016 Visualising our model's learned word embeddings with TensorFlow's projector tool.mp4 - 306 MB
015 Model 1 Building, fitting and evaluating our first deep model on text data.mp4 - 221 MB
009 Setting up a TensorFlow TextVectorization layer to convert text to numbers.mp4 - 213 MB
019 Model 3 Building, fitting and evaluating a GRU-cell powered RNN.mp4 - 179 MB
027 Fixing our data leakage issue with model 7 and retraining it.mp4 - 178 MB
020 Model 4 Building, fitting and evaluating a bidirectional RNN model.mp4 - 177 MB
018 Model 2 Building, fitting and evaluating our first TensorFlow RNN model (LSTM).mp4 - 176 MB
006 Becoming one with the data and visualising a text dataset.mp4 - 171 MB
014 Creating a function to track and evaluate our model's results.mp4 - 159 MB
011 Creating an Embedding layer to turn tokenised text into embedding vectors.mp4 - 144 MB
031 Downloading a pretrained model and preparing data to investigate predictions.mp4 - 138 MB
002 Introduction to Natural Language Processing (NLP) and Sequence Problems.mp4 - 131 MB
021 Discussing the intuition behind Conv1D neural networks for text and sequences.mp4 - 127 MB
028 Comparing all our modelling experiments evaluation metrics.mp4 - 123 MB
034 Understanding the concept of the speedscore tradeoff.mp4 - 118 MB
029 Uploading our model's training logs to TensorBoard and comparing them.mp4 - 117 MB
004 The typical architecture of a Recurrent Neural Network (RNN).mp4 - 114 MB
030 Saving and loading in a trained NLP model with TensorFlow.mp4 - 111 MB
026 Model 7 Building, training and evaluating a transfer learning model on 10% data.mp4 - 107 MB
024 Model 6 Building, training and evaluating a transfer learning model for NLP.mp4 - 105 MB
010 Mapping the TextVectorization layer to text data and turning it into numbers.mp4 - 104 MB
017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more.mp4 - 102 MB
013 Model 0 Building a baseline model to try and improve upon.mp4 - 99.7 MB
025 Preparing subsets of data for model 7 (same as model 6 but 10% of data).mp4 - 95.9 MB
012 Discussing the various modelling experiments we're going to run.mp4 - 92.5 MB
005 Preparing a notebook for our first NLP with TensorFlow project.mp4 - 87.2 MB
008 Converting text data to numbers using tokenisation and embeddings (overview).mp4 - 85.9 MB
032 Visualising our model's most wrong predictions.mp4 - 81 MB
033 Making and visualising predictions on the test dataset.mp4 - 80.6 MB
007 Splitting data into training and validation sets.mp4 - 63.6 MB
023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP).mp4 - 59.9 MB
022 Model 5 Building, fitting and evaluating a 1D CNN for text.mp4 - 56.8 MB
003 Example NLP inputs and outputs.mp4 - 29.1 MB
016 Visualising our model's learned word embeddings with TensorFlow's projector tool_en.srt - 30.4 kB
015 Model 1 Building, fitting and evaluating our first deep model on text data_en.srt - 29.3 kB
020 Model 4 Building, fitting and evaluating a bidirectional RNN model_en.srt - 27.8 kB
021 Discussing the intuition behind Conv1D neural networks for text and sequences_en.srt - 27.6 kB
018 Model 2 Building, fitting and evaluating our first TensorFlow RNN model (LSTM)_en.srt - 25.2 kB
019 Model 3 Building, fitting and evaluating a GRU-cell powered RNN_en.srt - 24.4 kB
006 Becoming one with the data and visualising a text dataset_en.srt - 22.7 kB
009 Setting up a TensorFlow TextVectorization layer to convert text to numbers_en.srt - 22.7 kB
002 Introduction to Natural Language Processing (NLP) and Sequence Problems_en.srt - 20.7 kB
023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP)_en.srt - 19.9 kB
034 Understanding the concept of the speedscore tradeoff_en.srt - 19.1 kB
011 Creating an Embedding layer to turn tokenised text into embedding vectors_en.srt - 18.3 kB
028 Comparing all our modelling experiments evaluation metrics_en.srt - 18.3 kB
027 Fixing our data leakage issue with model 7 and retraining it_en.srt - 17.7 kB
014 Creating a function to track and evaluate our model's results_en.srt - 17.1 kB
031 Downloading a pretrained model and preparing data to investigate predictions_en.srt - 16.9 kB
010 Mapping the TextVectorization layer to text data and turning it into numbers_en.srt - 16.3 kB
029 Uploading our model's training logs to TensorBoard and comparing them_en.srt - 15.7 kB
025 Preparing subsets of data for model 7 (same as model 6 but 10% of data)_en.srt - 15.7 kB
024 Model 6 Building, training and evaluating a transfer learning model for NLP_en.srt - 15.5 kB
022 Model 5 Building, fitting and evaluating a 1D CNN for text_en.srt - 15.2 kB
012 Discussing the various modelling experiments we're going to run_en.srt - 14.1 kB
017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more_en.srt - 14.1 kB
030 Saving and loading in a trained NLP model with TensorFlow_en.srt - 13.9 kB
004 The typical architecture of a Recurrent Neural Network (RNN)_en.srt - 13.7 kB
008 Converting text data to numbers using tokenisation and embeddings (overview)_en.srt - 13.4 kB
026 Model 7 Building, training and evaluating a transfer learning model on 10% data_en.srt - 13.2 kB
013 Model 0 Building a baseline model to try and improve upon_en.srt - 12.9 kB
032 Visualising our model's most wrong predictions_en.srt - 12.6 kB
005 Preparing a notebook for our first NLP with TensorFlow project_en.srt - 12 kB
003 Example NLP inputs and outputs_en.srt - 12 kB
033 Making and visualising predictions on the test dataset_en.srt - 11.9 kB
007 Splitting data into training and validation sets_en.srt - 8.05 kB
035 NLP Fundamentals in TensorFlow challenge, exercises and extra-curriculum.html - 2.19 kB
001 Welcome to natural language processing with TensorFlow!.html - 1.05 kB
023 Writing a preprocessing function to turn time series data into windows & labels.mp4 - 268 MB
047 Model 7 Putting together the pieces of the puzzle of the N-BEATS model.mp4 - 254 MB
041 Model 7 Replicating the N-BEATS basic block with TensorFlow layer subclassing.mp4 - 231 MB
042 Model 7 Testing our N-BEATS block implementation with dummy data inputs.mp4 - 197 MB
051 Model 8 Making and evaluating predictions with our ensemble model.mp4 - 194 MB
050 Model 8 Building, compiling and fitting an ensemble of models.mp4 - 191 MB
035 Model 5 Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data.mp4 - 177 MB
026 Model 1 Building, compiling and fitting a deep learning model on Bitcoin data.mp4 - 177 MB
028 Model 2 Building, fitting and evaluating a deep model with a larger window size.mp4 - 163 MB
045 Model 7 Getting ready for residual connections.mp4 - 158 MB
008 Downloading and inspecting our Bitcoin historical dataset.mp4 - 158 MB
034 Model 4 Building, fitting and evaluating a Conv1D model on our Bitcoin data.mp4 - 154 MB
043 Model 7 Creating a performant data pipeline for the N-BEATS model with tf.data.mp4 - 130 MB
029 Model 3 Building, fitting and evaluating a model with a larger horizon size.mp4 - 130 MB
027 Creating a function to make predictions with our trained models.mp4 - 129 MB
036 Investigating how to turn our univariate time series into multivariate.mp4 - 127 MB
054 Plotting the prediction intervals of our ensemble model predictions.mp4 - 124 MB
055 (Optional) Discussing the types of uncertainty in machine learning.mp4 - 121 MB
016 Model 0 Making and visualizing a naive forecast model.mp4 - 120 MB
052 Discussing the importance of prediction intervals in forecasting.mp4 - 120 MB
033 Preparing data for building a Conv1D model.mp4 - 119 MB
062 Model 10 Building a model to predict on turkey data (why forecasting is BS).mp4 - 118 MB
063 Comparing the results of all of our models and discussing where to go next.mp4 - 116 MB
021 Formatting data Part 2 Creating a function to label our windowed time series.mp4 - 115 MB
046 Model 7 Outlining the steps we're going to take to build the N-BEATS model.mp4 - 113 MB
060 Model 9 Plotting our model's future forecasts.mp4 - 112 MB
040 Model 7 Discussing what we're going to be doing with the N-BEATS algorithm.mp4 - 111 MB
011 Reading in our Bitcoin data with Python's CSV module.mp4 - 109 MB
018 Implementing MASE with TensorFlow.mp4 - 109 MB
038 Preparing our multivariate time series for a model.mp4 - 106 MB
017 Discussing some of the most common time series evaluation metrics.mp4 - 104 MB
032 Comparing our modelling experiments so far and discussing autocorrelation.mp4 - 98.5 MB
061 Model 10 Introducing the turkey problem and making data for it.mp4 - 98.1 MB
024 Turning our windowed time series data into training and test sets.mp4 - 95.9 MB
030 Adjusting the evaluation function to work for predictions with larger horizons.mp4 - 95 MB
031 Model 3 Visualizing the results.mp4 - 92.2 MB
039 Model 6 Building, fitting and evaluating a multivariate time series model.mp4 - 86.3 MB
059 Model 9 Creating a function to make forecasts into the future.mp4 - 84.2 MB
005 What can be forecast.mp4 - 81.6 MB
015 Discussing the various modelling experiments were going to be running.mp4 - 81.5 MB
056 Model 9 Preparing data to create a model capable of predicting into the future.mp4 - 79.3 MB
037 Creating and plotting a multivariate time series with BTC price and block reward.mp4 - 78.2 MB
022 Discussing the use of windows and horizons in time series data.mp4 - 74.3 MB
053 Getting the upper and lower bounds of our prediction intervals.mp4 - 73.9 MB
009 Different kinds of time series patterns & different amounts of feature variables.mp4 - 71.1 MB
003 What is a time series problem and example forecasting problems at Uber.mp4 - 68.8 MB
044 Model 7 Setting up hyperparameters for the N-BEATS algorithm.mp4 - 68.6 MB
025 Creating a modelling checkpoint callback to save our best performing model.mp4 - 68.4 MB
058 Model 9 Discussing what's required for our model to make future predictions.mp4 - 66.8 MB
012 Creating train and test splits for time series (the wrong way).mp4 - 66.2 MB
020 Discussing other non-TensorFlow kinds of time series forecasting models.mp4 - 63.5 MB
014 Creating a plotting function to visualize our time series data.mp4 - 62.4 MB
013 Creating train and test splits for time series (the right way).mp4 - 50.3 MB
010 Visualizing our Bitcoin historical data with pandas.mp4 - 44.3 MB
019 Creating a function to evaluate our model's forecasts with various metrics.mp4 - 43 MB
057 Model 9 Building, compiling and fitting a future predictions model.mp4 - 42.4 MB
049 Model 8 Ensemble model overview.mp4 - 39.8 MB
002 Introduction to Milestone Project 3 (BitPredict) & where you can get help.mp4 - 31.8 MB
007 Time series forecasting inputs and outputs.mp4 - 30.6 MB
004 Example forecasting problems in daily life.mp4 - 28.5 MB
006 What we're going to cover (broadly).mp4 - 27.1 MB
048 Model 7 Plotting the N-BEATS algorithm we've created and admiring its beauty.mp4 - 25.2 MB
023 Writing a preprocessing function to turn time series data into windows & labels_en.srt - 32.1 kB
047 Model 7 Putting together the pieces of the puzzle of the N-BEATS model_en.srt - 30.8 kB
050 Model 8 Building, compiling and fitting an ensemble of models_en.srt - 29.5 kB
028 Model 2 Building, fitting and evaluating a deep model with a larger window size_en.srt - 27.2 kB
041 Model 7 Replicating the N-BEATS basic block with TensorFlow layer subclassing_en.srt - 26.7 kB
026 Model 1 Building, compiling and fitting a deep learning model on Bitcoin data_en.srt - 26.3 kB
051 Model 8 Making and evaluating predictions with our ensemble model_en.srt - 23 kB
042 Model 7 Testing our N-BEATS block implementation with dummy data inputs_en.srt - 22.2 kB
035 Model 5 Building, fitting and evaluating a LSTM (RNN) model on our Bitcoin data_en.srt - 22.1 kB
008 Downloading and inspecting our Bitcoin historical dataset_en.srt - 21.9 kB
034 Model 4 Building, fitting and evaluating a Conv1D model on our Bitcoin data_en.srt - 21 kB
027 Creating a function to make predictions with our trained models_en.srt - 20.2 kB
063 Comparing the results of all of our models and discussing where to go next_en.srt - 20.1 kB
061 Model 10 Introducing the turkey problem and making data for it_en.srt - 19.6 kB
043 Model 7 Creating a performant data pipeline for the N-BEATS model with tf.data_en.srt - 19.6 kB
062 Model 10 Building a model to predict on turkey data (why forecasting is BS)_en.srt - 19.3 kB
029 Model 3 Building, fitting and evaluating a model with a larger horizon size_en.srt - 19.3 kB
033 Preparing data for building a Conv1D model_en.srt - 19.2 kB
055 (Optional) Discussing the types of uncertainty in machine learning_en.srt - 18.9 kB
021 Formatting data Part 2 Creating a function to label our windowed time series_en.srt - 18.8 kB
036 Investigating how to turn our univariate time series into multivariate_en.srt - 18.3 kB
038 Preparing our multivariate time series for a model_en.srt - 17.9 kB
054 Plotting the prediction intervals of our ensemble model predictions_en.srt - 17.9 kB
016 Model 0 Making and visualizing a naive forecast model_en.srt - 17.8 kB
060 Model 9 Plotting our model's future forecasts_en.srt - 17.7 kB
045 Model 7 Getting ready for residual connections_en.srt - 17.6 kB
052 Discussing the importance of prediction intervals in forecasting_en.srt - 17.2 kB
017 Discussing some of the most common time series evaluation metrics_en.srt - 16.7 kB
011 Reading in our Bitcoin data with Python's CSV module_en.srt - 16.3 kB
059 Model 9 Creating a function to make forecasts into the future_en.srt - 16.2 kB
007 Time series forecasting inputs and outputs_en.srt - 15.9 kB
037 Creating and plotting a multivariate time series with BTC price and block reward_en.srt - 14.8 kB
032 Comparing our modelling experiments so far and discussing autocorrelation_en.srt - 14.5 kB
024 Turning our windowed time series data into training and test sets_en.srt - 14.3 kB
015 Discussing the various modelling experiments were going to be running_en.srt - 14.1 kB
019 Creating a function to evaluate our model's forecasts with various metrics_en.srt - 14.1 kB
046 Model 7 Outlining the steps we're going to take to build the N-BEATS model_en.srt - 14.1 kB
040 Model 7 Discussing what we're going to be doing with the N-BEATS algorithm_en.srt - 13.6 kB
003 What is a time series problem and example forecasting problems at Uber_en.srt - 13.5 kB
005 What can be forecast_en.srt - 13.4 kB
039 Model 6 Building, fitting and evaluating a multivariate time series model_en.srt - 13.4 kB
044 Model 7 Setting up hyperparameters for the N-BEATS algorithm_en.srt - 13.3 kB
031 Model 3 Visualizing the results_en.srt - 13.2 kB
018 Implementing MASE with TensorFlow_en.srt - 13.2 kB
022 Discussing the use of windows and horizons in time series data_en.srt - 13 kB
009 Different kinds of time series patterns & different amounts of feature variables_en.srt - 12.2 kB
012 Creating train and test splits for time series (the wrong way)_en.srt - 11.9 kB
058 Model 9 Discussing what's required for our model to make future predictions_en.srt - 11.8 kB
030 Adjusting the evaluation function to work for predictions with larger horizons_en.srt - 11.3 kB
056 Model 9 Preparing data to create a model capable of predicting into the future_en.srt - 10.9 kB
025 Creating a modelling checkpoint callback to save our best performing model_en.srt - 10.9 kB
048 Model 7 Plotting the N-BEATS algorithm we've created and admiring its beauty_en.srt - 10.7 kB
053 Getting the upper and lower bounds of our prediction intervals_en.srt - 10.7 kB
014 Creating a plotting function to visualize our time series data_en.srt - 10.5 kB
013 Creating train and test splits for time series (the right way)_en.srt - 10.5 kB
004 Example forecasting problems in daily life_en.srt - 8.16 kB
020 Discussing other non-TensorFlow kinds of time series forecasting models_en.srt - 7.87 kB
010 Visualizing our Bitcoin historical data with pandas_en.srt - 7.53 kB
057 Model 9 Building, compiling and fitting a future predictions model_en.srt - 7.27 kB
049 Model 8 Ensemble model overview_en.srt - 7.07 kB
002 Introduction to Milestone Project 3 (BitPredict) & where you can get help_en.srt - 6.65 kB
006 What we're going to cover (broadly)_en.srt - 4.55 kB
064 TensorFlow Time Series Fundamentals Challenge and Extra Resources.html - 1.91 kB
001 Welcome to time series fundamentals with TensorFlow + Milestone Project 3!.html - 1.35 kB
006 Writing a preprocessing function to structure our data for modelling.mp4 - 233 MB
017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer.mp4 - 208 MB
021 Model 4 Building a multi-input model (hybrid token + character embeddings).mp4 - 195 MB
014 Model 1 Building, fitting and evaluating a Conv1D with token embeddings.mp4 - 179 MB
029 Model 5 Completing the build of a tribrid embedding model for sequences.mp4 - 164 MB
001 Introduction to Milestone Project 2 SkimLit.mp4 - 157 MB
004 Setting up our notebook for Milestone Project 2 (getting the data).mp4 - 154 MB
024 Model 4 Building, fitting and evaluating a hybrid embedding model.mp4 - 149 MB
035 Congratulations and your challenge before heading to the next module.mp4 - 144 MB
005 Visualising examples from the dataset (becoming one with the data).mp4 - 140 MB
019 Model 3 Building, fitting and evaluating a Conv1D model on character embeddings.mp4 - 138 MB
011 Creating a text vectoriser to map our tokens (text) to numbers.mp4 - 137 MB
015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2.mp4 - 134 MB
008 Turning our target labels into numbers (ML models require numbers).mp4 - 125 MB
026 Encoding the line number feature to used with Model 5.mp4 - 119 MB
030 Visually inspecting the architecture of our tribrid embedding model.mp4 - 114 MB
016 Model 2 Building, fitting and evaluating a Conv1D model with token embeddings.mp4 - 113 MB
031 Creating multi-level data input pipelines for Model 5 with the tf.data API.mp4 - 106 MB
012 Creating a custom token embedding layer with TensorFlow.mp4 - 106 MB
022 Model 4 Plotting and visually exploring different data inputs.mp4 - 93 MB
010 Preparing our data for deep sequence models.mp4 - 89.6 MB
023 Crafting multi-input fast loading tf.data datasets for Model 4.mp4 - 89.5 MB
034 Saving, loading & testing our best performing model.mp4 - 89.2 MB
028 Model 5 Building the foundations of a tribrid embedding model.mp4 - 86.2 MB
009 Model 0 Creating, fitting and evaluating a baseline model for SkimLit.mp4 - 85.7 MB
033 Comparing the performance of all of our modelling experiments.mp4 - 82.2 MB
013 Creating fast loading dataset with the TensorFlow tf.data API.mp4 - 81.8 MB
007 Performing visual data analysis on our preprocessed text.mp4 - 78.8 MB
002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts).mp4 - 75.2 MB
018 Creating a character-level embedding layer with tf.keras.layers.Embedding.mp4 - 69.6 MB
027 Encoding the total lines feature to be used with Model 5.mp4 - 67.4 MB
020 Discussing how we're going to build Model 4 (character + token embeddings).mp4 - 63.3 MB
003 SkimLit inputs and outputs.mp4 - 57.7 MB
032 Bringing SkimLit to life!!! (fitting and evaluating Model 5).mp4 - 49.3 MB
025 Model 5 Adding positional embeddings via feature engineering (overview).mp4 - 46.9 MB
017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer_en.srt - 30.5 kB
006 Writing a preprocessing function to structure our data for modelling_en.srt - 26.6 kB
014 Model 1 Building, fitting and evaluating a Conv1D with token embeddings_en.srt - 25.2 kB
021 Model 4 Building a multi-input model (hybrid token + character embeddings)_en.srt - 23.1 kB
001 Introduction to Milestone Project 2 SkimLit_en.srt - 22.6 kB
004 Setting up our notebook for Milestone Project 2 (getting the data)_en.srt - 20.2 kB
011 Creating a text vectoriser to map our tokens (text) to numbers_en.srt - 19.5 kB
019 Model 3 Building, fitting and evaluating a Conv1D model on character embeddings_en.srt - 19.4 kB
008 Turning our target labels into numbers (ML models require numbers)_en.srt - 19.3 kB
024 Model 4 Building, fitting and evaluating a hybrid embedding model_en.srt - 19 kB
029 Model 5 Completing the build of a tribrid embedding model for sequences_en.srt - 18.6 kB
003 SkimLit inputs and outputs_en.srt - 18.5 kB
035 Congratulations and your challenge before heading to the next module_en.srt - 17.6 kB
005 Visualising examples from the dataset (becoming one with the data)_en.srt - 17.6 kB
026 Encoding the line number feature to used with Model 5_en.srt - 17.1 kB
016 Model 2 Building, fitting and evaluating a Conv1D model with token embeddings_en.srt - 16.5 kB
015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2_en.srt - 15.4 kB
032 Bringing SkimLit to life!!! (fitting and evaluating Model 5)_en.srt - 15.2 kB
030 Visually inspecting the architecture of our tribrid embedding model_en.srt - 14.2 kB
010 Preparing our data for deep sequence models_en.srt - 13.3 kB
013 Creating fast loading dataset with the TensorFlow tf.data API_en.srt - 13.1 kB
012 Creating a custom token embedding layer with TensorFlow_en.srt - 12.9 kB
033 Comparing the performance of all of our modelling experiments_en.srt - 12.7 kB
022 Model 4 Plotting and visually exploring different data inputs_en.srt - 12.6 kB
002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts)_en.srt - 12.2 kB
009 Model 0 Creating, fitting and evaluating a baseline model for SkimLit_en.srt - 11.8 kB
028 Model 5 Building the foundations of a tribrid embedding model_en.srt - 11.7 kB
023 Crafting multi-input fast loading tf.data datasets for Model 4_en.srt - 11.1 kB
007 Performing visual data analysis on our preprocessed text_en.srt - 11.1 kB
031 Creating multi-level data input pipelines for Model 5 with the tf.data API_en.srt - 11 kB
018 Creating a character-level embedding layer with tf.keras.layers.Embedding_en.srt - 10.7 kB
025 Model 5 Adding positional embeddings via feature engineering (overview)_en.srt - 10.4 kB
027 Encoding the total lines feature to be used with Model 5_en.srt - 10.4 kB
034 Saving, loading & testing our best performing model_en.srt - 10.3 kB
020 Discussing how we're going to build Model 4 (character + token embeddings)_en.srt - 8.95 kB
036 Milestone Project 2 (SkimLit) challenge, exercises and extra-curriculum.html - 1.57 kB
019 Preparing Model 3 (our first fine-tuned model).mp4 - 211 MB
003 Downloading and turning our images into a TensorFlow BatchDataset.mp4 - 184 MB
015 Building Model 2 (with a data augmentation layer and 10% of training data).mp4 - 169 MB
014 Building Model 1 (with a data augmentation layer and 1% of training data).mp4 - 164 MB
008 Getting a feature vector from our trained model.mp4 - 157 MB
006 Creating our first model with the TensorFlow Keras Functional API.mp4 - 141 MB
013 Visualizing what happens when images pass through our data augmentation layer.mp4 - 129 MB
011 Building a data augmentation layer to use inside our model.mp4 - 124 MB
010 Downloading and preparing the data for Model 1 (1 percent of training data).mp4 - 103 MB
024 Fine-tuning Model 4 on 100% of the training data and evaluating its results.mp4 - 103 MB
023 Preparing our final modelling experiment (Model 4).mp4 - 101 MB
025 Comparing our modelling experiment results in TensorBoard.mp4 - 101 MB
002 Importing a script full of helper functions (and saving lots of space).mp4 - 95.6 MB
021 Comparing our model's results before and after fine-tuning.mp4 - 88.9 MB
007 Compiling and fitting our first Functional API model.mp4 - 84 MB
017 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp4 - 72.7 MB
016 Creating a ModelCheckpoint to save our model's weights during training.mp4 - 72.4 MB
018 Loading and comparing saved weights to our existing trained Model 2.mp4 - 66 MB
001 Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.mp4 - 65.1 MB
020 Fitting and evaluating Model 3 (our first fine-tuned model).mp4 - 62.4 MB
022 Downloading and preparing data for our biggest experiment yet (Model 4).mp4 - 59.4 MB
009 Drilling into the concept of a feature vector (a learned representation).mp4 - 55.8 MB
026 How to view and delete previous TensorBoard experiments.mp4 - 19.4 MB
005 Comparing the TensorFlow Keras Sequential API versus the Functional API.mp4 - 17.8 MB
004 Discussing the four (actually five) modelling experiments we're running.mp4 - 11.7 MB
019 Preparing Model 3 (our first fine-tuned model)_en.srt - 26.5 kB
015 Building Model 2 (with a data augmentation layer and 10% of training data)_en.srt - 24 kB
014 Building Model 1 (with a data augmentation layer and 1% of training data)_en.srt - 23 kB
003 Downloading and turning our images into a TensorFlow BatchDataset_en.srt - 22.5 kB
008 Getting a feature vector from our trained model_en.srt - 18.2 kB
011 Building a data augmentation layer to use inside our model_en.srt - 16.5 kB
013 Visualizing what happens when images pass through our data augmentation layer_en.srt - 16.5 kB
006 Creating our first model with the TensorFlow Keras Functional API_en.srt - 16.2 kB
007 Compiling and fitting our first Functional API model_en.srt - 16.1 kB
025 Comparing our modelling experiment results in TensorBoard_en.srt - 16.1 kB
023 Preparing our final modelling experiment (Model 4)_en.srt - 15.2 kB
024 Fine-tuning Model 4 on 100% of the training data and evaluating its results_en.srt - 15.2 kB
021 Comparing our model's results before and after fine-tuning_en.srt - 14.2 kB
010 Downloading and preparing the data for Model 1 (1 percent of training data)_en.srt - 13.3 kB
016 Creating a ModelCheckpoint to save our model's weights during training_en.srt - 11 kB
020 Fitting and evaluating Model 3 (our first fine-tuned model)_en.srt - 10.9 kB
017 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint)_en.srt - 10.1 kB
001 Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning_en.srt - 10 kB
002 Importing a script full of helper functions (and saving lots of space)_en.srt - 10 kB
018 Loading and comparing saved weights to our existing trained Model 2_en.srt - 9.88 kB
022 Downloading and preparing data for our biggest experiment yet (Model 4)_en.srt - 9.19 kB
009 Drilling into the concept of a feature vector (a learned representation)_en.srt - 5.52 kB
005 Comparing the TensorFlow Keras Sequential API versus the Functional API_en.srt - 4.13 kB
004 Discussing the four (actually five) modelling experiments we're running_en.srt - 3.67 kB
026 How to view and delete previous TensorBoard experiments_en.srt - 2.88 kB
027 Transfer Learning in TensorFlow Part 2 challenge, exercises and extra-curriculum.html - 2.67 kB
012 Note Small fix for next video, for images not augmenting.html - 2 kB
012 Evaluating a TensorFlow model part 3 (getting a model summary).mp4 - 206 MB
006 The major steps in modelling with TensorFlow.mp4 - 195 MB
026 Putting together what we've learned part 3 (improving our regression model).mp4 - 165 MB
024 Putting together what we've learned part 1 (preparing a dataset).mp4 - 154 MB
009 Steps in improving a model with TensorFlow part 3.mp4 - 142 MB
018 Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4 - 134 MB
025 Putting together what we've learned part 2 (building a regression model).mp4 - 129 MB
022 How to load and use a saved TensorFlow model.mp4 - 111 MB
027 Preprocessing data with feature scaling part 1 (what is feature scaling).mp4 - 98.1 MB
021 How to save a TensorFlow model.mp4 - 98 MB
020 Comparing and tracking your TensorFlow modelling experiments.mp4 - 97.5 MB
008 Steps in improving a model with TensorFlow part 2.mp4 - 96 MB
004 Creating sample regression data (so we can model it).mp4 - 93.8 MB
028 Preprocessing data with feature scaling part 2 (normalising our data).mp4 - 87.2 MB
011 Evaluating a TensorFlow model part 2 (the three datasets).mp4 - 85.2 MB
014 Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp4 - 83.4 MB
029 Preprocessing data with feature scaling part 3 (fitting a model on scaled data).mp4 - 80.6 MB
013 Evaluating a TensorFlow model part 4 (visualising a model's layers).mp4 - 74.6 MB
015 Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp4 - 74.4 MB
023 (Optional) How to save and download files from Google Colab.mp4 - 72.5 MB
010 Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).mp4 - 70.2 MB
016 Evaluating a TensorFlow regression model part 7 (mean absolute error).mp4 - 59.4 MB
003 Anatomy and architecture of a neural network regression model.mp4 - 54.4 MB
001 Introduction to Neural Network Regression with TensorFlow.mp4 - 54 MB
002 Inputs and outputs of a neural network regression model.mp4 - 52.8 MB
007 Steps in improving a model with TensorFlow part 1.mp4 - 47.8 MB
019 Setting up TensorFlow modelling experiments part 2 (increasing complexity).mp4 - 35.1 MB
017 Evaluating a TensorFlow regression model part 7 (mean square error).mp4 - 34.3 MB
006 The major steps in modelling with TensorFlow_en.srt - 27.1 kB
012 Evaluating a TensorFlow model part 3 (getting a model summary)_en.srt - 22 kB
026 Putting together what we've learned part 3 (improving our regression model)_en.srt - 19.3 kB
024 Putting together what we've learned part 1 (preparing a dataset)_en.srt - 19.2 kB
025 Putting together what we've learned part 2 (building a regression model)_en.srt - 18.4 kB
018 Setting up TensorFlow modelling experiments part 1 (start with a simple model)_en.srt - 17.9 kB
009 Steps in improving a model with TensorFlow part 3_en.srt - 17.2 kB
004 Creating sample regression data (so we can model it)_en.srt - 16.5 kB
019 Setting up TensorFlow modelling experiments part 2 (increasing complexity)_en.srt - 16.2 kB
011 Evaluating a TensorFlow model part 2 (the three datasets)_en.srt - 14.4 kB
028 Preprocessing data with feature scaling part 2 (normalising our data)_en.srt - 14.3 kB
027 Preprocessing data with feature scaling part 1 (what is feature scaling)_en.srt - 14.2 kB
020 Comparing and tracking your TensorFlow modelling experiments_en.srt - 13.5 kB
002 Inputs and outputs of a neural network regression model_en.srt - 13.4 kB
008 Steps in improving a model with TensorFlow part 2_en.srt - 13.4 kB
022 How to load and use a saved TensorFlow model_en.srt - 13.1 kB
003 Anatomy and architecture of a neural network regression model_en.srt - 12.5 kB
014 Evaluating a TensorFlow model part 5 (visualising a model's predictions)_en.srt - 12.2 kB
001 Introduction to Neural Network Regression with TensorFlow_en.srt - 11.7 kB
021 How to save a TensorFlow model_en.srt - 11.7 kB
015 Evaluating a TensorFlow model part 6 (common regression evaluation metrics)_en.srt - 11.4 kB
029 Preprocessing data with feature scaling part 3 (fitting a model on scaled data)_en.srt - 11.2 kB
010 Evaluating a TensorFlow model part 1 (visualise, visualise, visualise)_en.srt - 10 kB
013 Evaluating a TensorFlow model part 4 (visualising a model's layers)_en.srt - 9.45 kB
016 Evaluating a TensorFlow regression model part 7 (mean absolute error)_en.srt - 8.3 kB
023 (Optional) How to save and download files from Google Colab_en.srt - 7.98 kB
007 Steps in improving a model with TensorFlow part 1_en.srt - 7.81 kB
017 Evaluating a TensorFlow regression model part 7 (mean square error)_en.srt - 3.98 kB
005 Note Code update for upcoming lecture(s) for TensorFlow 2.7.0+ fix.html - 2.44 kB
030 TensorFlow Regression challenge, exercises & extra-curriculum.html - 2.01 kB
031 Learning Guideline.html - 336 B
external-links.txt - 130 B
0. Websites you may like
[FreeCourseSite.com].url - 127 B
[CourseClub.Me].url - 122 B
[GigaCourse.Com].url - 49 B
001 All-course-materials-and-links-notebooks-code-data-slides-on-GitHub.url - 77 B
015 Breaking our CNN model down part 5 Looking inside a Conv2D layer.mp4 - 200 MB
020 Breaking our CNN model down part 10 Visualizing our augmented data.mp4 - 169 MB
018 Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.mp4 - 139 MB
032 Multi-class CNN's part 6 Trying to fix overfitting by removing layers.mp4 - 138 MB
033 Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.mp4 - 129 MB
035 Multi-class CNN's part 9 Making predictions with our model on custom images.mp4 - 127 MB
008 Using a GPU to run our CNN model 5x faster.mp4 - 123 MB
012 Breaking our CNN model down part 2 Preparing to load our data.mp4 - 115 MB
010 Improving our non-CNN model by adding more layers.mp4 - 114 MB
025 Writing a helper function to load and preprocessing custom images.mp4 - 113 MB
022 Breaking our CNN model down part 12 Discovering the power of shuffling data.mp4 - 110 MB
013 Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.mp4 - 110 MB
009 Trying a non-CNN model on our image data.mp4 - 107 MB
026 Making a prediction on a custom image with our trained CNN.mp4 - 106 MB
021 Breaking our CNN model down part 11 Training a CNN model on augmented data.mp4 - 101 MB
011 Breaking our CNN model down part 1 Becoming one with the data.mp4 - 96.7 MB
029 Multi-class CNN's part 3 Building a multi-class CNN model.mp4 - 96.4 MB
005 Becoming One With Data Part 2.mp4 - 94.6 MB
017 Breaking our CNN model down part 7 Evaluating our CNN's training curves.mp4 - 93.5 MB
014 Breaking our CNN model down part 4 Building a baseline CNN model.mp4 - 91.7 MB
001 Introduction to Computer Vision with TensorFlow.mp4 - 78.6 MB
003 Downloading an image dataset for our first Food Vision model.mp4 - 76.9 MB
036 Saving and loading our trained CNN model.mp4 - 73.9 MB
019 Breaking our CNN model down part 9 Reducing overfitting with data augmentation.mp4 - 69.6 MB
016 Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.mp4 - 68.1 MB
028 Multi-class CNN's part 2 Preparing our data (turning it into tensors).mp4 - 64.4 MB
027 Multi-class CNN's part 1 Becoming one with the data.mp4 - 64 MB
030 Multi-class CNN's part 4 Fitting a multi-class CNN model to the data.mp4 - 64 MB
007 Building an end to end CNN Model.mp4 - 62.1 MB
004 Becoming One With Data.mp4 - 47.9 MB
024 Downloading a custom image to make predictions on.mp4 - 46.5 MB
023 Breaking our CNN model down part 13 Exploring options to improve our model.mp4 - 44.5 MB
034 Multi-class CNN's part 8 Things you could do to improve your CNN model.mp4 - 37.5 MB
031 Multi-class CNN's part 5 Evaluating our multi-class CNN model.mp4 - 36 MB
002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.mp4 - 35.5 MB
006 Becoming One With Data Part 3.mp4 - 35.4 MB
007 Building an end to end CNN Model_en.srt - 26.6 kB
015 Breaking our CNN model down part 5 Looking inside a Conv2D layer_en.srt - 23.3 kB
027 Multi-class CNN's part 1 Becoming one with the data_en.srt - 23.2 kB
020 Breaking our CNN model down part 10 Visualizing our augmented data_en.srt - 22.1 kB
018 Breaking our CNN model down part 8 Reducing overfitting with Max Pooling_en.srt - 19.7 kB
017 Breaking our CNN model down part 7 Evaluating our CNN's training curves_en.srt - 17.5 kB
012 Breaking our CNN model down part 2 Preparing to load our data_en.srt - 16.9 kB
032 Multi-class CNN's part 6 Trying to fix overfitting by removing layers_en.srt - 16.8 kB
033 Multi-class CNN's part 7 Trying to fix overfitting with data augmentation_en.srt - 16.7 kB
005 Becoming One With Data Part 2_en.srt - 16.4 kB
026 Making a prediction on a custom image with our trained CNN_en.srt - 15.8 kB
001 Introduction to Computer Vision with TensorFlow_en.srt - 15.4 kB
022 Breaking our CNN model down part 12 Discovering the power of shuffling data_en.srt - 14.6 kB
010 Improving our non-CNN model by adding more layers_en.srt - 14.3 kB
025 Writing a helper function to load and preprocessing custom images_en.srt - 14.1 kB
021 Breaking our CNN model down part 11 Training a CNN model on augmented data_en.srt - 13.9 kB
013 Breaking our CNN model down part 3 Loading our data with ImageDataGenerator_en.srt - 13.8 kB
008 Using a GPU to run our CNN model 5x faster_en.srt - 13.4 kB
011 Breaking our CNN model down part 1 Becoming one with the data_en.srt - 13.3 kB
002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow_en.srt - 12.4 kB
035 Multi-class CNN's part 9 Making predictions with our model on custom images_en.srt - 12.2 kB
009 Trying a non-CNN model on our image data_en.srt - 11.9 kB
014 Breaking our CNN model down part 4 Building a baseline CNN model_en.srt - 11.5 kB
029 Multi-class CNN's part 3 Building a multi-class CNN model_en.srt - 10.9 kB
003 Downloading an image dataset for our first Food Vision model_en.srt - 10.6 kB
028 Multi-class CNN's part 2 Preparing our data (turning it into tensors)_en.srt - 10.2 kB
016 Breaking our CNN model down part 6 Compiling and fitting our baseline CNN_en.srt - 10.1 kB
019 Breaking our CNN model down part 9 Reducing overfitting with data augmentation_en.srt - 9.61 kB
036 Saving and loading our trained CNN model_en.srt - 9.29 kB
030 Multi-class CNN's part 4 Fitting a multi-class CNN model to the data_en.srt - 9.17 kB
023 Breaking our CNN model down part 13 Exploring options to improve our model_en.srt - 7.71 kB
024 Downloading a custom image to make predictions on_en.srt - 7.1 kB
031 Multi-class CNN's part 5 Evaluating our multi-class CNN model_en.srt - 6.96 kB
004 Becoming One With Data_en.srt - 6.89 kB
006 Becoming One With Data Part 3_en.srt - 6.7 kB
034 Multi-class CNN's part 8 Things you could do to improve your CNN model_en.srt - 6.33 kB
037 TensorFlow computer vision and CNNs challenge, exercises & extra-curriculum.html - 2.54 kB
external-links.txt - 207 B
001 All-course-materials-and-links-notebooks-code-data-slides-on-GitHub.url - 82 B
015 CNN-Explainer-website.url - 65 B
018 Making predictions on our test images and evaluating them.mp4 - 182 MB
014 Creating a confusion matrix for our model's 101 different classes.mp4 - 171 MB
015 Evaluating every individual class in our dataset.mp4 - 140 MB
002 Getting helper functions ready and downloading data to model.mp4 - 139 MB
021 Plotting and visualising the samples our model got most wrong.mp4 - 134 MB
011 Making predictions with our trained model on 25,250 test samples.mp4 - 121 MB
020 Writing code to uncover our model's most wrong predictions.mp4 - 116 MB
022 Making predictions on and plotting our own custom images.mp4 - 115 MB
017 Creating a function to load and prepare images for making predictions.mp4 - 114 MB
007 Unfreezing some layers in our base model to prepare for fine-tuning.mp4 - 105 MB
005 Creating a headless EfficientNetB0 model with data augmentation built in.mp4 - 85.4 MB
010 Downloading a pretrained model to make and evaluate predictions with.mp4 - 84.1 MB
008 Fine-tuning our feature extraction model and evaluating its performance.mp4 - 69.4 MB
016 Plotting our model's F1-scores for each separate class.mp4 - 67.6 MB
006 Fitting and evaluating our biggest transfer learning model yet.mp4 - 63.1 MB
019 Discussing the benefits of finding your model's most wrong predictions.mp4 - 61.9 MB
009 Saving and loading our trained model.mp4 - 60.7 MB
013 Confirming our model's predictions are in the same order as the test labels.mp4 - 53.4 MB
001 Introduction to Transfer Learning Part 3 Scaling Up.mp4 - 43 MB
012 Unravelling our test dataset for comparing ground truth labels to predictions.mp4 - 40 MB
004 Creating a data augmentation layer to use with our model.mp4 - 38 MB
003 Outlining the model we're going to build and building a ModelCheckpoint callback.mp4 - 30.6 MB
018 Making predictions on our test images and evaluating them_en.srt - 24 kB
015 Evaluating every individual class in our dataset_en.srt - 19.8 kB
002 Getting helper functions ready and downloading data to model_en.srt - 18.2 kB
014 Creating a confusion matrix for our model's 101 different classes_en.srt - 17.9 kB
020 Writing code to uncover our model's most wrong predictions_en.srt - 17.4 kB
007 Unfreezing some layers in our base model to prepare for fine-tuning_en.srt - 17 kB
011 Making predictions with our trained model on 25,250 test samples_en.srt - 16.6 kB
017 Creating a function to load and prepare images for making predictions_en.srt - 16.2 kB
021 Plotting and visualising the samples our model got most wrong_en.srt - 15.8 kB
022 Making predictions on and plotting our own custom images_en.srt - 15 kB
005 Creating a headless EfficientNetB0 model with data augmentation built in_en.srt - 13.8 kB
008 Fine-tuning our feature extraction model and evaluating its performance_en.srt - 12.2 kB
006 Fitting and evaluating our biggest transfer learning model yet_en.srt - 11.7 kB
016 Plotting our model's F1-scores for each separate class_en.srt - 11 kB
001 Introduction to Transfer Learning Part 3 Scaling Up_en.srt - 10.4 kB
019 Discussing the benefits of finding your model's most wrong predictions_en.srt - 9.64 kB
009 Saving and loading our trained model_en.srt - 9.2 kB
010 Downloading a pretrained model to make and evaluate predictions with_en.srt - 9.13 kB
012 Unravelling our test dataset for comparing ground truth labels to predictions_en.srt - 7.9 kB
003 Outlining the model we're going to build and building a ModelCheckpoint callback_en.srt - 7.59 kB
013 Confirming our model's predictions are in the same order as the test labels_en.srt - 6.94 kB
004 Creating a data augmentation layer to use with our model_en.srt - 6.4 kB
023 Transfer Learning in TensorFlow Part 3 challenge, exercises and extra-curriculum.html - 2.32 kB
009 Creating a function to view our model's not so good predictions.mp4 - 171 MB
019 Using callbacks to find a model's ideal learning rate.mp4 - 164 MB
016 Non-linearity part 5 Replicating non-linear activation functions from scratch.mp4 - 156 MB
027 Multi-class classification part 3 Building a multi-class classification model.mp4 - 151 MB
017 Getting great results in less time by tweaking the learning rate.mp4 - 145 MB
034 What patterns is our model learning.mp4 - 134 MB
007 Building a not very good classification model with TensorFlow.mp4 - 133 MB
014 Non-linearity part 3 Upgrading our non-linear model with more layers.mp4 - 132 MB
011 Make our poor classification model work for a regression dataset.mp4 - 132 MB
031 Multi-class classification part 7 Evaluating our model.mp4 - 125 MB
024 Making our confusion matrix prettier.mp4 - 121 MB
028 Multi-class classification part 4 Improving performance with normalisation.mp4 - 120 MB
004 Typical architecture of neural network classification models with TensorFlow.mp4 - 119 MB
005 Creating and viewing classification data to model.mp4 - 112 MB
015 Non-linearity part 4 Modelling our non-linear data once and for all.mp4 - 104 MB
012 Non-linearity part 1 Straight lines and non-straight lines.mp4 - 102 MB
020 Training and evaluating a model with an ideal learning rate.mp4 - 93.9 MB
025 Putting things together with multi-class classification part 1 Getting the data.mp4 - 91.5 MB
008 Trying to improve our not very good classification model.mp4 - 89.3 MB
001 Introduction to neural network classification in TensorFlow.mp4 - 66 MB
018 Using the TensorFlow History object to plot a model's loss curves.mp4 - 66 MB
013 Non-linearity part 2 Building our first neural network with non-linearity.mp4 - 63.3 MB
033 Multi-class classification part 9 Visualising random model predictions.mp4 - 61.8 MB
023 Creating our first confusion matrix (to see where our model is getting confused).mp4 - 59.2 MB
026 Multi-class classification part 2 Becoming one with the data.mp4 - 51.2 MB
006 Checking the input and output shapes of our classification data.mp4 - 40.8 MB
021 Introducing more classification evaluation methods.mp4 - 38.6 MB
030 Multi-class classification part 6 Finding the ideal learning rate.mp4 - 38.5 MB
032 Multi-class classification part 8 Creating a confusion matrix.mp4 - 35.9 MB
022 Finding the accuracy of our classification model.mp4 - 35.6 MB
002 Example classification problems (and their inputs and outputs).mp4 - 21.4 MB
029 Multi-class classification part 5 Comparing normalised and non-normalised data.mp4 - 19.7 MB
003 Input and output tensors of classification problems.mp4 - 19.6 MB
019 Using callbacks to find a model's ideal learning rate_en.srt - 25.5 kB
027 Multi-class classification part 3 Building a multi-class classification model_en.srt - 21.6 kB
034 What patterns is our model learning_en.srt - 21.3 kB
017 Getting great results in less time by tweaking the learning rate_en.srt - 19.8 kB
009 Creating a function to view our model's not so good predictions_en.srt - 19.4 kB
016 Non-linearity part 5 Replicating non-linear activation functions from scratch_en.srt - 18.7 kB
024 Making our confusion matrix prettier_en.srt - 18.7 kB
031 Multi-class classification part 7 Evaluating our model_en.srt - 17.4 kB
011 Make our poor classification model work for a regression dataset_en.srt - 17.2 kB
028 Multi-class classification part 4 Improving performance with normalisation_en.srt - 16.6 kB
007 Building a not very good classification model with TensorFlow_en.srt - 16.4 kB
030 Multi-class classification part 6 Finding the ideal learning rate_en.srt - 15.3 kB
004 Typical architecture of neural network classification models with TensorFlow_en.srt - 15 kB
005 Creating and viewing classification data to model_en.srt - 14.7 kB
014 Non-linearity part 3 Upgrading our non-linear model with more layers_en.srt - 14.7 kB
012 Non-linearity part 1 Straight lines and non-straight lines_en.srt - 14.1 kB
025 Putting things together with multi-class classification part 1 Getting the data_en.srt - 14.1 kB
033 Multi-class classification part 9 Visualising random model predictions_en.srt - 13.8 kB
001 Introduction to neural network classification in TensorFlow_en.srt - 13.1 kB
008 Trying to improve our not very good classification model_en.srt - 13 kB
015 Non-linearity part 4 Modelling our non-linear data once and for all_en.srt - 12.3 kB
020 Training and evaluating a model with an ideal learning rate_en.srt - 12.2 kB
023 Creating our first confusion matrix (to see where our model is getting confused)_en.srt - 11.8 kB
026 Multi-class classification part 2 Becoming one with the data_en.srt - 10.2 kB
002 Example classification problems (and their inputs and outputs)_en.srt - 10.1 kB
021 Introducing more classification evaluation methods_en.srt - 9.08 kB
003 Input and output tensors of classification problems_en.srt - 9.06 kB
018 Using the TensorFlow History object to plot a model's loss curves_en.srt - 8.58 kB
013 Non-linearity part 2 Building our first neural network with non-linearity_en.srt - 7.76 kB
032 Multi-class classification part 8 Creating a confusion matrix_en.srt - 6.83 kB
006 Checking the input and output shapes of our classification data_en.srt - 6.73 kB
022 Finding the accuracy of our classification model_en.srt - 5.76 kB
029 Multi-class classification part 5 Comparing normalised and non-normalised data_en.srt - 5.57 kB
010 Note Updates for TensorFlow 2.7.0.html - 3.53 kB
035 TensorFlow classification challenge, exercises & extra-curriculum.html - 2.51 kB
external-links.txt - 135 B
001 All-course-materials-and-links-notebooks-code-data-slides-on-GitHub.url - 82 B
010 Comparing Our Model's Results.mp4 - 153 MB
005 Building and compiling a TensorFlow Hub feature extraction model.mp4 - 145 MB
002 Downloading and preparing data for our first transfer learning model.mp4 - 140 MB
009 Different Types of Transfer Learning.mp4 - 119 MB
008 Building and training a pre-trained EfficientNet model on our data.mp4 - 113 MB
006 Blowing our previous models out of the water with transfer learning.mp4 - 107 MB
003 Introducing Callbacks in TensorFlow and making a callback to track our models.mp4 - 100 MB
004 Exploring the TensorFlow Hub website for pretrained models.mp4 - 91.9 MB
007 Plotting the loss curves of our ResNet feature extraction model.mp4 - 65.3 MB
001 What is and why use transfer learning.mp4 - 31.9 MB
012 Exercise Imposter Syndrome.mp4 - 28.7 MB
010 Comparing Our Model's Results_en.srt - 22.1 kB
005 Building and compiling a TensorFlow Hub feature extraction model_en.srt - 19.4 kB
002 Downloading and preparing data for our first transfer learning model_en.srt - 18.6 kB
001 What is and why use transfer learning_en.srt - 16.3 kB
009 Different Types of Transfer Learning_en.srt - 16.1 kB
004 Exploring the TensorFlow Hub website for pretrained models_en.srt - 15 kB
008 Building and training a pre-trained EfficientNet model on our data_en.srt - 14.6 kB
003 Introducing Callbacks in TensorFlow and making a callback to track our models_en.srt - 14.6 kB
006 Blowing our previous models out of the water with transfer learning_en.srt - 14 kB
007 Plotting the loss curves of our ResNet feature extraction model_en.srt - 11.1 kB
012 Exercise Imposter Syndrome_en.srt - 4.6 kB
011 TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum.html - 2.47 kB
external-links.txt - 130 B
001 All-course-materials-and-links-notebooks-code-data-slides-on-GitHub.url - 77 B
011 Creating your first tensors with TensorFlow and tf.constant().mp4 - 140 MB
030 Making sure our tensor operations run really fast on GPUs.mp4 - 118 MB
020 Matrix multiplication with tensors part 2.mp4 - 112 MB
019 Matrix multiplication with tensors part 1.mp4 - 108 MB
015 Creating tensors from NumPy arrays.mp4 - 106 MB
025 Finding the positional minimum and maximum of a tensor (argmin and argmax).mp4 - 102 MB
014 Shuffling the order of tensors.mp4 - 94.8 MB
023 Tensor aggregation (finding the min, max, mean & more).mp4 - 94.5 MB
013 Creating random tensors with TensorFlow.mp4 - 93.1 MB
016 Getting information from your tensors (tensor attributes).mp4 - 91.1 MB
017 Indexing and expanding tensors.mp4 - 89.9 MB
021 Matrix multiplication with tensors part 3.mp4 - 84.4 MB
022 Changing the datatype of tensors.mp4 - 76.3 MB
012 Creating tensors with TensorFlow and tf.Variable().mp4 - 74.9 MB
024 Tensor troubleshooting example (updating tensor datatypes).mp4 - 74.1 MB
006 What is and why use TensorFlow.mp4 - 72.7 MB
003 What are neural networks.mp4 - 68.8 MB
005 What is deep learning already being used for.mp4 - 67.7 MB
002 Why use deep learning.mp4 - 64.3 MB
027 One-hot encoding tensors.mp4 - 63.1 MB
028 Trying out more tensor math operations.mp4 - 60 MB
018 Manipulating tensors with basic operations.mp4 - 48.2 MB
001 What is deep learning.mp4 - 38.1 MB
026 Squeezing a tensor (removing all 1-dimension axes).mp4 - 31.6 MB
009 How to approach this course.mp4 - 26.2 MB
007 What is a Tensor.mp4 - 20.3 MB
029 Exploring TensorFlow and NumPy's compatibility.mp4 - 17.4 MB
008 What we're going to cover throughout the course.mp4 - 15.1 MB
011 Creating your first tensors with TensorFlow and tf.constant()_en.srt - 25.3 kB
020 Matrix multiplication with tensors part 2_en.srt - 17.8 kB
016 Getting information from your tensors (tensor attributes)_en.srt - 17.4 kB
017 Indexing and expanding tensors_en.srt - 17.4 kB
019 Matrix multiplication with tensors part 1_en.srt - 15.6 kB
015 Creating tensors from NumPy arrays_en.srt - 15.4 kB
003 What are neural networks_en.srt - 15.1 kB
030 Making sure our tensor operations run really fast on GPUs_en.srt - 14.8 kB
002 Why use deep learning_en.srt - 14.5 kB
005 What is deep learning already being used for_en.srt - 13.8 kB
021 Matrix multiplication with tensors part 3_en.srt - 13.6 kB
013 Creating random tensors with TensorFlow_en.srt - 13.3 kB
023 Tensor aggregation (finding the min, max, mean & more)_en.srt - 13.2 kB
014 Shuffling the order of tensors_en.srt - 12.9 kB
025 Finding the positional minimum and maximum of a tensor (argmin and argmax)_en.srt - 12.7 kB
006 What is and why use TensorFlow_en.srt - 12 kB
012 Creating tensors with TensorFlow and tf.Variable()_en.srt - 10.1 kB
022 Changing the datatype of tensors_en.srt - 8.85 kB
009 How to approach this course_en.srt - 8.44 kB
027 One-hot encoding tensors_en.srt - 8.17 kB
008 What we're going to cover throughout the course_en.srt - 7.41 kB
029 Exploring TensorFlow and NumPy's compatibility_en.srt - 7.29 kB
018 Manipulating tensors with basic operations_en.srt - 7.12 kB
001 What is deep learning_en.srt - 7.1 kB
024 Tensor troubleshooting example (updating tensor datatypes)_en.srt - 6.79 kB
028 Trying out more tensor math operations_en.srt - 6.38 kB
007 What is a Tensor_en.srt - 5.12 kB
026 Squeezing a tensor (removing all 1-dimension axes)_en.srt - 3.93 kB
031 TensorFlow Fundamentals challenge, exercises & extra-curriculum.html - 1.99 kB
032 Monthly Coding Challenges, Free Resources and Guides.html - 1.62 kB
033 LinkedIn Endorsements.html - 1.4 kB
010 Need A Refresher.html - 942 B
004 Python + Machine Learning Monthly.html - 796 B
external-links.txt - 94 B
001 All-course-materials-and-links-.url - 77 B
007 Batching and preparing our datasets (to make them run fast).mp4 - 140 MB
006 Creating a preprocessing function to prepare our data for modelling.mp4 - 139 MB
005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4 - 122 MB
011 Turning on mixed precision training with TensorFlow.mp4 - 115 MB
012 Creating a feature extraction model capable of using mixed precision training.mp4 - 114 MB
004 Introduction to TensorFlow Datasets (TFDS).mp4 - 104 MB
015 Introducing your Milestone Project 1 challenge build a model to beat DeepFood.mp4 - 96 MB
013 Checking to see if our model is using mixed precision training layer by layer.mp4 - 93.5 MB
002 Making sure we have access to the right GPU for mixed precision training.mp4 - 92.1 MB
014 Training and evaluating a feature extraction model (Food Vision Big™).mp4 - 80.6 MB
009 Creating modelling callbacks for our feature extraction model.mp4 - 63.3 MB
008 Exploring what happens when we batch and prefetch our data.mp4 - 58.4 MB
003 Getting helper functions ready.mp4 - 27.7 MB
001 Introduction to Milestone Project 1 Food Vision Big™.mp4 - 17.1 MB
005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets)_en.srt - 22.9 kB
007 Batching and preparing our datasets (to make them run fast)_en.srt - 19.7 kB
006 Creating a preprocessing function to prepare our data for modelling_en.srt - 19.3 kB
004 Introduction to TensorFlow Datasets (TFDS)_en.srt - 18 kB
012 Creating a feature extraction model capable of using mixed precision training_en.srt - 17.8 kB
014 Training and evaluating a feature extraction model (Food Vision Big™)_en.srt - 14.5 kB
002 Making sure we have access to the right GPU for mixed precision training_en.srt - 14.4 kB
011 Turning on mixed precision training with TensorFlow_en.srt - 14.2 kB
015 Introducing your Milestone Project 1 challenge build a model to beat DeepFood_en.srt - 11.5 kB
013 Checking to see if our model is using mixed precision training layer by layer_en.srt - 10.5 kB
009 Creating modelling callbacks for our feature extraction model_en.srt - 10.1 kB
008 Exploring what happens when we batch and prefetch our data_en.srt - 9.64 kB
001 Introduction to Milestone Project 1 Food Vision Big™_en.srt - 9.4 kB
003 Getting helper functions ready_en.srt - 4.04 kB
016 Milestone Project 1 Food Vision Big™, exercises and extra-curriculum.html - 2.36 kB
010 Note Mixed Precision producing errors for TensorFlow 2.5+.html - 1.96 kB
0. Websites you may like
[FreeCourseSite.com].url - 127 B
[CourseClub.Me].url - 122 B
[GigaCourse.Com].url - 49 B
009 Selecting and Viewing Data with Pandas Part 2.mp4 - 112 MB
010 Manipulating Data.mp4 - 110 MB
005 Series, Data Frames and CSVs.mp4 - 99.2 MB
011 Manipulating Data 2.mp4 - 91.1 MB
012 Manipulating Data 3.mp4 - 82.8 MB
014 How To Download The Course Assignments.mp4 - 70.8 MB
007 Describing Data with Pandas.mp4 - 68.1 MB
008 Selecting and Viewing Data with Pandas.mp4 - 55.8 MB
004 Pandas Introduction.mp4 - 11.9 MB
002 Section Overview.mp4 - 5.52 MB
005 pandas-anatomy-of-a-dataframe.png - 341 kB
011 pandas-anatomy-of-a-dataframe.png - 341 kB
009 Selecting and Viewing Data with Pandas Part 2_en.srt - 19.4 kB
010 Manipulating Data_en.srt - 19 kB
005 Series, Data Frames and CSVs_en.srt - 18.9 kB
008 Selecting and Viewing Data with Pandas_en.srt - 15.6 kB
011 Manipulating Data 2_en.srt - 15.2 kB
007 Describing Data with Pandas_en.srt - 14.6 kB
012 Manipulating Data 3_en.srt - 14.3 kB
014 How To Download The Course Assignments_en.srt - 11.5 kB
004 Pandas Introduction_en.srt - 7.08 kB
002 Section Overview_en.srt - 3.78 kB
013 Assignment Pandas Practice.html - 2.1 kB
006 Data from URLs.html - 1.11 kB
external-links.txt - 1.04 kB
003 Downloading Workbooks and Assignments.html - 967 B
001 Quick Note Upcoming Videos.html - 706 B
008 car-sales.csv - 369 B
010 car-sales-missing-data.csv - 287 B
004 Intro-to-pandas-code.url - 154 B
012 Pandas-video-code.url - 154 B
004 Intro-to-pandas-notes.url - 148 B
012 Pandas-video-notes.url - 148 B
010 https-jakevdp.github.io-PythonDataScienceHandbook-03.00-introduction-to-pandas.html.url - 109 B
004 10-Minutes-to-pandas.url - 90 B
014 Course-Notes.url - 71 B
014 https-colab.research.google.com-.url - 58 B
014 Exercise Nut Butter Store Sales.mp4 - 94.9 MB
017 Turn Images Into NumPy Arrays.mp4 - 92.4 MB
013 Dot Product vs Element Wise.mp4 - 75.7 MB
009 Manipulating Arrays.mp4 - 73.8 MB
005 NumPy DataTypes and Attributes.mp4 - 72.5 MB
010 Manipulating Arrays 2.mp4 - 70.2 MB
008 Viewing Arrays and Matrices.mp4 - 64.1 MB
006 Creating NumPy Arrays.mp4 - 61.2 MB
012 Reshape and Transpose.mp4 - 56.1 MB
011 Standard Deviation and Variance.mp4 - 39.6 MB
007 NumPy Random Seed.mp4 - 39.1 MB
016 Sorting Arrays.mp4 - 26.4 MB
015 Comparison Operators.mp4 - 23.7 MB
003 NumPy Introduction.mp4 - 14.7 MB
002 Section Overview.mp4 - 13.5 MB
017 numpy-images.zip - 7.62 MB
005 NumPy DataTypes and Attributes_en.srt - 20.5 kB
014 Exercise Nut Butter Store Sales_en.srt - 17.8 kB
009 Manipulating Arrays_en.srt - 17.6 kB
013 Dot Product vs Element Wise_en.srt - 16.3 kB
008 Viewing Arrays and Matrices_en.srt - 14.2 kB
006 Creating NumPy Arrays_en.srt - 12.8 kB
010 Manipulating Arrays 2_en.srt - 12.3 kB
017 Turn Images Into NumPy Arrays_en.srt - 10.9 kB
007 NumPy Random Seed_en.srt - 10.7 kB
011 Standard Deviation and Variance_en.srt - 10.1 kB
012 Reshape and Transpose_en.srt - 9.91 kB
016 Sorting Arrays_en.srt - 9.16 kB
003 NumPy Introduction_en.srt - 7.79 kB
015 Comparison Operators_en.srt - 5.35 kB
002 Section Overview_en.srt - 3.32 kB
018 Assignment NumPy Practice.html - 2.22 kB
004 Quick Note Correction In Next Video.html - 1.27 kB
external-links.txt - 1.13 kB
019 Optional Extra NumPy resources.html - 1.05 kB
001 Quick Note Upcoming Videos.html - 706 B
003 NumPy-Video-code.url - 153 B
017 NumPy-Video-code.url - 153 B
003 NumPy-Notes.url - 147 B
017 Section-Notes.url - 147 B
013 https-www.mathsisfun.com-algebra-matrix-multiplying.html.url - 82 B
009 https-www.mathsisfun.com-data-standard-deviation.html.url - 79 B
010 https-www.mathsisfun.com-data-standard-deviation.html.url - 79 B
011 https-www.mathsisfun.com-data-standard-deviation.html.url - 79 B
003 https-numpy.org-doc-.url - 46 B
005 Types of Machine Learning Problems.mp4 - 27.9 MB
006 Types of Data.mp4 - 21.6 MB
012 Modelling - Comparison.mp4 - 19.6 MB
008 Features In Data.mp4 - 18.7 MB
009 Modelling - Splitting Data.mp4 - 14.4 MB
015 Tools We Will Use.mp4 - 13.9 MB
014 Experimentation.mp4 - 12.5 MB
004 6 Step Machine Learning Framework.mp4 - 10.9 MB
010 Modelling - Picking the Model.mp4 - 9.36 MB
007 Types of Evaluation.mp4 - 7 MB
002 Section Overview.mp4 - 6.88 MB
011 Modelling - Tuning.mp4 - 6.62 MB
003 Introducing Our Framework.mp4 - 4.62 MB
005 Types of Machine Learning Problems_en.srt - 14.8 kB
012 Modelling - Comparison_en.srt - 13.6 kB
009 Modelling - Splitting Data_en.srt - 7.98 kB
008 Features In Data_en.srt - 7.04 kB
004 6 Step Machine Learning Framework_en.srt - 7.03 kB
006 Types of Data_en.srt - 6.64 kB
010 Modelling - Picking the Model_en.srt - 6.39 kB
015 Tools We Will Use_en.srt - 6.22 kB
011 Modelling - Tuning_en.srt - 5.21 kB
014 Experimentation_en.srt - 5.21 kB
002 Section Overview_en.srt - 4.91 kB
007 Types of Evaluation_en.srt - 4.67 kB
003 Introducing Our Framework_en.srt - 3.79 kB
013 Overfitting and Underfitting Definitions.html - 1.99 kB
016 Optional Elements of AI.html - 975 B
001 Quick Note Upcoming Videos.html - 706 B
0. Websites you may like
[FreeCourseSite.com].url - 127 B
[CourseClub.Me].url - 122 B
[GigaCourse.Com].url - 49 B
004 6-Step-Guide.url - 110 B
external-links.txt - 108 B
0
0
Seeders
Leechers
File Size
Downloads
Uploaded
Torrent Download
Magnet Download
