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| 001 Become An Alumni.html |
1.79Кб |
| 001 Course Outline.en.srt |
8.28Кб |
| 001 Course Outline.mp4 |
58.03Мб |
| 001 Introduction to Computer Vision with TensorFlow.en.srt |
15.59Кб |
| 001 Introduction to Computer Vision with TensorFlow.mp4 |
75.00Мб |
| 001 Introduction to Milestone Project 1_ Food Vision Big™.en.srt |
9.53Кб |
| 001 Introduction to Milestone Project 1_ Food Vision Big™.mp4 |
42.31Мб |
| 001 Introduction to Milestone Project 2_ SkimLit.en.srt |
22.88Кб |
| 001 Introduction to Milestone Project 2_ SkimLit.mp4 |
148.38Мб |
| 001 Introduction to neural network classification in TensorFlow.en.srt |
13.27Кб |
| 001 Introduction to neural network classification in TensorFlow.mp4 |
72.81Мб |
| 001 Introduction to Neural Network Regression with TensorFlow.en.srt |
11.85Кб |
| 001 Introduction to Neural Network Regression with TensorFlow.mp4 |
60.06Мб |
| 001 Introduction to Transfer Learning in TensorFlow Part 2_ Fine-tuning.en.srt |
10.16Кб |
| 001 Introduction to Transfer Learning in TensorFlow Part 2_ Fine-tuning.mp4 |
61.46Мб |
| 001 Introduction to Transfer Learning Part 3_ Scaling Up.en.srt |
10.51Кб |
| 001 Introduction to Transfer Learning Part 3_ Scaling Up.mp4 |
41.52Мб |
| 001 More Videos Coming Soon!.html |
940б |
| 001 More Videos Coming Soon!.html |
940б |
| 001 More Videos Coming Soon!.html |
940б |
| 001 Quick Note_ Upcoming Videos.html |
1.57Кб |
| 001 Quick Note_ Upcoming Videos.html |
1.57Кб |
| 001 Quick Note_ Upcoming Videos.html |
1.57Кб |
| 001 Quick Note_ Upcoming Videos.html |
1.57Кб |
| 001 Special Bonus Lecture.html |
4.91Кб |
| 001 Welcome to natural language processing with TensorFlow!.html |
1.96Кб |
| 001 What is and why use transfer learning_.en.srt |
16.57Кб |
| 001 What is and why use transfer learning_.mp4 |
65.81Мб |
| 001 What is deep learning_.en.srt |
7.07Кб |
| 001 What is deep learning_.mp4 |
34.17Мб |
| 002 Downloading and preparing data for our first transfer learning model.en.srt |
18.85Кб |
| 002 Downloading and preparing data for our first transfer learning model.mp4 |
132.67Мб |
| 002 Example classification problems (and their inputs and outputs).en.srt |
10.30Кб |
| 002 Example classification problems (and their inputs and outputs).mp4 |
50.71Мб |
| 002 Getting helper functions ready and downloading data to model.en.srt |
18.47Кб |
| 002 Getting helper functions ready and downloading data to model.mp4 |
131.54Мб |
| 002 Importing a script full of helper functions (and saving lots of space).en.srt |
10.17Кб |
| 002 Importing a script full of helper functions (and saving lots of space).mp4 |
89.38Мб |
| 002 Inputs and outputs of a neural network regression model.en.srt |
13.63Кб |
| 002 Inputs and outputs of a neural network regression model.mp4 |
57.57Мб |
| 002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.en.srt |
12.59Кб |
| 002 Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.mp4 |
76.65Мб |
| 002 Introduction to Natural Language Processing (NLP) and Sequence Problems.en.srt |
21.08Кб |
| 002 Introduction to Natural Language Processing (NLP) and Sequence Problems.mp4 |
124.03Мб |
| 002 Join Our Online Classroom!.html |
3.31Кб |
| 002 LinkedIn Endorsements.html |
2.93Кб |
| 002 Making sure we have access to the right GPU for mixed precision training.en.srt |
14.63Кб |
| 002 Making sure we have access to the right GPU for mixed precision training.mp4 |
88.15Мб |
| 002 Section Overview.en.srt |
4.98Кб |
| 002 Section Overview.en.srt |
3.84Кб |
| 002 Section Overview.en.srt |
3.36Кб |
| 002 Section Overview.mp4 |
13.35Мб |
| 002 Section Overview.mp4 |
13.33Мб |
| 002 Section Overview.mp4 |
10.87Мб |
| 002 What is Machine Learning_.en.srt |
9.31Кб |
| 002 What is Machine Learning_.mp4 |
28.31Мб |
| 002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts).en.srt |
12.36Кб |
| 002 What we're going to cover in Milestone Project 2 (NLP for medical abstracts).mp4 |
71.01Мб |
| 002 Why use deep learning_.en.srt |
14.73Кб |
| 002 Why use deep learning_.mp4 |
62.32Мб |
| 003 AI_Machine Learning_Data Science.en.srt |
6.69Кб |
| 003 AI_Machine Learning_Data Science.mp4 |
19.67Мб |
| 003 Anatomy and architecture of a neural network regression model.en.srt |
12.74Кб |
| 003 Anatomy and architecture of a neural network regression model.mp4 |
59.00Мб |
| 003 Downloading and turning our images into a TensorFlow BatchDataset.en.srt |
22.98Кб |
| 003 Downloading and turning our images into a TensorFlow BatchDataset.mp4 |
173.59Мб |
| 003 Downloading an image dataset for our first Food Vision model.en.srt |
10.74Кб |
| 003 Downloading an image dataset for our first Food Vision model.mp4 |
72.93Мб |
| 003 Downloading Workbooks and Assignments.html |
1.83Кб |
| 003 Example NLP inputs and outputs.en.srt |
12.14Кб |
| 003 Example NLP inputs and outputs.mp4 |
64.27Мб |
| 003 Exercise_ Meet The Community.html |
3.71Кб |
| 003 Getting helper functions ready.en.srt |
4.09Кб |
| 003 Getting helper functions ready.mp4 |
31.09Мб |
| 003 Input and output tensors of classification problems.en.srt |
9.18Кб |
| 003 Input and output tensors of classification problems.mp4 |
51.01Мб |
| 003 Introducing Callbacks in TensorFlow and making a callback to track our models.en.srt |
14.87Кб |
| 003 Introducing Callbacks in TensorFlow and making a callback to track our models.mp4 |
94.89Мб |
| 003 Introducing Our Framework.en.srt |
3.83Кб |
| 003 Introducing Our Framework.mp4 |
11.39Мб |
| 003 NumPy Introduction.en.srt |
7.89Кб |
| 003 NumPy Introduction.mp4 |
26.86Мб |
| 003 Outlining the model we're going to build and building a ModelCheckpoint callback.en.srt |
7.70Кб |
| 003 Outlining the model we're going to build and building a ModelCheckpoint callback.mp4 |
40.61Мб |
| 003 SkimLit inputs and outputs.en.srt |
18.80Кб |
| 003 SkimLit inputs and outputs.mp4 |
76.78Мб |
| 003 TensorFlow Certificate.html |
1.25Кб |
| 003 What are neural networks_.en.srt |
15.27Кб |
| 003 What are neural networks_.mp4 |
63.43Мб |
| 004 6 Step Machine Learning Framework.en.srt |
7.12Кб |
| 004 6 Step Machine Learning Framework.mp4 |
23.45Мб |
| 004 All Course Resources + Notebooks.html |
2.86Кб |
| 004 Becoming One With Data.en.srt |
7.04Кб |
| 004 Becoming One With Data.mp4 |
45.61Мб |
| 004 Creating a data augmentation layer to use with our model.en.srt |
6.49Кб |
| 004 Creating a data augmentation layer to use with our model.mp4 |
40.56Мб |
| 004 Creating sample regression data (so we can model it).en.srt |
16.81Кб |
| 004 Creating sample regression data (so we can model it).mp4 |
90.16Мб |
| 004 Discussing the four (actually five) modelling experiments we're running.en.srt |
3.72Кб |
| 004 Discussing the four (actually five) modelling experiments we're running.mp4 |
15.87Мб |
| 004 Exercise_ Machine Learning Playground.en.srt |
8.46Кб |
| 004 Exercise_ Machine Learning Playground.mp4 |
42.56Мб |
| 004 Exploring the TensorFlow Hub website for pretrained models.en.srt |
15.33Кб |
| 004 Exploring the TensorFlow Hub website for pretrained models.mp4 |
102.96Мб |
| 004 Introduction to TensorFlow Datasets (TFDS).en.srt |
18.35Кб |
| 004 Introduction to TensorFlow Datasets (TFDS).mp4 |
116.84Мб |
| 004 Pandas Introduction.en.srt |
7.19Кб |
| 004 Pandas Introduction.mp4 |
27.46Мб |
| 004 Quick Note_ Correction In Next Video.html |
2.52Кб |
| 004 Setting up our notebook for Milestone Project 2 (getting the data).en.srt |
20.50Кб |
| 004 Setting up our notebook for Milestone Project 2 (getting the data).mp4 |
146.03Мб |
| 004 The typical architecture of a Recurrent Neural Network (RNN).en.srt |
13.96Кб |
| 004 The typical architecture of a Recurrent Neural Network (RNN).mp4 |
107.16Мб |
| 004 Typical architecture of neural network classification models with TensorFlow.en.srt |
15.21Кб |
| 004 Typical architecture of neural network classification models with TensorFlow.mp4 |
112.73Мб |
| 004 What is deep learning already being used for_.en.srt |
14.02Кб |
| 004 What is deep learning already being used for_.mp4 |
76.21Мб |
| 005 Becoming One With Data Part 2.en.srt |
16.74Кб |
| 005 Becoming One With Data Part 2.mp4 |
104.58Мб |
| 005 Building and compiling a TensorFlow Hub feature extraction model.en.srt |
19.74Кб |
| 005 Building and compiling a TensorFlow Hub feature extraction model.mp4 |
135.62Мб |
| 005 Comparing the TensorFlow Keras Sequential API versus the Functional API.en.srt |
4.20Кб |
| 005 Comparing the TensorFlow Keras Sequential API versus the Functional API.mp4 |
26.45Мб |
| 005 Creating a headless EfficientNetB0 model with data augmentation built in.en.srt |
14.02Кб |
| 005 Creating a headless EfficientNetB0 model with data augmentation built in.mp4 |
80.41Мб |
| 005 Creating and viewing classification data to model.en.srt |
15.00Кб |
| 005 Creating and viewing classification data to model.mp4 |
106.08Мб |
| 005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets).en.srt |
23.29Кб |
| 005 Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4 |
116.71Мб |
| 005 How Did We Get Here_.en.srt |
7.61Кб |
| 005 How Did We Get Here_.mp4 |
30.49Мб |
| 005 NumPy DataTypes and Attributes.en.srt |
20.87Кб |
| 005 NumPy DataTypes and Attributes.mp4 |
78.97Мб |
| 005 Preparing a notebook for our first NLP with TensorFlow project.en.srt |
12.20Кб |
| 005 Preparing a notebook for our first NLP with TensorFlow project.mp4 |
82.41Мб |
| 005 Series, Data Frames and CSVs.en.srt |
19.22Кб |
| 005 Series, Data Frames and CSVs.mp4 |
95.43Мб |
| 005 The major steps in modelling with TensorFlow.en.srt |
26.85Кб |
| 005 The major steps in modelling with TensorFlow.mp4 |
181.81Мб |
| 005 Types of Machine Learning Problems.en.srt |
14.97Кб |
| 005 Types of Machine Learning Problems.mp4 |
60.46Мб |
| 005 Visualising examples from the dataset (becoming one with the data).en.srt |
17.84Кб |
| 005 Visualising examples from the dataset (becoming one with the data).mp4 |
132.24Мб |
| 005 What is and why use TensorFlow_.en.srt |
12.19Кб |
| 005 What is and why use TensorFlow_.mp4 |
69.16Мб |
| 006 Becoming One With Data Part 3.en.srt |
6.81Кб |
| 006 Becoming One With Data Part 3.mp4 |
39.89Мб |
| 006 Becoming one with the data and visualising a text dataset.en.srt |
23.12Кб |
| 006 Becoming one with the data and visualising a text dataset.mp4 |
160.31Мб |
| 006 Blowing our previous models out of the water with transfer learning.en.srt |
14.28Кб |
| 006 Blowing our previous models out of the water with transfer learning.mp4 |
99.45Мб |
| 006 Checking the input and output shapes of our classification data.en.srt |
6.85Кб |
| 006 Checking the input and output shapes of our classification data.mp4 |
38.14Мб |
| 006 Creating a preprocessing function to prepare our data for modelling.en.srt |
19.63Кб |
| 006 Creating a preprocessing function to prepare our data for modelling.mp4 |
132.19Мб |
| 006 Creating NumPy Arrays.en.srt |
13.01Кб |
| 006 Creating NumPy Arrays.mp4 |
66.84Мб |
| 006 Creating our first model with the TensorFlow Keras Functional API.en.srt |
16.51Кб |
| 006 Creating our first model with the TensorFlow Keras Functional API.mp4 |
132.18Мб |
| 006 Data from URLs.html |
2.35Кб |
| 006 Exercise_ YouTube Recommendation Engine.en.srt |
5.85Кб |
| 006 Exercise_ YouTube Recommendation Engine.mp4 |
19.43Мб |
| 006 Fitting and evaluating our biggest transfer learning model yet.en.srt |
11.94Кб |
| 006 Fitting and evaluating our biggest transfer learning model yet.mp4 |
70.15Мб |
| 006 Steps in improving a model with TensorFlow part 1.en.srt |
7.93Кб |
| 006 Steps in improving a model with TensorFlow part 1.mp4 |
45.82Мб |
| 006 Types of Data.en.srt |
6.71Кб |
| 006 Types of Data.mp4 |
29.31Мб |
| 006 What is a Tensor_.en.srt |
5.19Кб |
| 006 What is a Tensor_.mp4 |
27.58Мб |
| 006 Writing a preprocessing function to structure our data for modelling.en.srt |
27.05Кб |
| 006 Writing a preprocessing function to structure our data for modelling.mp4 |
218.07Мб |
| 007 Batching and preparing our datasets (to make them run fast).en.srt |
19.98Кб |
| 007 Batching and preparing our datasets (to make them run fast).mp4 |
132.24Мб |
| 007 Building an end to end CNN Model.en.srt |
27.06Кб |
| 007 Building an end to end CNN Model.mp4 |
155.08Мб |
| 007 Building a not very good classification model with TensorFlow.en.srt |
16.72Кб |
| 007 Building a not very good classification model with TensorFlow.mp4 |
125.29Мб |
| 007 Compiling and fitting our first Functional API model.en.srt |
16.46Кб |
| 007 Compiling and fitting our first Functional API model.mp4 |
132.84Мб |
| 007 Describing Data with Pandas.en.srt |
14.83Кб |
| 007 Describing Data with Pandas.mp4 |
75.65Мб |
| 007 NumPy Random Seed.en.srt |
10.91Кб |
| 007 NumPy Random Seed.mp4 |
51.94Мб |
| 007 Performing visual data analysis on our preprocessed text.en.srt |
11.34Кб |
| 007 Performing visual data analysis on our preprocessed text.mp4 |
74.22Мб |
| 007 Plotting the loss curves of our ResNet feature extraction model.en.srt |
11.27Кб |
| 007 Plotting the loss curves of our ResNet feature extraction model.mp4 |
62.09Мб |
| 007 Splitting data into training and validation sets.en.srt |
8.17Кб |
| 007 Splitting data into training and validation sets.mp4 |
59.87Мб |
| 007 Steps in improving a model with TensorFlow part 2.en.srt |
13.67Кб |
| 007 Steps in improving a model with TensorFlow part 2.mp4 |
90.23Мб |
| 007 Types of Evaluation.en.srt |
4.73Кб |
| 007 Types of Evaluation.mp4 |
17.74Мб |
| 007 Types of Machine Learning.en.srt |
5.71Кб |
| 007 Types of Machine Learning.mp4 |
22.81Мб |
| 007 Unfreezing some layers in our base model to prepare for fine-tuning.en.srt |
17.30Кб |
| 007 Unfreezing some layers in our base model to prepare for fine-tuning.mp4 |
100.07Мб |
| 007 What we're going to cover throughout the course.en.srt |
7.52Кб |
| 007 What we're going to cover throughout the course.mp4 |
29.38Мб |
| 008 Are You Getting It Yet_.html |
1.03Кб |
| 008 Building and training a pre-trained EfficientNet model on our data.en.srt |
14.87Кб |
| 008 Building and training a pre-trained EfficientNet model on our data.mp4 |
105.92Мб |
| 008 Converting text data to numbers using tokenisation and embeddings (overview).en.srt |
13.60Кб |
| 008 Converting text data to numbers using tokenisation and embeddings (overview).mp4 |
82.30Мб |
| 008 Exploring what happens when we batch and prefetch our data.en.srt |
9.76Кб |
| 008 Exploring what happens when we batch and prefetch our data.mp4 |
63.82Мб |
| 008 Features In Data.en.srt |
7.12Кб |
| 008 Features In Data.mp4 |
36.77Мб |
| 008 Fine-tuning our feature extraction model and evaluating its performance.en.srt |
12.36Кб |
| 008 Fine-tuning our feature extraction model and evaluating its performance.mp4 |
66.23Мб |
| 008 Getting a feature vector from our trained model.en.srt |
18.47Кб |
| 008 Getting a feature vector from our trained model.mp4 |
147.62Мб |
| 008 How to approach this course.en.srt |
8.57Кб |
| 008 How to approach this course.mp4 |
26.17Мб |
| 008 Selecting and Viewing Data with Pandas.en.srt |
15.89Кб |
| 008 Selecting and Viewing Data with Pandas.mp4 |
72.29Мб |
| 008 Steps in improving a model with TensorFlow part 3.en.srt |
17.53Кб |
| 008 Steps in improving a model with TensorFlow part 3.mp4 |
132.94Мб |
| 008 Trying to improve our not very good classification model.en.srt |
13.20Кб |
| 008 Trying to improve our not very good classification model.mp4 |
84.29Мб |
| 008 Turning our target labels into numbers (ML models require numbers).en.srt |
19.64Кб |
| 008 Turning our target labels into numbers (ML models require numbers).mp4 |
117.40Мб |
| 008 Using a GPU to run our CNN model 5x faster.en.srt |
13.60Кб |
| 008 Using a GPU to run our CNN model 5x faster.mp4 |
114.94Мб |
| 008 Viewing Arrays and Matrices.en.srt |
14.49Кб |
| 008 Viewing Arrays and Matrices.mp4 |
70.65Мб |
| 009 Creating a function to view our model's not so good predictions.en.srt |
19.77Кб |
| 009 Creating a function to view our model's not so good predictions.mp4 |
160.55Мб |
| 009 Creating modelling callbacks for our feature extraction model.en.srt |
10.23Кб |
| 009 Creating modelling callbacks for our feature extraction model.mp4 |
60.79Мб |
| 009 Different Types of Transfer Learning.en.srt |
16.31Кб |
| 009 Different Types of Transfer Learning.mp4 |
110.57Мб |
| 009 Drilling into the concept of a feature vector (a learned representation).en.srt |
5.60Кб |
| 009 Drilling into the concept of a feature vector (a learned representation).mp4 |
51.50Мб |
| 009 Evaluating a TensorFlow model part 1 (_visualise, visualise, visualise_).en.srt |
10.20Кб |
| 009 Evaluating a TensorFlow model part 1 (_visualise, visualise, visualise_).mp4 |
66.94Мб |
| 009 Manipulating Arrays.en.srt |
17.91Кб |
| 009 Manipulating Arrays.mp4 |
80.66Мб |
| 009 Model 0_ Creating, fitting and evaluating a baseline model for SkimLit.en.srt |
11.97Кб |
| 009 Model 0_ Creating, fitting and evaluating a baseline model for SkimLit.mp4 |
81.63Мб |
| 009 Modelling - Splitting Data.en.srt |
8.08Кб |
| 009 Modelling - Splitting Data.mp4 |
27.55Мб |
| 009 Need A Refresher_.html |
1.79Кб |
| 009 Saving and loading our trained model.en.srt |
9.36Кб |
| 009 Saving and loading our trained model.mp4 |
57.40Мб |
| 009 Selecting and Viewing Data with Pandas Part 2.en.srt |
19.75Кб |
| 009 Selecting and Viewing Data with Pandas Part 2.mp4 |
106.49Мб |
| 009 Setting up a TensorFlow TextVectorization layer to convert text to numbers.en.srt |
23.08Кб |
| 009 Setting up a TensorFlow TextVectorization layer to convert text to numbers.mp4 |
199.93Мб |
| 009 Trying a non-CNN model on our image data.en.srt |
12.13Кб |
| 009 Trying a non-CNN model on our image data.mp4 |
100.55Мб |
| 009 What Is Machine Learning_ Round 2.en.srt |
6.48Кб |
| 009 What Is Machine Learning_ Round 2.mp4 |
25.51Мб |
| 010 Comparing Our Model's Results.en.srt |
22.44Кб |
| 010 Comparing Our Model's Results.mp4 |
143.93Мб |
| 010 Creating your first tensors with TensorFlow and tf.constant().en.srt |
25.80Кб |
| 010 Creating your first tensors with TensorFlow and tf.constant().mp4 |
134.83Мб |
| 010 Downloading and preparing the data for Model 1 (1 percent of training data).en.srt |
13.52Кб |
| 010 Downloading and preparing the data for Model 1 (1 percent of training data).mp4 |
97.80Мб |
| 010 Downloading a pretrained model to make and evaluate predictions with.en.srt |
9.27Кб |
| 010 Downloading a pretrained model to make and evaluate predictions with.mp4 |
78.69Мб |
| 010 Evaluating a TensorFlow model part 2 (the three datasets).en.srt |
14.63Кб |
| 010 Evaluating a TensorFlow model part 2 (the three datasets).mp4 |
81.56Мб |
| 010 Improving our non-CNN model by adding more layers.en.srt |
14.56Кб |
| 010 Improving our non-CNN model by adding more layers.mp4 |
106.47Мб |
| 010 Make our poor classification model work for a regression dataset.en.srt |
17.02Кб |
| 010 Make our poor classification model work for a regression dataset.mp4 |
123.01Мб |
| 010 Manipulating Arrays 2.en.srt |
12.53Кб |
| 010 Manipulating Arrays 2.mp4 |
67.91Мб |
| 010 Manipulating Data.en.srt |
19.33Кб |
| 010 Manipulating Data.mp4 |
104.99Мб |
| 010 Mapping the TextVectorization layer to text data and turning it into numbers.en.srt |
16.55Кб |
| 010 Mapping the TextVectorization layer to text data and turning it into numbers.mp4 |
97.91Мб |
| 010 Modelling - Picking the Model.en.srt |
6.45Кб |
| 010 Modelling - Picking the Model.mp4 |
23.24Мб |
| 010 Preparing our data for deep sequence models.en.srt |
13.52Кб |
| 010 Preparing our data for deep sequence models.mp4 |
85.15Мб |
| 010 Section Review.en.srt |
2.28Кб |
| 010 Section Review.mp4 |
5.55Мб |
| 010 Turning on mixed precision training with TensorFlow.en.srt |
14.46Кб |
| 010 Turning on mixed precision training with TensorFlow.mp4 |
107.71Мб |
| 011 Breaking our CNN model down part 1_ Becoming one with the data.en.srt |
13.51Кб |
| 011 Breaking our CNN model down part 1_ Becoming one with the data.mp4 |
90.92Мб |
| 011 Building a data augmentation layer to use inside our model.en.srt |
16.83Кб |
| 011 Building a data augmentation layer to use inside our model.mp4 |
117.46Мб |
| 011 Creating a feature extraction model capable of using mixed precision training.en.srt |
18.12Кб |
| 011 Creating a feature extraction model capable of using mixed precision training.mp4 |
107.92Мб |
| 011 Creating an Embedding layer to turn tokenised text into embedding vectors.en.srt |
18.64Кб |
| 011 Creating an Embedding layer to turn tokenised text into embedding vectors.mp4 |
135.65Мб |
| 011 Creating a text vectoriser to map our tokens (text) to numbers.en.srt |
19.81Кб |
| 011 Creating a text vectoriser to map our tokens (text) to numbers.mp4 |
129.78Мб |
| 011 Creating tensors with TensorFlow and tf.Variable().en.srt |
10.36Кб |
| 011 Creating tensors with TensorFlow and tf.Variable().mp4 |
70.85Мб |
| 011 Evaluating a TensorFlow model part 3 (getting a model summary).en.srt |
22.43Кб |
| 011 Evaluating a TensorFlow model part 3 (getting a model summary).mp4 |
192.79Мб |
| 011 Making predictions with our trained model on 25,250 test samples.en.srt |
16.93Кб |
| 011 Making predictions with our trained model on 25,250 test samples.mp4 |
115.59Мб |
| 011 Manipulating Data 2.en.srt |
15.49Кб |
| 011 Manipulating Data 2.mp4 |
86.56Мб |
| 011 Modelling - Tuning.en.srt |
5.28Кб |
| 011 Modelling - Tuning.mp4 |
15.98Мб |
| 011 Non-linearity part 1_ Straight lines and non-straight lines.en.srt |
14.39Кб |
| 011 Non-linearity part 1_ Straight lines and non-straight lines.mp4 |
95.61Мб |
| 011 Standard Deviation and Variance.en.srt |
10.25Кб |
| 011 Standard Deviation and Variance.mp4 |
51.13Мб |
| 011 TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum.html |
3.37Кб |
| 012 Breaking our CNN model down part 2_ Preparing to load our data.en.srt |
17.18Кб |
| 012 Breaking our CNN model down part 2_ Preparing to load our data.mp4 |
109.47Мб |
| 012 Checking to see if our model is using mixed precision training layer by layer.en.srt |
10.71Кб |
| 012 Checking to see if our model is using mixed precision training layer by layer.mp4 |
87.67Мб |
| 012 Creating a custom token embedding layer with TensorFlow.en.srt |
13.15Кб |
| 012 Creating a custom token embedding layer with TensorFlow.mp4 |
99.51Мб |
| 012 Creating random tensors with TensorFlow.en.srt |
13.58Кб |
| 012 Creating random tensors with TensorFlow.mp4 |
88.45Мб |
| 012 Discussing the various modelling experiments we're going to run.en.srt |
14.33Кб |
| 012 Discussing the various modelling experiments we're going to run.mp4 |
87.60Мб |
| 012 Evaluating a TensorFlow model part 4 (visualising a model's layers).en.srt |
9.61Кб |
| 012 Evaluating a TensorFlow model part 4 (visualising a model's layers).mp4 |
70.28Мб |
| 012 Manipulating Data 3.en.srt |
14.58Кб |
| 012 Manipulating Data 3.mp4 |
91.07Мб |
| 012 Modelling - Comparison.en.srt |
13.81Кб |
| 012 Modelling - Comparison.mp4 |
44.86Мб |
| 012 Non-linearity part 2_ Building our first neural network with non-linearity.en.srt |
7.88Кб |
| 012 Non-linearity part 2_ Building our first neural network with non-linearity.mp4 |
59.00Мб |
| 012 Reshape and Transpose.en.srt |
10.10Кб |
| 012 Reshape and Transpose.mp4 |
53.57Мб |
| 012 Unravelling our test dataset for comparing ground truth labels to predictions.en.srt |
8.02Кб |
| 012 Unravelling our test dataset for comparing ground truth labels to predictions.mp4 |
43.81Мб |
| 012 Visualising what happens when images pass through our data augmentation layer.en.srt |
15.06Кб |
| 012 Visualising what happens when images pass through our data augmentation layer.mp4 |
119.36Мб |
| 013 Assignment_ Pandas Practice.html |
2.93Кб |
| 013 Breaking our CNN model down part 3_ Loading our data with ImageDataGenerator.en.srt |
14.01Кб |
| 013 Breaking our CNN model down part 3_ Loading our data with ImageDataGenerator.mp4 |
103.42Мб |
| 013 Building Model 1 (with a data augmentation layer and 1% of training data).en.srt |
23.38Кб |
| 013 Building Model 1 (with a data augmentation layer and 1% of training data).mp4 |
152.95Мб |
| 013 Confirming our model's predictions are in the same order as the test labels.en.srt |
7.05Кб |
| 013 Confirming our model's predictions are in the same order as the test labels.mp4 |
50.54Мб |
| 013 Creating fast loading dataset with the TensorFlow tf.data API.en.srt |
13.32Кб |
| 013 Creating fast loading dataset with the TensorFlow tf.data API.mp4 |
90.64Мб |
| 013 Dot Product vs Element Wise.en.srt |
16.58Кб |
| 013 Dot Product vs Element Wise.mp4 |
83.80Мб |
| 013 Evaluating a TensorFlow model part 5 (visualising a model's predictions).en.srt |
12.42Кб |
| 013 Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp4 |
78.87Мб |
| 013 Model 0_ Building a baseline model to try and improve upon.en.srt |
13.14Кб |
| 013 Model 0_ Building a baseline model to try and improve upon.mp4 |
93.18Мб |
| 013 Non-linearity part 3_ Upgrading our non-linear model with more layers.en.srt |
14.98Кб |
| 013 Non-linearity part 3_ Upgrading our non-linear model with more layers.mp4 |
123.24Мб |
| 013 Overfitting and Underfitting Definitions.html |
2.87Кб |
| 013 Shuffling the order of tensors.en.srt |
13.19Кб |
| 013 Shuffling the order of tensors.mp4 |
89.86Мб |
| 013 Training and evaluating a feature extraction model (Food Vision Big™).en.srt |
14.67Кб |
| 013 Training and evaluating a feature extraction model (Food Vision Big™).mp4 |
89.02Мб |
| 014 Breaking our CNN model down part 4_ Building a baseline CNN model.en.srt |
11.70Кб |
| 014 Breaking our CNN model down part 4_ Building a baseline CNN model.mp4 |
85.30Мб |
| 014 Building Model 2 (with a data augmentation layer and 10% of training data).en.srt |
24.42Кб |
| 014 Building Model 2 (with a data augmentation layer and 10% of training data).mp4 |
159.77Мб |
| 014 Creating a confusion matrix for our model's 101 different classes.en.srt |
18.34Кб |
| 014 Creating a confusion matrix for our model's 101 different classes.mp4 |
156.60Мб |
| 014 Creating a function to track and evaluate our model's results.en.srt |
17.37Кб |
| 014 Creating a function to track and evaluate our model's results.mp4 |
148.65Мб |
| 014 Creating tensors from NumPy arrays.en.srt |
15.69Кб |
| 014 Creating tensors from NumPy arrays.mp4 |
101.33Мб |
| 014 Evaluating a TensorFlow model part 6 (common regression evaluation metrics).en.srt |
11.60Кб |
| 014 Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp4 |
70.37Мб |
| 014 Exercise_ Nut Butter Store Sales.en.srt |
18.19Кб |
| 014 Exercise_ Nut Butter Store Sales.mp4 |
91.26Мб |
| 014 Experimentation.en.srt |
5.29Кб |
| 014 Experimentation.mp4 |
21.29Мб |
| 014 How To Download The Course Assignments.en.srt |
11.70Кб |
| 014 How To Download The Course Assignments.mp4 |
66.79Мб |
| 014 Introducing your Milestone Project 1 challenge_ build a model to beat DeepFood.en.srt |
11.70Кб |
| 014 Introducing your Milestone Project 1 challenge_ build a model to beat DeepFood.mp4 |
89.12Мб |
| 014 Model 1_ Building, fitting and evaluating a Conv1D with token embeddings.en.srt |
25.65Кб |
| 014 Model 1_ Building, fitting and evaluating a Conv1D with token embeddings.mp4 |
168.42Мб |
| 014 Non-linearity part 4_ Modelling our non-linear data once and for all.en.srt |
12.52Кб |
| 014 Non-linearity part 4_ Modelling our non-linear data once and for all.mp4 |
96.62Мб |
| 015 Breaking our CNN model down part 5_ Looking inside a Conv2D layer.en.srt |
23.74Кб |
| 015 Breaking our CNN model down part 5_ Looking inside a Conv2D layer.mp4 |
186.03Мб |
| 015 Comparison Operators.en.srt |
5.47Кб |
| 015 Comparison Operators.mp4 |
26.37Мб |
| 015 Creating a ModelCheckpoint to save our model's weights during training.en.srt |
11.21Кб |
| 015 Creating a ModelCheckpoint to save our model's weights during training.mp4 |
68.98Мб |
| 015 Evaluating a TensorFlow regression model part 7 (mean absolute error).en.srt |
8.49Кб |
| 015 Evaluating a TensorFlow regression model part 7 (mean absolute error).mp4 |
56.09Мб |
| 015 Evaluating every individual class in our dataset.en.srt |
20.16Кб |
| 015 Evaluating every individual class in our dataset.mp4 |
131.77Мб |
| 015 Getting information from your tensors (tensor attributes).en.srt |
17.69Кб |
| 015 Getting information from your tensors (tensor attributes).mp4 |
87.38Мб |
| 015 Milestone Project 1_ Food Vision Big™, exercises and extra-curriculum.html |
3.24Кб |
| 015 Model 1_ Building, fitting and evaluating our first deep model on text data.en.srt |
29.85Кб |
| 015 Model 1_ Building, fitting and evaluating our first deep model on text data.mp4 |
207.74Мб |
| 015 Non-linearity part 5_ Replicating non-linear activation functions from scratch.en.srt |
19.08Кб |
| 015 Non-linearity part 5_ Replicating non-linear activation functions from scratch.mp4 |
146.61Мб |
| 015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2.en.srt |
15.62Кб |
| 015 Preparing a pretrained embedding layer from TensorFlow Hub for Model 2.mp4 |
124.68Мб |
| 015 Tools We Will Use.en.srt |
6.31Кб |
| 015 Tools We Will Use.mp4 |
27.34Мб |
| 016 Breaking our CNN model down part 6_ Compiling and fitting our baseline CNN.en.srt |
10.29Кб |
| 016 Breaking our CNN model down part 6_ Compiling and fitting our baseline CNN.mp4 |
77.08Мб |
| 016 Evaluating a TensorFlow regression model part 7 (mean square error).en.srt |
4.06Кб |
| 016 Evaluating a TensorFlow regression model part 7 (mean square error).mp4 |
32.31Мб |
| 016 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).en.srt |
10.27Кб |
| 016 Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp4 |
68.15Мб |
| 016 Getting great results in less time by tweaking the learning rate.en.srt |
20.18Кб |
| 016 Getting great results in less time by tweaking the learning rate.mp4 |
136.77Мб |
| 016 Indexing and expanding tensors.en.srt |
17.66Кб |
| 016 Indexing and expanding tensors.mp4 |
86.56Мб |
| 016 Model 2_ Building, fitting and evaluating a Conv1D model with token embeddings.en.srt |
16.79Кб |
| 016 Model 2_ Building, fitting and evaluating a Conv1D model with token embeddings.mp4 |
106.95Мб |
| 016 Optional_ Elements of AI.html |
1.83Кб |
| 016 Plotting our model's F1-scores for each separate class.en.srt |
11.19Кб |
| 016 Plotting our model's F1-scores for each separate class.mp4 |
77.93Мб |
| 016 Sorting Arrays.en.srt |
9.35Кб |
| 016 Sorting Arrays.mp4 |
32.82Мб |
| 016 Visualising our model's learned word embeddings with TensorFlow's projector tool.en.srt |
30.95Кб |
| 016 Visualising our model's learned word embeddings with TensorFlow's projector tool.mp4 |
283.21Мб |
| 017 Breaking our CNN model down part 7_ Evaluating our CNN's training curves.en.srt |
17.81Кб |
| 017 Breaking our CNN model down part 7_ Evaluating our CNN's training curves.mp4 |
106.20Мб |
| 017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer.en.srt |
31.05Кб |
| 017 Creating a character-level tokeniser with TensorFlow's TextVectorization layer.mp4 |
197.66Мб |
| 017 Creating a function to load and prepare images for making predictions.en.srt |
16.43Кб |
| 017 Creating a function to load and prepare images for making predictions.mp4 |
109.54Мб |
| 017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more.en.srt |
14.34Кб |
| 017 High-level overview of Recurrent Neural Networks (RNNs) + where to learn more.mp4 |
96.64Мб |
| 017 Loading and comparing saved weights to our existing trained Model 2.en.srt |
10.06Кб |
| 017 Loading and comparing saved weights to our existing trained Model 2.mp4 |
62.67Мб |
| 017 Manipulating tensors with basic operations.en.srt |
7.24Кб |
| 017 Manipulating tensors with basic operations.mp4 |
45.22Мб |
| 017 Setting up TensorFlow modelling experiments part 1 (start with a simple model).en.srt |
18.17Кб |
| 017 Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4 |
127.25Мб |
| 017 Turn Images Into NumPy Arrays.en.srt |
11.05Кб |
| 017 Turn Images Into NumPy Arrays.mp4 |
85.98Мб |
| 017 Using the TensorFlow History object to plot a model's loss curves.en.srt |
8.72Кб |
| 017 Using the TensorFlow History object to plot a model's loss curves.mp4 |
62.12Мб |
| 018 Assignment_ NumPy Practice.html |
3.05Кб |
| 018 Breaking our CNN model down part 8_ Reducing overfitting with Max Pooling.en.srt |
20.02Кб |
| 018 Breaking our CNN model down part 8_ Reducing overfitting with Max Pooling.mp4 |
130.43Мб |
| 018 Creating a character-level embedding layer with tf.keras.layers.Embedding.en.srt |
10.86Кб |
| 018 Creating a character-level embedding layer with tf.keras.layers.Embedding.mp4 |
77.52Мб |
| 018 Making predictions on our test images and evaluating them.en.srt |
24.55Кб |
| 018 Making predictions on our test images and evaluating them.mp4 |
171.68Мб |
| 018 Matrix multiplication with tensors part 1.en.srt |
15.91Кб |
| 018 Matrix multiplication with tensors part 1.mp4 |
100.85Мб |
| 018 Model 2_ Building, fitting and evaluating our first TensorFlow RNN model (LSTM).en.srt |
25.65Кб |
| 018 Model 2_ Building, fitting and evaluating our first TensorFlow RNN model (LSTM).mp4 |
165.78Мб |
| 018 Preparing Model 3 (our first fine-tuned model).en.srt |
26.97Кб |
| 018 Preparing Model 3 (our first fine-tuned model).mp4 |
198.23Мб |
| 018 Setting up TensorFlow modelling experiments part 2 (increasing complexity).en.srt |
16.55Кб |
| 018 Setting up TensorFlow modelling experiments part 2 (increasing complexity).mp4 |
95.62Мб |
| 018 Using callbacks to find a model's ideal learning rate.en.srt |
25.94Кб |
| 018 Using callbacks to find a model's ideal learning rate.mp4 |
155.88Мб |
| 019 Breaking our CNN model down part 9_ Reducing overfitting with data augmentation.en.srt |
9.77Кб |
| 019 Breaking our CNN model down part 9_ Reducing overfitting with data augmentation.mp4 |
66.08Мб |
| 019 Comparing and tracking your TensorFlow modelling experiments.en.srt |
13.70Кб |
| 019 Comparing and tracking your TensorFlow modelling experiments.mp4 |
92.25Мб |
| 019 Discussing the benefits of finding your model's most wrong predictions.en.srt |
9.80Кб |
| 019 Discussing the benefits of finding your model's most wrong predictions.mp4 |
59.29Мб |
| 019 Fitting and evaluating Model 3 (our first fine-tuned model).en.srt |
11.06Кб |
| 019 Fitting and evaluating Model 3 (our first fine-tuned model).mp4 |
69.16Мб |
| 019 Matrix multiplication with tensors part 2.en.srt |
18.10Кб |
| 019 Matrix multiplication with tensors part 2.mp4 |
107.79Мб |
| 019 Model 3_ Building, fitting and evaluating a Conv1D model on character embeddings.en.srt |
19.80Кб |
| 019 Model 3_ Building, fitting and evaluating a Conv1D model on character embeddings.mp4 |
131.07Мб |
| 019 Model 3_ Building, fitting and evaluating a GRU-cell powered RNN.en.srt |
24.87Кб |
| 019 Model 3_ Building, fitting and evaluating a GRU-cell powered RNN.mp4 |
168.10Мб |
| 019 Optional_ Extra NumPy resources.html |
1.91Кб |
| 019 Training and evaluating a model with an ideal learning rate.en.srt |
12.39Кб |
| 019 Training and evaluating a model with an ideal learning rate.mp4 |
89.00Мб |
| 020 Breaking our CNN model down part 10_ Visualizing our augmented data.en.srt |
22.45Кб |
| 020 Breaking our CNN model down part 10_ Visualizing our augmented data.mp4 |
157.61Мб |
| 020 Comparing our model's results before and after fine-tuning.en.srt |
14.45Кб |
| 020 Comparing our model's results before and after fine-tuning.mp4 |
84.17Мб |
| 020 Discussing how we're going to build Model 4 (character + token embeddings).en.srt |
9.08Кб |
| 020 Discussing how we're going to build Model 4 (character + token embeddings).mp4 |
60.31Мб |
| 020 How to save a TensorFlow model.en.srt |
11.88Кб |
| 020 How to save a TensorFlow model.mp4 |
92.29Мб |
| 020 Introducing more classification evaluation methods.en.srt |
9.21Кб |
| 020 Introducing more classification evaluation methods.mp4 |
42.21Мб |
| 020 Matrix multiplication with tensors part 3.en.srt |
13.84Кб |
| 020 Matrix multiplication with tensors part 3.mp4 |
80.62Мб |
| 020 Model 4_ Building, fitting and evaluating a bidirectional RNN model.en.srt |
28.30Кб |
| 020 Model 4_ Building, fitting and evaluating a bidirectional RNN model.mp4 |
167.29Мб |
| 020 Writing code to uncover our model's most wrong predictions.en.srt |
17.79Кб |
| 020 Writing code to uncover our model's most wrong predictions.mp4 |
109.59Мб |
| 021 Breaking our CNN model down part 11_ Training a CNN model on augmented data.en.srt |
14.17Кб |
| 021 Breaking our CNN model down part 11_ Training a CNN model on augmented data.mp4 |
94.06Мб |
| 021 Changing the datatype of tensors.en.srt |
9.00Кб |
| 021 Changing the datatype of tensors.mp4 |
71.39Мб |
| 021 Discussing the intuition behind Conv1D neural networks for text and sequences.en.srt |
28.08Кб |
| 021 Discussing the intuition behind Conv1D neural networks for text and sequences.mp4 |
184.39Мб |
| 021 Downloading and preparing data for our biggest experiment yet (Model 4).en.srt |
9.34Кб |
| 021 Downloading and preparing data for our biggest experiment yet (Model 4).mp4 |
56.68Мб |
| 021 Finding the accuracy of our classification model.en.srt |
5.86Кб |
| 021 Finding the accuracy of our classification model.mp4 |
34.07Мб |
| 021 How to load and use a saved TensorFlow model.en.srt |
13.35Кб |
| 021 How to load and use a saved TensorFlow model.mp4 |
104.36Мб |
| 021 Model 4_ Building a multi-input model (hybrid token + character embeddings).en.srt |
23.53Кб |
| 021 Model 4_ Building a multi-input model (hybrid token + character embeddings).mp4 |
181.85Мб |
| 021 Plotting and visualising the samples our model got most wrong.en.srt |
16.14Кб |
| 021 Plotting and visualising the samples our model got most wrong.mp4 |
125.49Мб |
| 022 (Optional) How to save and download files from Google Colab.en.srt |
8.10Кб |
| 022 (Optional) How to save and download files from Google Colab.mp4 |
67.70Мб |
| 022 Breaking our CNN model down part 12_ Discovering the power of shuffling data.en.srt |
14.88Кб |
| 022 Breaking our CNN model down part 12_ Discovering the power of shuffling data.mp4 |
103.86Мб |
| 022 Creating our first confusion matrix (to see where our model is getting confused).en.srt |
12.04Кб |
| 022 Creating our first confusion matrix (to see where our model is getting confused).mp4 |
65.70Мб |
| 022 Making predictions on and plotting our own custom images.en.srt |
15.23Кб |
| 022 Making predictions on and plotting our own custom images.mp4 |
108.30Мб |
| 022 Model 4_ Plotting and visually exploring different data inputs.en.srt |
12.83Кб |
| 022 Model 4_ Plotting and visually exploring different data inputs.mp4 |
86.56Мб |
| 022 Model 5_ Building, fitting and evaluating a 1D CNN for text.en.srt |
15.45Кб |
| 022 Model 5_ Building, fitting and evaluating a 1D CNN for text.mp4 |
77.75Мб |
| 022 Preparing our final modelling experiment (Model 4).en.srt |
15.53Кб |
| 022 Preparing our final modelling experiment (Model 4).mp4 |
96.42Мб |
| 022 Tensor aggregation (finding the min, max, mean & more).en.srt |
13.43Кб |
| 022 Tensor aggregation (finding the min, max, mean & more).mp4 |
89.58Мб |
| 023 Breaking our CNN model down part 13_ Exploring options to improve our model.en.srt |
7.82Кб |
| 023 Breaking our CNN model down part 13_ Exploring options to improve our model.mp4 |
50.34Мб |
| 023 Crafting multi-input fast loading tf.data datasets for Model 4.en.srt |
11.32Кб |
| 023 Crafting multi-input fast loading tf.data datasets for Model 4.mp4 |
83.83Мб |
| 023 Fine-tuning Model 4 on 100% of the training data and evaluating its results.en.srt |
15.54Кб |
| 023 Fine-tuning Model 4 on 100% of the training data and evaluating its results.mp4 |
96.84Мб |
| 023 Making our confusion matrix prettier.en.srt |
19.08Кб |
| 023 Making our confusion matrix prettier.mp4 |
114.11Мб |
| 023 Putting together what we've learned part 1 (preparing a dataset).en.srt |
19.51Кб |
| 023 Putting together what we've learned part 1 (preparing a dataset).mp4 |
143.51Мб |
| 023 Tensor troubleshooting example (updating tensor datatypes).en.srt |
6.93Кб |
| 023 Tensor troubleshooting example (updating tensor datatypes).mp4 |
69.39Мб |
| 023 Transfer Learning in TensorFlow Part 3 challenge, exercises and extra-curriculum.html |
3.21Кб |
| 023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP).en.srt |
20.25Кб |
| 023 Using TensorFlow Hub for pretrained word embeddings (transfer learning for NLP).mp4 |
138.06Мб |
| 024 Comparing our modelling experiment results in TensorBoard.en.srt |
16.43Кб |
| 024 Comparing our modelling experiment results in TensorBoard.mp4 |
95.75Мб |
| 024 Downloading a custom image to make predictions on.en.srt |
7.25Кб |
| 024 Downloading a custom image to make predictions on.mp4 |
53.08Мб |
| 024 Finding the positional minimum and maximum of a tensor (argmin and argmax).en.srt |
12.92Кб |
| 024 Finding the positional minimum and maximum of a tensor (argmin and argmax).mp4 |
96.50Мб |
| 024 Model 4_ Building, fitting and evaluating a hybrid embedding model.en.srt |
19.35Кб |
| 024 Model 4_ Building, fitting and evaluating a hybrid embedding model.mp4 |
139.22Мб |
| 024 Model 6_ Building, training and evaluating a transfer learning model for NLP.en.srt |
15.71Кб |
| 024 Model 6_ Building, training and evaluating a transfer learning model for NLP.mp4 |
99.03Мб |
| 024 Putting things together with multi-class classification part 1_ Getting the data.en.srt |
14.33Кб |
| 024 Putting things together with multi-class classification part 1_ Getting the data.mp4 |
87.22Мб |
| 024 Putting together what we've learned part 2 (building a regression model).en.srt |
18.76Кб |
| 024 Putting together what we've learned part 2 (building a regression model).mp4 |
121.37Мб |
| 025 How to view and delete previous TensorBoard experiments.en.srt |
2.93Кб |
| 025 How to view and delete previous TensorBoard experiments.mp4 |
21.91Мб |
| 025 Model 5_ Adding positional embeddings via feature engineering (overview).en.srt |
10.57Кб |
| 025 Model 5_ Adding positional embeddings via feature engineering (overview).mp4 |
66.23Мб |
| 025 Multi-class classification part 2_ Becoming one with the data.en.srt |
10.40Кб |
| 025 Multi-class classification part 2_ Becoming one with the data.mp4 |
48.65Мб |
| 025 Preparing subsets of data for model 7 (same as model 6 but 10% of data).en.srt |
15.93Кб |
| 025 Preparing subsets of data for model 7 (same as model 6 but 10% of data).mp4 |
91.64Мб |
| 025 Putting together what we've learned part 3 (improving our regression model).en.srt |
19.63Кб |
| 025 Putting together what we've learned part 3 (improving our regression model).mp4 |
155.11Мб |
| 025 Squeezing a tensor (removing all 1-dimension axes).en.srt |
4.01Кб |
| 025 Squeezing a tensor (removing all 1-dimension axes).mp4 |
30.20Мб |
| 025 Writing a helper function to load and preprocessing custom images.en.srt |
14.34Кб |
| 025 Writing a helper function to load and preprocessing custom images.mp4 |
105.15Мб |
| 026 Encoding the line number feature to used with Model 5.en.srt |
17.32Кб |
| 026 Encoding the line number feature to used with Model 5.mp4 |
113.03Мб |
| 026 Making a prediction on a custom image with our trained CNN.en.srt |
16.14Кб |
| 026 Making a prediction on a custom image with our trained CNN.mp4 |
99.90Мб |
| 026 Model 7_ Building, training and evaluating a transfer learning model on 10% data.en.srt |
13.43Кб |
| 026 Model 7_ Building, training and evaluating a transfer learning model on 10% data.mp4 |
100.71Мб |
| 026 Multi-class classification part 3_ Building a multi-class classification model.en.srt |
22.06Кб |
| 026 Multi-class classification part 3_ Building a multi-class classification model.mp4 |
142.80Мб |
| 026 One-hot encoding tensors.en.srt |
8.35Кб |
| 026 One-hot encoding tensors.mp4 |
59.72Мб |
| 026 Preprocessing data with feature scaling part 1 (what is feature scaling_).en.srt |
14.48Кб |
| 026 Preprocessing data with feature scaling part 1 (what is feature scaling_).mp4 |
92.51Мб |
| 026 Transfer Learning in TensorFlow Part 2 challenge, exercises and extra-curriculum.html |
3.57Кб |
| 027 Encoding the total lines feature to be used with Model 5.en.srt |
10.58Кб |
| 027 Encoding the total lines feature to be used with Model 5.mp4 |
64.28Мб |
| 027 Fixing our data leakage issue with model 7 and retraining it.en.srt |
18.01Кб |
| 027 Fixing our data leakage issue with model 7 and retraining it.mp4 |
165.94Мб |
| 027 Multi-class classification part 4_ Improving performance with normalisation.en.srt |
16.89Кб |
| 027 Multi-class classification part 4_ Improving performance with normalisation.mp4 |
113.41Мб |
| 027 Multi-class CNN's part 1_ Becoming one with the data.en.srt |
23.68Кб |
| 027 Multi-class CNN's part 1_ Becoming one with the data.mp4 |
140.19Мб |
| 027 Preprocessing data with feature scaling part 2 (normalising our data).en.srt |
14.54Кб |
| 027 Preprocessing data with feature scaling part 2 (normalising our data).mp4 |
97.18Мб |
| 027 Trying out more tensor math operations.en.srt |
6.53Кб |
| 027 Trying out more tensor math operations.mp4 |
55.93Мб |
| 028 Comparing all our modelling experiments evaluation metrics.en.srt |
18.60Кб |
| 028 Comparing all our modelling experiments evaluation metrics.mp4 |
115.92Мб |
| 028 Exploring TensorFlow and NumPy's compatibility.en.srt |
7.41Кб |
| 028 Exploring TensorFlow and NumPy's compatibility.mp4 |
43.74Мб |
| 028 Model 5_ Building the foundations of a tribrid embedding model.en.srt |
11.92Кб |
| 028 Model 5_ Building the foundations of a tribrid embedding model.mp4 |
81.89Мб |
| 028 Multi-class classification part 5_ Comparing normalised and non-normalised data.en.srt |
5.66Кб |
| 028 Multi-class classification part 5_ Comparing normalised and non-normalised data.mp4 |
26.77Мб |
| 028 Multi-class CNN's part 2_ Preparing our data (turning it into tensors).en.srt |
10.40Кб |
| 028 Multi-class CNN's part 2_ Preparing our data (turning it into tensors).mp4 |
72.71Мб |
| 028 Preprocessing data with feature scaling part 3 (fitting a model on scaled data).en.srt |
11.45Кб |
| 028 Preprocessing data with feature scaling part 3 (fitting a model on scaled data).mp4 |
75.72Мб |
| 029 Making sure our tensor operations run really fast on GPUs.en.srt |
15.05Кб |
| 029 Making sure our tensor operations run really fast on GPUs.mp4 |
110.90Мб |
| 029 Model 5_ Completing the build of a tribrid embedding model for sequences.en.srt |
18.94Кб |
| 029 Model 5_ Completing the build of a tribrid embedding model for sequences.mp4 |
152.91Мб |
| 029 Multi-class classification part 6_ Finding the ideal learning rate.en.srt |
15.57Кб |
| 029 Multi-class classification part 6_ Finding the ideal learning rate.mp4 |
73.33Мб |
| 029 Multi-class CNN's part 3_ Building a multi-class CNN model.en.srt |
11.14Кб |
| 029 Multi-class CNN's part 3_ Building a multi-class CNN model.mp4 |
89.24Мб |
| 029 TensorFlow Regression challenge, exercises & extra-curriculum.html |
2.89Кб |
| 029 Uploading our model's training logs to TensorBoard and comparing them.en.srt |
15.95Кб |
| 029 Uploading our model's training logs to TensorBoard and comparing them.mp4 |
109.34Мб |
| 030 Multi-class classification part 7_ Evaluating our model.en.srt |
17.69Кб |
| 030 Multi-class classification part 7_ Evaluating our model.mp4 |
119.14Мб |
| 030 Multi-class CNN's part 4_ Fitting a multi-class CNN model to the data.en.srt |
9.34Кб |
| 030 Multi-class CNN's part 4_ Fitting a multi-class CNN model to the data.mp4 |
59.66Мб |
| 030 Saving and loading in a trained NLP model with TensorFlow.en.srt |
14.07Кб |
| 030 Saving and loading in a trained NLP model with TensorFlow.mp4 |
104.88Мб |
| 030 TensorFlow Fundamentals challenge, exercises & extra-curriculum.html |
2.86Кб |
| 030 Visually inspecting the architecture of our tribrid embedding model.en.srt |
14.38Кб |
| 030 Visually inspecting the architecture of our tribrid embedding model.mp4 |
107.80Мб |
| 031 Creating multi-level data input pipelines for Model 5 with the tf.data API.en.srt |
11.17Кб |
| 031 Creating multi-level data input pipelines for Model 5 with the tf.data API.mp4 |
99.16Мб |
| 031 Downloading a pretrained model and preparing data to investigate predictions.en.srt |
17.18Кб |
| 031 Downloading a pretrained model and preparing data to investigate predictions.mp4 |
131.00Мб |
| 031 Multi-class classification part 8_ Creating a confusion matrix.en.srt |
6.95Кб |
| 031 Multi-class classification part 8_ Creating a confusion matrix.mp4 |
40.52Мб |
| 031 Multi-class CNN's part 5_ Evaluating our multi-class CNN model.en.srt |
7.07Кб |
| 031 Multi-class CNN's part 5_ Evaluating our multi-class CNN model.mp4 |
41.04Мб |
| 031 Python + Machine Learning Monthly.html |
1.66Кб |
| 032 Bringing SkimLit to life!!! (fitting and evaluating Model 5).en.srt |
15.49Кб |
| 032 Bringing SkimLit to life!!! (fitting and evaluating Model 5).mp4 |
115.78Мб |
| 032 LinkedIn Endorsements.html |
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| 032 Multi-class classification part 9_ Visualising random model predictions.en.srt |
14.12Кб |
| 032 Multi-class classification part 9_ Visualising random model predictions.mp4 |
65.68Мб |
| 032 Multi-class CNN's part 6_ Trying to fix overfitting by removing layers.en.srt |
17.13Кб |
| 032 Multi-class CNN's part 6_ Trying to fix overfitting by removing layers.mp4 |
129.83Мб |
| 032 Visualising our model's most wrong predictions.en.srt |
12.80Кб |
| 032 Visualising our model's most wrong predictions.mp4 |
77.07Мб |
| 033 Comparing the performance of all of our modelling experiments.en.srt |
12.89Кб |
| 033 Comparing the performance of all of our modelling experiments.mp4 |
77.95Мб |
| 033 Making and visualising predictions on the test dataset.en.srt |
12.14Кб |
| 033 Making and visualising predictions on the test dataset.mp4 |
76.72Мб |
| 033 Multi-class CNN's part 7_ Trying to fix overfitting with data augmentation.en.srt |
17.04Кб |
| 033 Multi-class CNN's part 7_ Trying to fix overfitting with data augmentation.mp4 |
121.02Мб |
| 033 What _patterns_ is our model learning_.en.srt |
21.67Кб |
| 033 What _patterns_ is our model learning_.mp4 |
127.95Мб |
| 034 Multi-class CNN's part 8_ Things you could do to improve your CNN model.en.srt |
6.43Кб |
| 034 Multi-class CNN's part 8_ Things you could do to improve your CNN model.mp4 |
43.29Мб |
| 034 Saving, loading & testing our best performing model.en.srt |
10.42Кб |
| 034 Saving, loading & testing our best performing model.mp4 |
83.63Мб |
| 034 TensorFlow classification challenge, exercises & extra-curriculum.html |
3.40Кб |
| 034 Understanding the concept of the speed_score tradeoff.en.srt |
19.39Кб |
| 034 Understanding the concept of the speed_score tradeoff.mp4 |
130.63Мб |
| 035 Congratulations and your challenge before heading to the next module.en.srt |
17.88Кб |
| 035 Congratulations and your challenge before heading to the next module.mp4 |
135.69Мб |
| 035 Multi-class CNN's part 9_ Making predictions with our model on custom images.en.srt |
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| 035 Multi-class CNN's part 9_ Making predictions with our model on custom images.mp4 |
118.98Мб |
| 035 NLP Fundamentals in TensorFlow challenge, exercises and extra-curriculum.html |
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| 036 Milestone Project 2 (SkimLit) challenge, exercises and extra-curriculum.html |
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| 036 Saving and loading our trained CNN model.en.srt |
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| 036 Saving and loading our trained CNN model.mp4 |
69.28Мб |
| 037 TensorFlow computer vision and CNNs challenge, exercises & extra-curriculum.html |
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