shuffling the order of tensors.mp4 (76.03 MB)
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015 Creating tensors from NumPy arrays.mp4 (101.04 MB)
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016 Getting information from your tensors (tensor attributes).mp4 (86.84 MB)
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017 Indexing and expanding tensors.mp4 (85.73 MB)
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018 Manipulating tensors with basic operations.mp4 (45.95 MB)
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019 Matrix multiplication with tensors part 1.mp4 (103.39 MB)
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020 Matrix multiplication with tensors part 2.mp4 (106.93 MB)
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021 Matrix multiplication with tensors part 3.mp4 (80.48 MB)
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022 Changing the datatype of tensors.mp4 (72.64 MB)
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023 Tensor aggregation (finding the min, max, mean & more).mp4 (90.17 MB)
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024 Tensor troubleshooting example (updating tensor datatypes).mp4 (70.68 MB)
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025 Finding the positional minimum and maximum of a tensor (argmin and argmax).mp4 (97.68 MB)
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026 Squeezing a tensor (removing all 1-dimension axes).mp4 (30.1 MB)
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027 One-hot encoding tensors.mp4 (19.96 MB)
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028 Trying out more tensor math operations.mp4 (57.17 MB)
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029 Exploring TensorFlow and NumPy's compatibility.mp4 (43.6 MB)
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030 Making sure our tensor operations run really fast on GPUs.mp4 (112.43 MB)
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001 Introduction to Neural Network Regression with TensorFlow.mp4 (51.38 MB)
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002 Inputs and outputs of a neural network regression model.mp4 (50.3 MB)
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003 Anatomy and architecture of a neural network regression model.mp4 (51.87 MB)
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004 Creating sample regression data (so we can model it).mp4 (89.43 MB)
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006 The major steps in modelling with TensorFlow.mp4 (186.24 MB)
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007 Steps in improving a model with TensorFlow part 1.mp4 (39.71 MB)
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008 Steps in improving a model with TensorFlow part 2.mp4 (91.48 MB)
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009 Steps in improving a model with TensorFlow part 3.mp4 (135.67 MB)
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010 Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).mp4 (66.92 MB)
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011 Evaluating a TensorFlow model part 2 (the three datasets).mp4 (81.29 MB)
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012 Evaluating a TensorFlow model part 3 (getting a model summary).mp4 (196.42 MB)
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013 Evaluating a TensorFlow model part 4 (visualising a model's layers).mp4 (70.97 MB)
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014 Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp4 (66.57 MB)
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015 Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp4 (70.81 MB)
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016 Evaluating a TensorFlow regression model part 7 (mean absolute error).mp4 (56.52 MB)
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017 Evaluating a TensorFlow regression model part 7 (mean square error).mp4 (32.6 MB)
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018 Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4 (128 MB)
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019 Setting up TensorFlow modelling experiments part 2 (increasing complexity).mp4 (61.4 MB)
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020 Comparing and tracking your TensorFlow modelling experiments.mp4 (93.06 MB)
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021 How to save a TensorFlow model.mp4 (93.26 MB)
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022 How to load and use a saved TensorFlow model.mp4 (105.9 MB)
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023 (Optional) How to save and download files from Google Colab.mp4 (68.94 MB)
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024 Putting together what we've learned part 1 (preparing a dataset).mp4 (146.36 MB)
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025 Putting together what we've learned part 2 (building a regression model).mp4 (122.94 MB)
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026 Putting together what we've learned part 3 (improving our regression model).mp4 (156.78 MB)
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027 Preprocessing data with feature scaling part 1 (what is feature scaling).mp4 (93.61 MB)
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028 Preprocessing data with feature scaling part 2 (normalising our data).mp4 (83.12 MB)
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029 Preprocessing data with feature scaling part 3 (fitting a model on scaled data).mp4 (76.74 MB)
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001 Introduction to neural network classification in TensorFlow.mp4 (73.83 MB)
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002 Example classification problems (and their inputs and outputs).mp4 (20.43 MB)
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003 Input and output tensors of classification problems.mp4 (18.68 MB)
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004 Typical architecture of neural network classification models with TensorFlow.mp4 (113.61 MB)
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005 Creating and viewing classification data to model.mp4 (107.1 MB)
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006 Checking the input and output shapes of our classification data.mp4 (38.9 MB)
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007 Building a not very good classification model with TensorFlow.mp4 (127.17 MB)
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008 Trying to improve our not very good classification model.mp4 (85.14 MB)
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009 Creating a function to view our model's not so good predictions.mp4 (163.19 MB)
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011 Make our poor classification model work for a regression dataset.mp4 (125.83 MB)
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012 Non-linearity part 1 Straight lines and non-straight lines.mp4 (97.48 MB)
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013 Non-linearity part 2 Building our first neural network with non-linearity.mp4 (60.23 MB)
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014 Non-linearity part 3 Upgrading our non-linear model with more layers.mp4 (125.8 MB)
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015 Non-linearity part 4 Modelling our non-linear data once and for all.mp4 (98.44 MB)
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016 Non-linearity part 5 Replicating non-linear activation functions from scratch.mp4 (149 MB)
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017 Getting great results in less time by tweaking the learning rate.mp4 (137.89 MB)
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018 Using the TensorFlow History object to plot a model's loss curves.mp4 (62.92 MB)
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019 Using callbacks to find a model's ideal learning rate.mp4 (156.68 MB)
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020 Training and evaluating a model with an ideal learning rate.mp4 (89.55 MB)
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021 Introducing more classification evaluation methods.mp4 (36.78 MB)
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022 Finding the accuracy of our classification model.mp4 (33.87 MB)
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023 Creating our first confusion matrix (to see where our model is getting confused).mp4 (56.54 MB)
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024 Making our confusion matrix prettier.mp4 (114.96 MB)
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025 Putting things together with multi-class classification part 1 Getting the data.mp4 (87.22 MB)
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026 Multi-class classification part 2 Becoming one with the data.mp4 (48.8 MB)
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027 Multi-class classification part 3 Building a multi-class classification model.mp4 (143.98 MB)
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028 Multi-class classification part 4 Improving performance with normalisation.mp4 (114.43 MB)
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029 Multi-class classification part 5 Comparing normalised and non-normalised data.mp4 (18.81 MB)
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030 Multi-class classification part 6 Finding the ideal learning rate.mp4 (25.44 MB)
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031 Multi-class classification part 7 Evaluating our model.mp4 (119.38 MB)
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032 Multi-class classification part 8 Creating a confusion matrix.mp4 (34.22 MB)
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033 Multi-class classification part 9 Visualising random model predictions.mp4 (58.93 MB)
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034 What patterns is our model learning.mp4 (56.26 MB)
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001 Introduction to Computer Vision with TensorFlow.mp4 (23.3 MB)
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002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.mp4 (77.91 MB)
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003 Downloading an image dataset for our first Food Vision model.mp4 (73.2 MB)
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004 Becoming One With Data.mp4 (45.67 MB)
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005 Becoming One With Data Part 2.mp4 (90.2 MB)
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006 Becoming One With Data Part 3.mp4 (33.74 MB)
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007 Building an end to end CNN Model.mp4 (70.1 MB)
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008 Using a GPU to run our CNN model 5x faster.mp4 (117.14 MB)
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009 Trying a non-CNN model on our image data.mp4 (102.21 MB)
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010 Improving our non-CNN model by adding more layers.mp4 (108.42 MB)
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011 Breaking our CNN model down part 1 Becoming one with the data.mp4 (92.14 MB)
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012 Breaking our CNN model down part 2 Preparing to load our data.mp4 (110 MB)
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013 Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.mp4 (105.23 MB)
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014 Breaking our CNN model down part 4 Building a baseline CNN model.mp4 (87.47 MB)
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015 Breaking our CNN model down part 5 Looking inside a Conv2D layer.mp4 (190.14 MB)
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016 Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.mp4 (64.91 MB)
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017 Breaking our CNN model down part 7 Evaluating our CNN's training curves.mp4 (89.21 MB)
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018 Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.mp4 (132.37 MB)
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019 Breaking our CNN model down part 9 Reducing overfitting with data augmentation.mp4 (66.34 MB)
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020 Breaking our CNN model down part 10 Visualizing our augmented data.mp4 (160.58 MB)
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021 Breaking our CNN model down part 11 Training a CNN model on augmented data.mp4 (96.01 MB)
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022 Breaking our CNN model down part 12 Discovering the power of shuffling data.mp4 (105.23 MB)
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023 Breaking our CNN model down part 13 Exploring options to improve our model.mp4 (42.39 MB)
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024 Downloading a custom image to make predictions on.mp4 (44.31 MB)
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025 Writing a helper function to load and preprocessing custom images.mp4 (107.27 MB)
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026 Making a prediction on a custom image with our trained CNN.mp4 (100.93 MB)
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027 Multi-class CNN's part 1 Becoming one with the data.mp4 (140.95 MB)
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028 Multi-class CNN's part 2 Preparing our data (turning it into tensors).mp4 (46.62 MB)
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029 Multi-class CNN's part 3 Building a multi-class CNN model.mp4 (91.9 MB)
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030 Multi-class CNN's part 4 Fitting a multi-class CNN model to the data.mp4 (60.92 MB)
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031 Multi-class CNN's part 5 Evaluating our multi-class CNN model.mp4 (34.31 MB)
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032 Multi-class CNN's part 6 Trying to fix overfitting by removing layers.mp4 (131.54 MB)
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033 Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.mp4 (48.71 MB)
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034 Multi-class CNN's part 8 Things you could do to improve your CNN model.mp4 (35.81 MB)
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035 Multi-class CNN's part 9 Making predictions with our model on custom images.mp4 (97.9 MB)
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036 Saving and loading our trained CNN model.mp4 (70.43 MB)
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001 What is and why use transfer learning.mp4 (30.4 MB)
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002 Downloading and preparing data for our first transfer learning model.mp4 (133.27 MB)
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003 Introducing Callbacks in TensorFlow and making a callback to track our models.mp4 (95.32 MB)
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004 Exploring the TensorFlow Hub website for pretrained models.mp4 (87.72 MB)
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005 Building and compiling a TensorFlow Hub feature extraction model.mp4 (138.21 MB)
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006 Blowing our previous models out of the water with transfer learning.mp4 (101.53 MB)
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007 Plotting the loss curves of our ResNet feature extraction model.mp4 (62.18 MB)
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008 Building and training a pre-trained EfficientNet model on our data.mp4 (108.05 MB)
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009 Different Types of Transfer Learning.mp4 (113.32 MB)
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010 Comparing Our Model's Results.mp4 (53.08 MB)
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012 Exercise Imposter Syndrome.mp4 (8.97 MB)
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001 Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.mp4 (61.99 MB)
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002 Importing a script full of helper functions (and saving lots of space).mp4 (54.49 MB)
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003 Downloading and turning our images into a TensorFlow BatchDataset.mp4 (175.76 MB)
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004 Discussing the four (actually five) modelling experiments we're running.mp4 (11.15 MB)
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005 Comparing the TensorFlow Keras Sequential API versus the Functional API.mp4 (16.95 MB)
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007 Creating our first model with the TensorFlow Keras Functional API.mp4 (134.12 MB)
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008 Compiling and fitting our first Functional API model.mp4 (136.29 MB)
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009 Getting a feature vector from our trained model.mp4 (149.28 MB)
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010 Drilling into the concept of a feature vector (a learned representation).mp4 (53.28 MB)
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011 Downloading and preparing the data for Model 1 (1 percent of training data).mp4 (98.15 MB)
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012 Building a data augmentation layer to use inside our model.mp4 (118.18 MB)
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014 Visualizing what happens when images pass through our data augmentation layer.mp4 (123.3 MB)
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015 Building Model 1 (with a data augmentation layer and 1% of training data).mp4 (155.92 MB)
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016 Building Model 2 (with a data augmentation layer and 10% of training data).mp4 (161.1 MB)
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017 Creating a ModelCheckpoint to save our model's weights during training.mp4 (68.95 MB)
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018 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp4 (69.29 MB)
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019 Loading and comparing saved weights to our existing trained Model 2.mp4 (63.03 MB)
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020 Preparing Model 3 (our first fine-tuned model).mp4 (201.27 MB)
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021 Fitting and evaluating Model 3 (our first fine-tuned model).mp4 (59.53 MB)
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022 Comparing our model's results before and after fine-tuning.mp4 (84.75 MB)
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023 Downloading and preparing data for our biggest experiment yet (Model 4).mp4 (56.64 MB)
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024 Preparing our final modelling experiment (Model 4).mp4 (96.11 MB)
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025 Fine-tuning Model 4 on 100% of the training data and evaluating its results.mp4 (98.02 MB)
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026 Comparing our modelling experiment results in TensorBoard.mp4 (96.09 MB)
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027 How to view and delete previous TensorBoard experiments.mp4 (18.48 MB)
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001 Introduction to Transfer Learning Part 3 Scaling Up.mp4 (40.99 MB)
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002 Getting helper functions ready and downloading data to model.mp4 (132.57 MB)
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003 Outlining the model we're going to build and building a ModelCheckpoint callback.mp4 (29.17 MB)
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004 Creating a data augmentation layer to use with our model.mp4 (36.19 MB)
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005 Creating a headless EfficientNetB0 model with data augmentation built in.mp4 (81.39 MB)
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006 Fitting and evaluating our biggest transfer learning model yet.mp4 (60.1 MB)
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007 Unfreezing some layers in our base model to prepare for fine-tuning.mp4 (100.48 MB)
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008 Fine-tuning our feature extraction model and evaluating its performance.mp4 (66.14 MB)
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009 Saving and loading our trained model.mp4 (57.96 MB)
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010 Downloading a pretrained model to make and evaluate predictions with.mp4 (80.12 MB)
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011 Making predictions with our trained model on 25,250 test samples.mp4 (115.71 MB)
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012 Unravelling our test dataset for comparing ground truth labels to predictions.mp4 (38.11 MB)
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013 Confirming our model's predictions are in the same order as the test labels.mp4 (50.88 MB)
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014 Creating a confusion matrix for our model's 101 different classes.mp4 (162.54 MB)
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015 Evaluating every individual class in our dataset.mp4 (133.35 MB)
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016 Plotting our model's F1-scores for each separate class.mp4 (78.45 MB)
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017 Creating a function to load and prepare images for making predictions.mp4 (109.08 MB)
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018 Making predictions on our test images and evaluating them.mp4 (173.5 MB)
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019 Discussing the benefits of finding your model's most wrong predictions.mp4 (59.09 MB)
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020 Writing code to uncover our model's most wrong predictions.mp4 (110.89 MB)
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021 Plotting and visualising the samples our model got most wrong.mp4 (127.93 MB)
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022 Making predictions on and plotting our own custom images.mp4 (110.03 MB)
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001 Introduction to Milestone Project 1 Food Vision Big™.mp4 (28.07 MB)
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002 Making sure we have access to the right GPU for mixed precision training.mp4 (87.85 MB)
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003 Getting helper functions ready.mp4 (26.5 MB)
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004 Introduction to TensorFlow Datasets (TFDS).mp4 (99.42 MB)
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005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4 (116.54 MB)
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006 Creating a preprocessing function to prepare our data for modelling.mp4 (132.52 MB)
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007 Batching and preparing our datasets (to make them run fast).mp4 (133.48 MB)
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008 Exploring what happens when we batch and prefetch our data.mp4 (55.73 MB)
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009 Creating modelling callbacks for our feature extraction model.mp4 (60.3 MB)
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011 Turning on mixed precision training with TensorFlow.mp4 (109.41 MB)
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012 Creating a feature extraction model capable of using mixed precision training.mp4 (108.39 MB)
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013 Checking to see if our model is using mixed precision training layer by layer.mp4 (89.08 MB)
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014 Training and evaluating a feature extraction model (Food Vision Big™).mp4 (89.93 MB)
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015 Introducing your Milestone Project 1 challenge build a model to beat DeepFood.mp4 (91.54 MB)
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002 Introduction to Natural Language Processing (NLP) and Sequence Problems.mp4 (125.34 MB)
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003 Example NLP inputs and outputs.mp4 (27.77 MB)
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004 The typical architecture of a Recurrent Neural Network (RNN).mp4 (108.68 MB)
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005 Preparing a notebook for our first NLP with TensorFlow project.mp4 (83.11 MB)
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006 Becoming one with the data and visualising a text dataset.mp4 (162.87 MB)
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007 Splitting data into training and validation sets.mp4 (60.45 MB)
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008 Converting text data to numbers using tokenisation and embeddings (overview).mp4 (81.84 MB)
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009 Setting up a TensorFlow TextVectorization layer to convert text to numbers.mp4 (203.17 MB)
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010 Mapping the TextVectorization layer to text data and turning it into numbers.mp4 (98.85 MB)
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011 Creating an Embedding layer to turn tokenised text into embedding vectors.mp4 (137.63 MB)
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012 Discussing the various modelling experiments we're going to run.mp4 (88.08 MB)
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013 Model 0 Building a baseline model to try and improve upon.mp4 (94.83 MB)
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014 Creating a function to track and evaluate our model's results.mp4 (151.77 MB)
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015 Model 1 Building, fitting and evaluating our first deep model on text data.mp4 (210.67 MB)
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016 Visualising our model's learned word embeddings with TensorFlow's projector tool.mp4 (292.11 MB)
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017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more.mp4 (97.74 MB)
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018 Model 2 Building, fitting and evaluating our first TensorFlow RNN model (LSTM).mp4 (167.36 MB)
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019 Model 3 Building, fitting and evaluating a GRU-cell powered RNN.mp4 (170.57 MB)
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020 Model 4 Building, fitting and evaluating a bidirectional RNN model.mp4 (168.53 MB)
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021 Discussing the intuition behind Conv1D neural networks for text and sequences.mp4 (185.8 MB)
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022 Model 5 Building, fitting and evaluating a 1D CNN for text.mp4 (54.2 MB)
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023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP).mp4 (139.89 MB)
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024 Model 6 Building, training and evaluating a transfer learning model for NLP.mp4 (100.27 MB)
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025 Preparing subsets of data for model 7 (same as model 6 but 10% of data).mp4 (91.41 MB)
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026 Model 7 Building, training and evaluating a transfer learning model on 10% data.mp4 (102.51 MB)
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027 Fixing our data leakage issue with model 7 and retraining it.mp4 (169.69 MB)
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028 Comparing all our modelling experiments evaluation metrics.mp4 (117.33 MB)
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029 Uploading our model's training logs to TensorBoard and comparing them.mp4 (111.11 MB)
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030 Saving and loading in a trained NLP model with TensorFlow.mp4 (105.82 MB)
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031 Downloading a pretrained model and preparing data to investigate predictions.mp4 (132.11 MB)
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032 Visualising our model's most wrong predictions.mp4 (77.13 MB)
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033 Making and visualising predictions on the test dataset.mp4 (76.94 MB)
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034 Understanding the concept of the speedscore tradeoff.mp4 (112.62 MB)
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001 Introduction to Milestone Project 2 SkimLit.mp4 (149.5 MB)
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002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts).mp4 (71.73 MB)
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003 SkimLit inputs and outputs.mp4 (55.06 MB)
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004 Setting up our notebook for Milestone Project 2 (getting the data).mp4 (146.94 MB)
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005 Visualising examples from the dataset (becoming one with the data).mp4 (133.59 MB)
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006 Writing a preprocessing function to structure our data for modelling.mp4 (221.99 MB)
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007 Performing visual data analysis on our preprocessed text.mp4 (75.02 MB)
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008 Turning our target labels into numbers (ML models require numbers).mp4 (100.39 MB)
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009 Model 0 Creating, fitting and evaluating a baseline model for SkimLit.mp4 (81.62 MB)
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010 Preparing our data for deep sequence models.mp4 (85.54 MB)
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011 Creating a text vectoriser to map our tokens (text) to numbers.mp4 (130.95 MB)
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012 Creating a custom token embedding layer with TensorFlow.mp4 (101.34 MB)
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013 Creating fast loading dataset with the TensorFlow tf data API.mp4 (77.96 MB)
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014 Model 1 Building, fitting and evaluating a Conv1D with token embeddings.mp4 (170.8 MB)
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015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2.mp4 (127.2 MB)
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016 Model 2 Building, fitting and evaluating a Conv1D model with token embeddings.mp4 (108.1 MB)
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017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer.mp4 (171.15 MB)
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018 Creating a character-level embedding layer with tf keras layers Embedding.mp4 (27.55 MB)
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019 Model 3 Building, fitting and evaluating a Conv1D model on character embeddings.mp4 (131.91 MB)
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020 Discussing how we're going to build Model 4 (character + token embeddings).mp4 (60.34 MB)
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021 Model 4 Building a multi-input model (hybrid token + character embeddings).mp4 (186.03 MB)
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022 Model 4 Plotting and visually exploring different data inputs.mp4 (88.6 MB)
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023 Crafting multi-input fast loading tf data datasets for Model 4.mp4 (85.28 MB)
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024 Model 4 Building, fitting and evaluating a hybrid embedding model.mp4 (141.43 MB)
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025 Model 5 Adding positional embeddings via feature engineering (overview).mp4 (44.78 MB)
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026 Encoding the line number feature to used with Model 5.mp4 (113.08 MB)
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027 Encoding the total lines feature to be used with Model 5.mp4 (64.34 MB)
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028 Model 5 Building the foundations of a tribrid embedding model.mp4 (70.49 MB)
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029 Model 5 Completing the build of a tribrid embedding model for sequences.mp4 (156.23 MB)
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030 Visually inspecting the architecture of our tribrid embedding model.mp4 (108.77 MB)
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031 Creating multi-level data input pipelines for Model 5 with the tf data API.mp4 (101.46 MB)
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032 Bringing SkimLit to life!!! (fitting and evaluating Model 5).mp4 (118.04 MB)
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033 Comparing the performance of all of our modelling experiments.mp4 (78.33 MB)
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034 Saving, loading & testing our best performing model.mp4 (85.01 MB)
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035 Congratulations and your challenge before heading to the next module.mp4 (136.93 MB)
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002 Introduction to Milestone Project 3 (BitPredict) & where you can get help.mp4 (30.33 MB)
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003 What is a time series problem and example forecasting problems at Uber.mp4 (65.61 MB)
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004 Example forecasting problems in daily life.mp4 (27.1 MB)
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005 What can be forecast.mp4 (77.85 MB)
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006 What we're going to cover (broadly).mp4 (25.78 MB)
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007 Time series forecasting inputs and outputs.mp4 (29.17 MB)
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008 Downloading and inspecting our Bitcoin historical dataset.mp4 (150.07 MB)
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009 Different kinds of time series patterns & different amounts of feature variables.mp4 (67.78 MB)
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010 Visualizing our Bitcoin historical data with pandas.mp4 (42.3 MB)
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011 Reading in our Bitcoin data with Python's CSV module.mp4 (103.95 MB)
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012 Creating train and test splits for time series (the wrong way).mp4 (63.04 MB)
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013 Creating train and test splits for time series (the right way).mp4 (37.74 MB)
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014 Creating a plotting function to visualize our time series data.mp4 (59.44 MB)
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015 Discussing the various modelling experiments were going to be running.mp4 (77.68 MB)
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016 Model 0 Making and visualizing a naive forecast model.mp4 (114.79 MB)
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017 Discussing some of the most common time series evaluation metrics.mp4 (66.66 MB)
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018 Implementing MASE with TensorFlow.mp4 (41.74 MB)
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019 Creating a function to evaluate our model's forecasts with various metrics.mp4 (93.07 MB)
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020 Discussing other non-TensorFlow kinds of time series forecasting models.mp4 (60.62 MB)
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021 Formatting data Part 2 Creating a function to label our windowed time series.mp4 (109.91 MB)
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022 Discussing the use of windows and horizons in time series data.mp4 (72.79 MB)
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023 Writing a preprocessing function to turn time series data into windows & labels.mp4 (255.25 MB)
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024 Turning our windowed time series data into training and test sets.mp4 (93.57 MB)
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025 Creating a modelling checkpoint callback to save our best performing model.mp4 (65.23 MB)
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026 Model 1 Building, compiling and fitting a deep learning model on Bitcoin data.mp4 (168.99 MB)
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027 Creating a function to make predictions with our trained models.mp4 (122.55 MB)
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028 Model 2 Building, fitting and evaluating a deep model with a larger window size.mp4 (155 MB)
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029 Model 3 Building, fitting and evaluating a model with a larger horizon size.mp4 (123.54 MB)
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030 Adjusting the evaluation function to work for predictions with larger horizons.mp4 (90.46 MB)
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031 Model 3 Visualizing the results.mp4 (87.97 MB)
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032 Comparing our modelling experiments so far and discussing autocorrelation.mp4 (93.88 MB)
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033 Preparing data for building a Conv1D model.mp4 (113.81 MB)
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034 Model 4 Building, fitting and evaluating a Conv1D model on our Bitcoin data.mp4 (147.27 MB)
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035 Model 5 Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data.mp4 (169.02 MB)
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036 Investigating how to turn our univariate time series into multivariate.mp4 (121.18 MB)
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037 Creating and plotting a multivariate time series with BTC price and block reward.mp4 (74.59 MB)
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038 Preparing our multivariate time series for a model.mp4 (100.77 MB)
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039 Model 6 Building, fitting and evaluating a multivariate time series model.mp4 (82.26 MB)
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040 Model 7 Discussing what we're going to be doing with the N-BEATS algorithm.mp4 (105.39 MB)
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041 Model 7 Replicating the N-BEATS basic block with TensorFlow layer subclassing.mp4 (220.05 MB)
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042 Model 7 Testing our N-BEATS block implementation with dummy data inputs.mp4 (187.28 MB)
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043 Model 7 Creating a performant data pipeline for the N-BEATS model with tf data.mp4 (124.14 MB)
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044 Model 7 Setting up hyperparameters for the N-BEATS algorithm.mp4 (101.81 MB)
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045 Model 7 Getting ready for residual connections.mp4 (150.37 MB)
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046 Model 7 Outlining the steps we're going to take to build the N-BEATS model.mp4 (107.91 MB)
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047 Model 7 Putting together the pieces of the puzzle of the N-BEATS model.mp4 (242.27 MB)
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048 Model 7 Plotting the N-BEATS algorithm we've created and admiring its beauty.mp4 (47 MB)
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049 Model 8 Ensemble model overview.mp4 (38.03 MB)
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050 Model 8 Building, compiling and fitting an ensemble of models.mp4 (182.63 MB)
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051 Model 8 Making and evaluating predictions with our ensemble model.mp4 (185.17 MB)
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052 Discussing the importance of prediction intervals in forecasting.mp4 (114.33 MB)
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053 Getting the upper and lower bounds of our prediction intervals.mp4 (70.48 MB)
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054 Plotting the prediction intervals of our ensemble model predictions.mp4 (117.82 MB)
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055 (Optional) Discussing the types of uncertainty in machine learning.mp4 (115.69 MB)
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056 Model 9 Preparing data to create a model capable of predicting into the future.mp4 (75.64 MB)
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057 Model 9 Building, compiling and fitting a future predictions model.mp4 (40.33 MB)
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058 Model 9 Discussing what's required for our model to make future predictions.mp4 (63.57 MB)
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059 Model 9 Creating a function to make forecasts into the future.mp4 (121.24 MB)
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060 Model 9 Plotting our model's future forecasts.mp4 (106.71 MB)
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061 Model 10 Introducing the turkey problem and making data for it.mp4 (93.47 MB)
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062 Model 10 Building a model to predict on turkey data (why forecasting is BS).mp4 (112.21 MB)
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063 Comparing the results of all of our models and discussing where to go next.mp4 (110.21 MB)
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002 What is Machine Learning.mp4 (18.65 MB)
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003 AIMachine LearningData Science.mp4 (13.63 MB)
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004 Exercise Machine Learning Playground.mp4 (37.36 MB)
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005 How Did We Get Here.mp4 (15.25 MB)
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006 Exercise YouTube Recommendation Engine.mp4 (9.18 MB)
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007 Types of Machine Learning.mp4 (9.85 MB)
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009 What Is Machine Learning Round 2.mp4 (12.24 MB)
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010 Section Review.mp4 (2.84 MB)
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002 Section Overview.mp4 (6.56 MB)
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003 Introducing Our Framework.mp4 (4.4 MB)
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004 6 Step Machine Learning Framework.mp4 (10.35 MB)
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005 Types of Machine Learning Problems.mp4 (26.57 MB)
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006 Types of Data.mp4 (20.61 MB)
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007 Types of Evaluation.mp4 (6.66 MB)
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008 Features In Data.mp4 (17.82 MB)
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009 Modelling - Splitting Data.mp4 (13.74 MB)
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010 Modelling - Picking the Model.mp4 (8.93 MB)
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011 Modelling - Tuning.mp4 (6.31 MB)
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012 Modelling - Comparison.mp4 (18.67 MB)
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014 Experimentation.mp4 (11.95 MB)
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015 Tools We Will Use.mp4 (13.24 MB)
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002 Section Overview.mp4 (5.26 MB)
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004 Pandas Introduction.mp4 (11.35 MB)
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005 Series, Data Frames and CSVs.mp4 (94.57 MB)
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007 Describing Data with Pandas.mp4 (64.95 MB)
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008 Selecting and Viewing Data with Pandas.mp4 (53.27 MB)
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009 Selecting and Viewing Data with Pandas Part 2.mp4 (106.94 MB)
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010 Manipulating Data.mp4 (105.22 MB)
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011 Manipulating Data 2.mp4 (86.91 MB)
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012 Manipulating Data 3.mp4 (78.96 MB)
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014 How To Download The Course Assignments.mp4 (67.5 MB)
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002 Section Overview.mp4 (12.8 MB)
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003 NumPy Introduction.mp4 (13.99 MB)
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005 NumPy DataTypes and Attributes.mp4 (69.06 MB)
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006 Creating NumPy Arrays.mp4 (58.27 MB)
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007 NumPy Random Seed.mp4 (37.34 MB)
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008 Viewing Arrays and Matrices.mp4 (61.13 MB)
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009 Manipulating Arrays.mp4 (70.45 MB)
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010 Manipulating Arrays 2.mp4 (66.96 MB)
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011 Standard Deviation and Variance.mp4 (45.7 MB)
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012 Reshape and Transpose.mp4 (53.5 MB)
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013 Dot Product vs Element Wise.mp4 (72.12 MB)
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014 Exercise Nut Butter Store Sales.mp4 (90.55 MB)
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015 Comparison Operators.mp4 (22.54 MB)
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016 Sorting Arrays.mp4 (25.15 MB)
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017 numpy-images.zip (7.27 MB)
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017 Turn Images Into NumPy Arrays.mp4 (88.03 MB)
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