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TensorFlow Developer Certificate in 2021 Zero to Mastery
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[TutsNode.com] - TensorFlow Developer Certificate in 2021 Zero to Mastery
10 NLP Fundamentals in TensorFlow
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
015 Model 1_ Building, fitting and evaluating our first deep model on text data.en.srt
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.en.srt
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.en.srt
006 Becoming one with the data and visualising a text dataset.en.srt
009 Setting up a TensorFlow TextVectorization layer to convert text to numbers.en.srt
002 Introduction to Natural Language Processing (NLP) and Sequence Problems.en.srt
023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP).en.srt
034 Understanding the concept of the speed_score tradeoff.en.srt
011 Creating an Embedding layer to turn tokenised text into embedding vectors.en.srt
028 Comparing all our modelling experiments evaluation metrics.en.srt
027 Fixing our data leakage issue with model 7 and retraining it.en.srt
014 Creating a function to track and evaluate our model's results.en.srt
031 Downloading a pretrained model and preparing data to investigate predictions.en.srt
010 Mapping the TextVectorization layer to text data and turning it into numbers.en.srt
029 Uploading our model's training logs to TensorBoard and comparing them.en.srt
015 Model 1_ Building, fitting and evaluating our first deep model on text data.mp4
025 Preparing subsets of data for model 7 (same as model 6 but 10% of data).en.srt
024 Model 6_ Building, training and evaluating a transfer learning model for NLP.en.srt
022 Model 5_ Building, fitting and evaluating a 1D CNN for text.en.srt
009 Setting up a TensorFlow TextVectorization layer to convert text to numbers.mp4
017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more.en.srt
012 Discussing the various modelling experiments we're going to run.en.srt
030 Saving and loading in a trained NLP model with TensorFlow.en.srt
004 The typical architecture of a Recurrent Neural Network (RNN).en.srt
008 Converting text data to numbers using tokenisation and embeddings (overview).en.srt
026 Model 7_ Building, training and evaluating a transfer learning model on 10% data.en.srt
013 Model 0_ Building a baseline model to try and improve upon.en.srt
032 Visualising our model's most wrong predictions.en.srt
005 Preparing a notebook for our first NLP with TensorFlow project.en.srt
003 Example NLP inputs and outputs.en.srt
033 Making and visualising predictions on the test dataset.en.srt
007 Splitting data into training and validation sets.en.srt
035 NLP Fundamentals in TensorFlow challenge, exercises and extra-curriculum.html
001 Welcome to natural language processing with TensorFlow!.html
021 Discussing the intuition behind Conv1D neural networks for text and sequences.mp4
019 Model 3_ Building, fitting and evaluating a GRU-cell powered RNN.mp4
020 Model 4_ Building, fitting and evaluating a bidirectional RNN model.mp4
027 Fixing our data leakage issue with model 7 and retraining it.mp4
018 Model 2_ Building, fitting and evaluating our first TensorFlow RNN model (LSTM).mp4
006 Becoming one with the data and visualising a text dataset.mp4
014 Creating a function to track and evaluate our model's results.mp4
023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP).mp4
011 Creating an Embedding layer to turn tokenised text into embedding vectors.mp4
031 Downloading a pretrained model and preparing data to investigate predictions.mp4
034 Understanding the concept of the speed_score tradeoff.mp4
002 Introduction to Natural Language Processing (NLP) and Sequence Problems.mp4
028 Comparing all our modelling experiments evaluation metrics.mp4
029 Uploading our model's training logs to TensorBoard and comparing them.mp4
004 The typical architecture of a Recurrent Neural Network (RNN).mp4
030 Saving and loading in a trained NLP model with TensorFlow.mp4
026 Model 7_ Building, training and evaluating a transfer learning model on 10% data.mp4
024 Model 6_ Building, training and evaluating a transfer learning model for NLP.mp4
010 Mapping the TextVectorization layer to text data and turning it into numbers.mp4
017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more.mp4
013 Model 0_ Building a baseline model to try and improve upon.mp4
025 Preparing subsets of data for model 7 (same as model 6 but 10% of data).mp4
012 Discussing the various modelling experiments we're going to run.mp4
005 Preparing a notebook for our first NLP with TensorFlow project.mp4
008 Converting text data to numbers using tokenisation and embeddings (overview).mp4
022 Model 5_ Building, fitting and evaluating a 1D CNN for text.mp4
032 Visualising our model's most wrong predictions.mp4
033 Making and visualising predictions on the test dataset.mp4
003 Example NLP inputs and outputs.mp4
007 Splitting data into training and validation sets.mp4
18 Appendix_ Pandas for Data Analysis
319 pandas-anatomy-of-a-dataframe.png
325 pandas-anatomy-of-a-dataframe.png
324 car-sales-missing-data.csv
009 Selecting and Viewing Data with Pandas Part 2.en.srt
010 Manipulating Data.en.srt
005 Series, Data Frames and CSVs.en.srt
008 Selecting and Viewing Data with Pandas.en.srt
011 Manipulating Data 2.en.srt
007 Describing Data with Pandas.en.srt
012 Manipulating Data 3.en.srt
014 How To Download The Course Assignments.en.srt
004 Pandas Introduction.en.srt
002 Section Overview.en.srt
013 Assignment_ Pandas Practice.html
006 Data from URLs.html
003 Downloading Workbooks and Assignments.html
001 Quick Note_ Upcoming Videos.html
external-assets-links.txt
322 car-sales.csv
009 Selecting and Viewing Data with Pandas Part 2.mp4
010 Manipulating Data.mp4
005 Series, Data Frames and CSVs.mp4
012 Manipulating Data 3.mp4
011 Manipulating Data 2.mp4
007 Describing Data with Pandas.mp4
008 Selecting and Viewing Data with Pandas.mp4
014 How To Download The Course Assignments.mp4
004 Pandas Introduction.mp4
002 Section Overview.mp4
11 Milestone Project 2_ SkimLit
017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer.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
014 Model 1_ Building, fitting and evaluating a Conv1D with token embeddings.en.srt
021 Model 4_ Building a multi-input model (hybrid token + character embeddings).en.srt
001 Introduction to Milestone Project 2_ SkimLit.en.srt
004 Setting up our notebook for Milestone Project 2 (getting the data).en.srt
011 Creating a text vectoriser to map our tokens (text) to numbers.en.srt
019 Model 3_ Building, fitting and evaluating a Conv1D model on character embeddings.en.srt
008 Turning our target labels into numbers (ML models require numbers).en.srt
024 Model 4_ Building, fitting and evaluating a hybrid embedding model.en.srt
029 Model 5_ Completing the build of a tribrid embedding model for sequences.en.srt
003 SkimLit inputs and outputs.en.srt
035 Congratulations and your challenge before heading to the next module.en.srt
005 Visualising examples from the dataset (becoming one with the data).en.srt
026 Encoding the line number feature to used with Model 5.en.srt
016 Model 2_ Building, fitting and evaluating a Conv1D model with token embeddings.en.srt
015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2.en.srt
032 Bringing SkimLit to life!!! (fitting and evaluating Model 5).en.srt
030 Visually inspecting the architecture of our tribrid embedding model.en.srt
010 Preparing our data for deep sequence models.en.srt
013 Creating fast loading dataset with the TensorFlow tf.data API.en.srt
012 Creating a custom token embedding layer with TensorFlow.en.srt
033 Comparing the performance of all of our modelling experiments.en.srt
022 Model 4_ Plotting and visually exploring different data inputs.en.srt
002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts).en.srt
009 Model 0_ Creating, fitting and evaluating a baseline model for SkimLit.en.srt
028 Model 5_ Building the foundations of a tribrid embedding model.en.srt
007 Performing visual data analysis on our preprocessed text.en.srt
023 Crafting multi-input fast loading tf.data datasets for Model 4.en.srt
031 Creating multi-level data input pipelines for Model 5 with the tf.data API.en.srt
018 Creating a character-level embedding layer with tf.keras.layers.Embedding.en.srt
027 Encoding the total lines feature to be used with Model 5.en.srt
025 Model 5_ Adding positional embeddings via feature engineering (overview).en.srt
034 Saving, loading & testing our best performing model.en.srt
020 Discussing how we're going to build Model 4 (character + token embeddings).en.srt
017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer.mp4
036 Milestone Project 2 (SkimLit) challenge, exercises and extra-curriculum.html
021 Model 4_ Building a multi-input model (hybrid token + character embeddings).mp4
014 Model 1_ Building, fitting and evaluating a Conv1D with token embeddings.mp4
029 Model 5_ Completing the build of a tribrid embedding model for sequences.mp4
001 Introduction to Milestone Project 2_ SkimLit.mp4
004 Setting up our notebook for Milestone Project 2 (getting the data).mp4
024 Model 4_ Building, fitting and evaluating a hybrid embedding model.mp4
035 Congratulations and your challenge before heading to the next module.mp4
005 Visualising examples from the dataset (becoming one with the data).mp4
019 Model 3_ Building, fitting and evaluating a Conv1D model on character embeddings.mp4
011 Creating a text vectoriser to map our tokens (text) to numbers.mp4
015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2.mp4
008 Turning our target labels into numbers (ML models require numbers).mp4
032 Bringing SkimLit to life!!! (fitting and evaluating Model 5).mp4
026 Encoding the line number feature to used with Model 5.mp4
030 Visually inspecting the architecture of our tribrid embedding model.mp4
016 Model 2_ Building, fitting and evaluating a Conv1D model with token embeddings.mp4
012 Creating a custom token embedding layer with TensorFlow.mp4
031 Creating multi-level data input pipelines for Model 5 with the tf.data API.mp4
013 Creating fast loading dataset with the TensorFlow tf.data API.mp4
022 Model 4_ Plotting and visually exploring different data inputs.mp4
010 Preparing our data for deep sequence models.mp4
023 Crafting multi-input fast loading tf.data datasets for Model 4.mp4
034 Saving, loading & testing our best performing model.mp4
028 Model 5_ Building the foundations of a tribrid embedding model.mp4
009 Model 0_ Creating, fitting and evaluating a baseline model for SkimLit.mp4
033 Comparing the performance of all of our modelling experiments.mp4
018 Creating a character-level embedding layer with tf.keras.layers.Embedding.mp4
003 SkimLit inputs and outputs.mp4
007 Performing visual data analysis on our preprocessed text.mp4
002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts).mp4
025 Model 5_ Adding positional embeddings via feature engineering (overview).mp4
027 Encoding the total lines feature to be used with Model 5.mp4
020 Discussing how we're going to build Model 4 (character + token embeddings).mp4
01 Introduction
004 All Course Resources + Notebooks.html
external-assets-links.txt
001 Course Outline.en.srt
003 Exercise_ Meet The Community.html
002 Join Our Online Classroom!.html
001 Course Outline.mp4
05 Computer Vision and Convolutional Neural Networks in TensorFlow
020 Breaking our CNN model down part 10_ Visualizing our augmented data.en.srt
007 Building an end to end CNN Model.en.srt
015 Breaking our CNN model down part 5_ Looking inside a Conv2D layer.en.srt
027 Multi-class CNN's part 1_ Becoming one with the data.en.srt
018 Breaking our CNN model down part 8_ Reducing overfitting with Max Pooling.en.srt
017 Breaking our CNN model down part 7_ Evaluating our CNN's training curves.en.srt
012 Breaking our CNN model down part 2_ Preparing to load our data.en.srt
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.en.srt
005 Becoming One With Data Part 2.en.srt
026 Making a prediction on a custom image with our trained CNN.en.srt
001 Introduction to Computer Vision with TensorFlow.en.srt
022 Breaking our CNN model down part 12_ Discovering the power of shuffling data.en.srt
010 Improving our non-CNN model by adding more layers.en.srt
025 Writing a helper function to load and preprocessing custom images.en.srt
021 Breaking our CNN model down part 11_ Training a CNN model on augmented data.en.srt
013 Breaking our CNN model down part 3_ Loading our data with ImageDataGenerator.en.srt
008 Using a GPU to run our CNN model 5x faster.en.srt
011 Breaking our CNN model down part 1_ Becoming one with the data.en.srt
002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.en.srt
035 Multi-class CNN's part 9_ Making predictions with our model on custom images.en.srt
009 Trying a non-CNN model on our image data.en.srt
014 Breaking our CNN model down part 4_ Building a baseline CNN model.en.srt
029 Multi-class CNN's part 3_ Building a multi-class CNN model.en.srt
003 Downloading an image dataset for our first Food Vision model.en.srt
028 Multi-class CNN's part 2_ Preparing our data (turning it into tensors).en.srt
016 Breaking our CNN model down part 6_ Compiling and fitting our baseline CNN.en.srt
019 Breaking our CNN model down part 9_ Reducing overfitting with data augmentation.en.srt
036 Saving and loading our trained CNN model.en.srt
030 Multi-class CNN's part 4_ Fitting a multi-class CNN model to the data.en.srt
external-assets-links.txt
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.en.srt
031 Multi-class CNN's part 5_ Evaluating our multi-class CNN model.en.srt
004 Becoming One With Data.en.srt
006 Becoming One With Data Part 3.en.srt
034 Multi-class CNN's part 8_ Things you could do to improve your CNN model.en.srt
037 TensorFlow computer vision and CNNs challenge, exercises & extra-curriculum.html
015 Breaking our CNN model down part 5_ Looking inside a Conv2D layer.mp4
020 Breaking our CNN model down part 10_ Visualizing our augmented data.mp4
007 Building an end to end CNN Model.mp4
027 Multi-class CNN's part 1_ Becoming one with the data.mp4
018 Breaking our CNN model down part 8_ Reducing overfitting with Max Pooling.mp4
032 Multi-class CNN's part 6_ Trying to fix overfitting by removing layers.mp4
033 Multi-class CNN's part 7_ Trying to fix overfitting with data augmentation.mp4
035 Multi-class CNN's part 9_ Making predictions with our model on custom images.mp4
008 Using a GPU to run our CNN model 5x faster.mp4
012 Breaking our CNN model down part 2_ Preparing to load our data.mp4
010 Improving our non-CNN model by adding more layers.mp4
017 Breaking our CNN model down part 7_ Evaluating our CNN's training curves.mp4
025 Writing a helper function to load and preprocessing custom images.mp4
005 Becoming One With Data Part 2.mp4
022 Breaking our CNN model down part 12_ Discovering the power of shuffling data.mp4
013 Breaking our CNN model down part 3_ Loading our data with ImageDataGenerator.mp4
009 Trying a non-CNN model on our image data.mp4
026 Making a prediction on a custom image with our trained CNN.mp4
021 Breaking our CNN model down part 11_ Training a CNN model on augmented data.mp4
011 Breaking our CNN model down part 1_ Becoming one with the data.mp4
029 Multi-class CNN's part 3_ Building a multi-class CNN model.mp4
014 Breaking our CNN model down part 4_ Building a baseline CNN model.mp4
016 Breaking our CNN model down part 6_ Compiling and fitting our baseline CNN.mp4
002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.mp4
001 Introduction to Computer Vision with TensorFlow.mp4
003 Downloading an image dataset for our first Food Vision model.mp4
028 Multi-class CNN's part 2_ Preparing our data (turning it into tensors).mp4
036 Saving and loading our trained CNN model.mp4
019 Breaking our CNN model down part 9_ Reducing overfitting with data augmentation.mp4
030 Multi-class CNN's part 4_ Fitting a multi-class CNN model to the data.mp4
024 Downloading a custom image to make predictions on.mp4
023 Breaking our CNN model down part 13_ Exploring options to improve our model.mp4
004 Becoming One With Data.mp4
034 Multi-class CNN's part 8_ Things you could do to improve your CNN model.mp4
031 Multi-class CNN's part 5_ Evaluating our multi-class CNN model.mp4
006 Becoming One With Data Part 3.mp4
07 Transfer Learning in TensorFlow Part 2_ Fine tuning
018 Preparing Model 3 (our first fine-tuned model).en.srt
014 Building Model 2 (with a data augmentation layer and 10% of training data).en.srt
013 Building Model 1 (with a data augmentation layer and 1% of training data).en.srt
003 Downloading and turning our images into a TensorFlow BatchDataset.en.srt
008 Getting a feature vector from our trained model.en.srt
011 Building a data augmentation layer to use inside our model.en.srt
006 Creating our first model with the TensorFlow Keras Functional API.en.srt
007 Compiling and fitting our first Functional API model.en.srt
024 Comparing our modelling experiment results in TensorBoard.en.srt
023 Fine-tuning Model 4 on 100% of the training data and evaluating its results.en.srt
022 Preparing our final modelling experiment (Model 4).en.srt
012 Visualising what happens when images pass through our data augmentation layer.en.srt
018 Preparing Model 3 (our first fine-tuned model).mp4
020 Comparing our model's results before and after fine-tuning.en.srt
010 Downloading and preparing the data for Model 1 (1 percent of training data).en.srt
015 Creating a ModelCheckpoint to save our model's weights during training.en.srt
019 Fitting and evaluating Model 3 (our first fine-tuned model).en.srt
016 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).en.srt
002 Importing a script full of helper functions (and saving lots of space).en.srt
001 Introduction to Transfer Learning in TensorFlow Part 2_ Fine-tuning.en.srt
017 Loading and comparing saved weights to our existing trained Model 2.en.srt
021 Downloading and preparing data for our biggest experiment yet (Model 4).en.srt
009 Drilling into the concept of a feature vector (a learned representation).en.srt
005 Comparing the TensorFlow Keras Sequential API versus the Functional API.en.srt
004 Discussing the four (actually five) modelling experiments we're running.en.srt
026 Transfer Learning in TensorFlow Part 2 challenge, exercises and extra-curriculum.html
025 How to view and delete previous TensorBoard experiments.en.srt
003 Downloading and turning our images into a TensorFlow BatchDataset.mp4
014 Building Model 2 (with a data augmentation layer and 10% of training data).mp4
013 Building Model 1 (with a data augmentation layer and 1% of training data).mp4
008 Getting a feature vector from our trained model.mp4
007 Compiling and fitting our first Functional API model.mp4
006 Creating our first model with the TensorFlow Keras Functional API.mp4
012 Visualising what happens when images pass through our data augmentation layer.mp4
011 Building a data augmentation layer to use inside our model.mp4
010 Downloading and preparing the data for Model 1 (1 percent of training data).mp4
023 Fine-tuning Model 4 on 100% of the training data and evaluating its results.mp4
022 Preparing our final modelling experiment (Model 4).mp4
024 Comparing our modelling experiment results in TensorBoard.mp4
002 Importing a script full of helper functions (and saving lots of space).mp4
020 Comparing our model's results before and after fine-tuning.mp4
019 Fitting and evaluating Model 3 (our first fine-tuned model).mp4
015 Creating a ModelCheckpoint to save our model's weights during training.mp4
016 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp4
017 Loading and comparing saved weights to our existing trained Model 2.mp4
001 Introduction to Transfer Learning in TensorFlow Part 2_ Fine-tuning.mp4
021 Downloading and preparing data for our biggest experiment yet (Model 4).mp4
009 Drilling into the concept of a feature vector (a learned representation).mp4
005 Comparing the TensorFlow Keras Sequential API versus the Functional API.mp4
025 How to view and delete previous TensorBoard experiments.mp4
004 Discussing the four (actually five) modelling experiments we're running.mp4
03 Neural network regression with TensorFlow
005 The major steps in modelling with TensorFlow.en.srt
011 Evaluating a TensorFlow model part 3 (getting a model summary).en.srt
025 Putting together what we've learned part 3 (improving our regression model).en.srt
023 Putting together what we've learned part 1 (preparing a dataset).en.srt
024 Putting together what we've learned part 2 (building a regression model).en.srt
017 Setting up TensorFlow modelling experiments part 1 (start with a simple model).en.srt
008 Steps in improving a model with TensorFlow part 3.en.srt
004 Creating sample regression data (so we can model it).en.srt
018 Setting up TensorFlow modelling experiments part 2 (increasing complexity).en.srt
002 Inputs and outputs of a neural network regression model.en.srt
029 TensorFlow Regression challenge, exercises & extra-curriculum.html
external-assets-links.txt
010 Evaluating a TensorFlow model part 2 (the three datasets).en.srt
027 Preprocessing data with feature scaling part 2 (normalising our data).en.srt
026 Preprocessing data with feature scaling part 1 (what is feature scaling_).en.srt
019 Comparing and tracking your TensorFlow modelling experiments.en.srt
007 Steps in improving a model with TensorFlow part 2.en.srt
021 How to load and use a saved TensorFlow model.en.srt
003 Anatomy and architecture of a neural network regression model.en.srt
013 Evaluating a TensorFlow model part 5 (visualising a model's predictions).en.srt
020 How to save a TensorFlow model.en.srt
001 Introduction to Neural Network Regression with TensorFlow.en.srt
014 Evaluating a TensorFlow model part 6 (common regression evaluation metrics).en.srt
028 Preprocessing data with feature scaling part 3 (fitting a model on scaled data).en.srt
009 Evaluating a TensorFlow model part 1 (_visualise, visualise, visualise_).en.srt
012 Evaluating a TensorFlow model part 4 (visualising a model's layers).en.srt
015 Evaluating a TensorFlow regression model part 7 (mean absolute error).en.srt
022 (Optional) How to save and download files from Google Colab.en.srt
006 Steps in improving a model with TensorFlow part 1.en.srt
016 Evaluating a TensorFlow regression model part 7 (mean square error).en.srt
011 Evaluating a TensorFlow model part 3 (getting a model summary).mp4
005 The major steps in modelling with TensorFlow.mp4
025 Putting together what we've learned part 3 (improving our regression model).mp4
023 Putting together what we've learned part 1 (preparing a dataset).mp4
008 Steps in improving a model with TensorFlow part 3.mp4
017 Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4
024 Putting together what we've learned part 2 (building a regression model).mp4
021 How to load and use a saved TensorFlow model.mp4
027 Preprocessing data with feature scaling part 2 (normalising our data).mp4
018 Setting up TensorFlow modelling experiments part 2 (increasing complexity).mp4
026 Preprocessing data with feature scaling part 1 (what is feature scaling_).mp4
020 How to save a TensorFlow model.mp4
019 Comparing and tracking your TensorFlow modelling experiments.mp4
007 Steps in improving a model with TensorFlow part 2.mp4
004 Creating sample regression data (so we can model it).mp4
010 Evaluating a TensorFlow model part 2 (the three datasets).mp4
013 Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp4
028 Preprocessing data with feature scaling part 3 (fitting a model on scaled data).mp4
014 Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp4
012 Evaluating a TensorFlow model part 4 (visualising a model's layers).mp4
022 (Optional) How to save and download files from Google Colab.mp4
009 Evaluating a TensorFlow model part 1 (_visualise, visualise, visualise_).mp4
001 Introduction to Neural Network Regression with TensorFlow.mp4
003 Anatomy and architecture of a neural network regression model.mp4
002 Inputs and outputs of a neural network regression model.mp4
015 Evaluating a TensorFlow regression model part 7 (mean absolute error).mp4
006 Steps in improving a model with TensorFlow part 1.mp4
016 Evaluating a TensorFlow regression model part 7 (mean square error).mp4
04 Neural network classification in TensorFlow
018 Using callbacks to find a model's ideal learning rate.en.srt
026 Multi-class classification part 3_ Building a multi-class classification model.en.srt
033 What _patterns_ is our model learning_.en.srt
016 Getting great results in less time by tweaking the learning rate.en.srt
009 Creating a function to view our model's not so good predictions.en.srt
015 Non-linearity part 5_ Replicating non-linear activation functions from scratch.en.srt
023 Making our confusion matrix prettier.en.srt
030 Multi-class classification part 7_ Evaluating our model.en.srt
010 Make our poor classification model work for a regression dataset.en.srt
027 Multi-class classification part 4_ Improving performance with normalisation.en.srt
007 Building a not very good classification model with TensorFlow.en.srt
029 Multi-class classification part 6_ Finding the ideal learning rate.en.srt
004 Typical architecture of neural network classification models with TensorFlow.en.srt
005 Creating and viewing classification data to model.en.srt
013 Non-linearity part 3_ Upgrading our non-linear model with more layers.en.srt
011 Non-linearity part 1_ Straight lines and non-straight lines.en.srt
024 Putting things together with multi-class classification part 1_ Getting the data.en.srt
032 Multi-class classification part 9_ Visualising random model predictions.en.srt
001 Introduction to neural network classification in TensorFlow.en.srt
external-assets-links.txt
008 Trying to improve our not very good classification model.en.srt
014 Non-linearity part 4_ Modelling our non-linear data once and for all.en.srt
019 Training and evaluating a model with an ideal learning rate.en.srt
022 Creating our first confusion matrix (to see where our model is getting confused).en.srt
025 Multi-class classification part 2_ Becoming one with the data.en.srt
002 Example classification problems (and their inputs and outputs).en.srt
020 Introducing more classification evaluation methods.en.srt
003 Input and output tensors of classification problems.en.srt
017 Using the TensorFlow History object to plot a model's loss curves.en.srt
012 Non-linearity part 2_ Building our first neural network with non-linearity.en.srt
031 Multi-class classification part 8_ Creating a confusion matrix.en.srt
006 Checking the input and output shapes of our classification data.en.srt
021 Finding the accuracy of our classification model.en.srt
034 TensorFlow classification challenge, exercises & extra-curriculum.html
028 Multi-class classification part 5_ Comparing normalised and non-normalised data.en.srt
009 Creating a function to view our model's not so good predictions.mp4
018 Using callbacks to find a model's ideal learning rate.mp4
015 Non-linearity part 5_ Replicating non-linear activation functions from scratch.mp4
026 Multi-class classification part 3_ Building a multi-class classification model.mp4
016 Getting great results in less time by tweaking the learning rate.mp4
033 What _patterns_ is our model learning_.mp4
007 Building a not very good classification model with TensorFlow.mp4
013 Non-linearity part 3_ Upgrading our non-linear model with more layers.mp4
010 Make our poor classification model work for a regression dataset.mp4
030 Multi-class classification part 7_ Evaluating our model.mp4
023 Making our confusion matrix prettier.mp4
027 Multi-class classification part 4_ Improving performance with normalisation.mp4
004 Typical architecture of neural network classification models with TensorFlow.mp4
005 Creating and viewing classification data to model.mp4
014 Non-linearity part 4_ Modelling our non-linear data once and for all.mp4
011 Non-linearity part 1_ Straight lines and non-straight lines.mp4
019 Training and evaluating a model with an ideal learning rate.mp4
024 Putting things together with multi-class classification part 1_ Getting the data.mp4
008 Trying to improve our not very good classification model.mp4
029 Multi-class classification part 6_ Finding the ideal learning rate.mp4
001 Introduction to neural network classification in TensorFlow.mp4
022 Creating our first confusion matrix (to see where our model is getting confused).mp4
032 Multi-class classification part 9_ Visualising random model predictions.mp4
017 Using the TensorFlow History object to plot a model's loss curves.mp4
012 Non-linearity part 2_ Building our first neural network with non-linearity.mp4
003 Input and output tensors of classification problems.mp4
002 Example classification problems (and their inputs and outputs).mp4
025 Multi-class classification part 2_ Becoming one with the data.mp4
020 Introducing more classification evaluation methods.mp4
031 Multi-class classification part 8_ Creating a confusion matrix.mp4
006 Checking the input and output shapes of our classification data.mp4
021 Finding the accuracy of our classification model.mp4
028 Multi-class classification part 5_ Comparing normalised and non-normalised data.mp4
02 Deep Learning and TensorFlow Fundamentals
010 Creating your first tensors with TensorFlow and tf.constant().en.srt
009 Need A Refresher_.html
030 TensorFlow Fundamentals challenge, exercises & extra-curriculum.html
031 Python + Machine Learning Monthly.html
032 LinkedIn Endorsements.html
external-assets-links.txt
019 Matrix multiplication with tensors part 2.en.srt
015 Getting information from your tensors (tensor attributes).en.srt
016 Indexing and expanding tensors.en.srt
018 Matrix multiplication with tensors part 1.en.srt
014 Creating tensors from NumPy arrays.en.srt
003 What are neural networks_.en.srt
029 Making sure our tensor operations run really fast on GPUs.en.srt
020 Matrix multiplication with tensors part 3.en.srt
002 Why use deep learning_.en.srt
004 What is deep learning already being used for_.en.srt
012 Creating random tensors with TensorFlow.en.srt
022 Tensor aggregation (finding the min, max, mean & more).en.srt
013 Shuffling the order of tensors.en.srt
024 Finding the positional minimum and maximum of a tensor (argmin and argmax).en.srt
005 What is and why use TensorFlow_.en.srt
011 Creating tensors with TensorFlow and tf.Variable().en.srt
021 Changing the datatype of tensors.en.srt
008 How to approach this course.en.srt
026 One-hot encoding tensors.en.srt
007 What we're going to cover throughout the course.en.srt
028 Exploring TensorFlow and NumPy's compatibility.en.srt
017 Manipulating tensors with basic operations.en.srt
001 What is deep learning_.en.srt
023 Tensor troubleshooting example (updating tensor datatypes).en.srt
027 Trying out more tensor math operations.en.srt
006 What is a Tensor_.en.srt
025 Squeezing a tensor (removing all 1-dimension axes).en.srt
010 Creating your first tensors with TensorFlow and tf.constant().mp4
029 Making sure our tensor operations run really fast on GPUs.mp4
019 Matrix multiplication with tensors part 2.mp4
014 Creating tensors from NumPy arrays.mp4
018 Matrix multiplication with tensors part 1.mp4
024 Finding the positional minimum and maximum of a tensor (argmin and argmax).mp4
013 Shuffling the order of tensors.mp4
022 Tensor aggregation (finding the min, max, mean & more).mp4
012 Creating random tensors with TensorFlow.mp4
015 Getting information from your tensors (tensor attributes).mp4
016 Indexing and expanding tensors.mp4
020 Matrix multiplication with tensors part 3.mp4
004 What is deep learning already being used for_.mp4
021 Changing the datatype of tensors.mp4
011 Creating tensors with TensorFlow and tf.Variable().mp4
023 Tensor troubleshooting example (updating tensor datatypes).mp4
005 What is and why use TensorFlow_.mp4
003 What are neural networks_.mp4
002 Why use deep learning_.mp4
026 One-hot encoding tensors.mp4
027 Trying out more tensor math operations.mp4
017 Manipulating tensors with basic operations.mp4
028 Exploring TensorFlow and NumPy's compatibility.mp4
001 What is deep learning_.mp4
025 Squeezing a tensor (removing all 1-dimension axes).mp4
007 What we're going to cover throughout the course.mp4
006 What is a Tensor_.mp4
008 How to approach this course.mp4
08 Transfer Learning with TensorFlow Part 3_ Scaling Up
018 Making predictions on our test images and evaluating them.en.srt
015 Evaluating every individual class in our dataset.en.srt
002 Getting helper functions ready and downloading data to model.en.srt
014 Creating a confusion matrix for our model's 101 different classes.en.srt
020 Writing code to uncover our model's most wrong predictions.en.srt
007 Unfreezing some layers in our base model to prepare for fine-tuning.en.srt
011 Making predictions with our trained model on 25,250 test samples.en.srt
017 Creating a function to load and prepare images for making predictions.en.srt
021 Plotting and visualising the samples our model got most wrong.en.srt
022 Making predictions on and plotting our own custom images.en.srt
019 Discussing the benefits of finding your model's most wrong predictions.en.srt
005 Creating a headless EfficientNetB0 model with data augmentation built in.en.srt
008 Fine-tuning our feature extraction model and evaluating its performance.en.srt
006 Fitting and evaluating our biggest transfer learning model yet.en.srt
016 Plotting our model's F1-scores for each separate class.en.srt
001 Introduction to Transfer Learning Part 3_ Scaling Up.en.srt
009 Saving and loading our trained model.en.srt
010 Downloading a pretrained model to make and evaluate predictions with.en.srt
012 Unravelling our test dataset for comparing ground truth labels to predictions.en.srt
003 Outlining the model we're going to build and building a ModelCheckpoint callback.en.srt
013 Confirming our model's predictions are in the same order as the test labels.en.srt
004 Creating a data augmentation layer to use with our model.en.srt
023 Transfer Learning in TensorFlow Part 3 challenge, exercises and extra-curriculum.html
018 Making predictions on our test images and evaluating them.mp4
014 Creating a confusion matrix for our model's 101 different classes.mp4
015 Evaluating every individual class in our dataset.mp4
002 Getting helper functions ready and downloading data to model.mp4
021 Plotting and visualising the samples our model got most wrong.mp4
011 Making predictions with our trained model on 25,250 test samples.mp4
020 Writing code to uncover our model's most wrong predictions.mp4
017 Creating a function to load and prepare images for making predictions.mp4
022 Making predictions on and plotting our own custom images.mp4
007 Unfreezing some layers in our base model to prepare for fine-tuning.mp4
005 Creating a headless EfficientNetB0 model with data augmentation built in.mp4
010 Downloading a pretrained model to make and evaluate predictions with.mp4
016 Plotting our model's F1-scores for each separate class.mp4
006 Fitting and evaluating our biggest transfer learning model yet.mp4
008 Fine-tuning our feature extraction model and evaluating its performance.mp4
019 Discussing the benefits of finding your model's most wrong predictions.mp4
009 Saving and loading our trained model.mp4
013 Confirming our model's predictions are in the same order as the test labels.mp4
012 Unravelling our test dataset for comparing ground truth labels to predictions.mp4
001 Introduction to Transfer Learning Part 3_ Scaling Up.mp4
003 Outlining the model we're going to build and building a ModelCheckpoint callback.mp4
004 Creating a data augmentation layer to use with our model.mp4
09 Milestone Project 1_ Food Vision Bigâ„¢ __
005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets).en.srt
007 Batching and preparing our datasets (to make them run fast).en.srt
006 Creating a preprocessing function to prepare our data for modelling.en.srt
004 Introduction to TensorFlow Datasets (TFDS).en.srt
011 Creating a feature extraction model capable of using mixed precision training.en.srt
013 Training and evaluating a feature extraction model (Food Vision Bigâ„¢).en.srt
002 Making sure we have access to the right GPU for mixed precision training.en.srt
010 Turning on mixed precision training with TensorFlow.en.srt
014 Introducing your Milestone Project 1 challenge_ build a model to beat DeepFood.en.srt
012 Checking to see if our model is using mixed precision training layer by layer.en.srt
009 Creating modelling callbacks for our feature extraction model.en.srt
008 Exploring what happens when we batch and prefetch our data.en.srt
001 Introduction to Milestone Project 1_ Food Vision Bigâ„¢.en.srt
003 Getting helper functions ready.en.srt
015 Milestone Project 1_ Food Vision Bigâ„¢, exercises and extra-curriculum.html
007 Batching and preparing our datasets (to make them run fast).mp4
006 Creating a preprocessing function to prepare our data for modelling.mp4
004 Introduction to TensorFlow Datasets (TFDS).mp4
005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4
011 Creating a feature extraction model capable of using mixed precision training.mp4
010 Turning on mixed precision training with TensorFlow.mp4
014 Introducing your Milestone Project 1 challenge_ build a model to beat DeepFood.mp4
013 Training and evaluating a feature extraction model (Food Vision Bigâ„¢).mp4
002 Making sure we have access to the right GPU for mixed precision training.mp4
012 Checking to see if our model is using mixed precision training layer by layer.mp4
008 Exploring what happens when we batch and prefetch our data.mp4
009 Creating modelling callbacks for our feature extraction model.mp4
001 Introduction to Milestone Project 1_ Food Vision Bigâ„¢.mp4
003 Getting helper functions ready.mp4
06 Transfer Learning in TensorFlow Part 1_ Feature extraction
010 Comparing Our Model's Results.en.srt
005 Building and compiling a TensorFlow Hub feature extraction model.en.srt
002 Downloading and preparing data for our first transfer learning model.en.srt
001 What is and why use transfer learning_.en.srt
009 Different Types of Transfer Learning.en.srt
004 Exploring the TensorFlow Hub website for pretrained models.en.srt
008 Building and training a pre-trained EfficientNet model on our data.en.srt
003 Introducing Callbacks in TensorFlow and making a callback to track our models.en.srt
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.en.srt
external-assets-links.txt
011 TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum.html
010 Comparing Our Model's Results.mp4
005 Building and compiling a TensorFlow Hub feature extraction model.mp4
002 Downloading and preparing data for our first transfer learning model.mp4
009 Different Types of Transfer Learning.mp4
008 Building and training a pre-trained EfficientNet model on our data.mp4
004 Exploring the TensorFlow Hub website for pretrained models.mp4
006 Blowing our previous models out of the water with transfer learning.mp4
003 Introducing Callbacks in TensorFlow and making a callback to track our models.mp4
001 What is and why use transfer learning_.mp4
007 Plotting the loss curves of our ResNet feature extraction model.mp4
19 Appendix_ NumPy
005 NumPy DataTypes and Attributes.en.srt
014 Exercise_ Nut Butter Store Sales.en.srt
009 Manipulating Arrays.en.srt
013 Dot Product vs Element Wise.en.srt
008 Viewing Arrays and Matrices.en.srt
006 Creating NumPy Arrays.en.srt
010 Manipulating Arrays 2.en.srt
017 Turn Images Into NumPy Arrays.en.srt
007 NumPy Random Seed.en.srt
011 Standard Deviation and Variance.en.srt
012 Reshape and Transpose.en.srt
016 Sorting Arrays.en.srt
003 NumPy Introduction.en.srt
015 Comparison Operators.en.srt
002 Section Overview.en.srt
018 Assignment_ NumPy Practice.html
004 Quick Note_ Correction In Next Video.html
019 Optional_ Extra NumPy resources.html
001 Quick Note_ Upcoming Videos.html
external-assets-links.txt
014 Exercise_ Nut Butter Store Sales.mp4
017 Turn Images Into NumPy Arrays.mp4
013 Dot Product vs Element Wise.mp4
009 Manipulating Arrays.mp4
005 NumPy DataTypes and Attributes.mp4
008 Viewing Arrays and Matrices.mp4
010 Manipulating Arrays 2.mp4
006 Creating NumPy Arrays.mp4
012 Reshape and Transpose.mp4
007 NumPy Random Seed.mp4
011 Standard Deviation and Variance.mp4
016 Sorting Arrays.mp4
003 NumPy Introduction.mp4
015 Comparison Operators.mp4
002 Section Overview.mp4
345 numpy-images.zip
17 Appendix_ Machine Learning and Data Science Framework
external-assets-links.txt
005 Types of Machine Learning Problems.en.srt
012 Modelling - Comparison.en.srt
009 Modelling - Splitting Data.en.srt
008 Features In Data.en.srt
004 6 Step Machine Learning Framework.en.srt
006 Types of Data.en.srt
010 Modelling - Picking the Model.en.srt
015 Tools We Will Use.en.srt
014 Experimentation.en.srt
011 Modelling - Tuning.en.srt
002 Section Overview.en.srt
007 Types of Evaluation.en.srt
003 Introducing Our Framework.en.srt
013 Overfitting and Underfitting Definitions.html
016 Optional_ Elements of AI.html
001 Quick Note_ Upcoming Videos.html
005 Types of Machine Learning Problems.mp4
012 Modelling - Comparison.mp4
008 Features In Data.mp4
006 Types of Data.mp4
009 Modelling - Splitting Data.mp4
015 Tools We Will Use.mp4
004 6 Step Machine Learning Framework.mp4
010 Modelling - Picking the Model.mp4
014 Experimentation.mp4
007 Types of Evaluation.mp4
011 Modelling - Tuning.mp4
002 Section Overview.mp4
003 Introducing Our Framework.mp4
16 Appendix_ Machine Learning Primer
002 What is Machine Learning_.en.srt
004 Exercise_ Machine Learning Playground.en.srt
005 How Did We Get Here_.en.srt
003 AI_Machine Learning_Data Science.en.srt
009 What Is Machine Learning_ Round 2.en.srt
006 Exercise_ YouTube Recommendation Engine.en.srt
007 Types of Machine Learning.en.srt
010 Section Review.en.srt
001 Quick Note_ Upcoming Videos.html
008 Are You Getting It Yet_.html
external-assets-links.txt
004 Exercise_ Machine Learning Playground.mp4
005 How Did We Get Here_.mp4
002 What is Machine Learning_.mp4
009 What Is Machine Learning_ Round 2.mp4
007 Types of Machine Learning.mp4
003 AI_Machine Learning_Data Science.mp4
006 Exercise_ YouTube Recommendation Engine.mp4
010 Section Review.mp4
20 BONUS SECTION
001 Special Bonus Lecture.html
15 Where To Go From Here_
002 LinkedIn Endorsements.html
001 Become An Alumni.html
003 TensorFlow Certificate.html
12 Time Series fundamentals in TensorFlow
001 More Videos Coming Soon!.html
13 Milestone Project 3_ BitPredict
001 More Videos Coming Soon!.html
14 Passing the TensorFlow Developer Certificate Exam
001 More Videos Coming Soon!.html
TutsNode.com.txt
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