Neural Networks with TensorFlow

Neural Networks with TensorFlow

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1. Basics of Keras

This segment introduces the fundamentals of Keras through hands-on experiences with the Spotify dataset, covering everything from layer configuration and model creation to compilation, data preprocessing, and training.

Model Compilation

Data Preprocessing

Model Training and Evaluation

Model Save and Load

Early Stopping and Checkpoints

Hyperparameter Tuning

2. Regularization

The regularization module, utilizing the Mushrooms dataset, addresses the critical issues of overfitting and underfitting, explaining the concept of regularization and its importance in model training.

Overfitting and Underfitting

What is Regularization?

L1, L2 Regularization

Batch Normalization

3. Advanced Techniques

Exploring cutting-edge methodologies, this section delves into the Diamonds dataset to uncover the intricacies of optimizers, learning rate adjustments, TensorFlow Datasets, data generators, and advanced modeling techniques like non-sequential models, transfer learning, and multitask learning in a comprehensive overview.

Learning Rate Scheduling

TensorFlow Datasets

Data Generators

Non-Sequential Models

Transfer Learning

Multitask Learning

Зміст курсу

Neural Networks with TensorFlow

Neural Networks with TensorFlow

Data Preprocessing

Now, let's explore setting up a model and apply it to a practical scenario. We'll aim to predict house prices using the well-known Boston Housing Price Regression Dataset.

Data Overview

First, we need to examine the data before loading it.

Note
For an in-depth description of the dataset, you can visit this link: Boston Housing price regression dataset.

Features

Below is a list of all the columns in the dataset:

Ethical Consideration: The dataset includes a variable "B," which implies a correlation between race and house prices. We will exclude this column in the preprocessing stage to avoid racial bias in our model.

Note
To understand more about ethical considerations, check out our Ethical Considerations in Deep Learning article.

Missing Values

We need to verify if there are any missing values in the dataset. This requires first loading the dataset from Keras and then evaluating for missing values.


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import tensorflow.keras as keras
import pandas as pd

# Load the dataset
(X_train, y_train), (X_test, y_test) = keras.datasets.boston_housing.load_data()

# Convert each subset to Pandas DataFrame
X_train = pd.DataFrame(X_train)
y_train = pd.DataFrame(y_train)

# Sum up number of empty values of each set
print('Null values in X_Train:', X_train.isnull().sum().sum())
print('Null values in y_train:', y_train.isnull().sum().sum())

As it turns out, there are no empty values, so we don't need to address this issue.

Data Preprocessing

Outliers: Although Keras datasets are typically free of outliers, we will demonstrate outlier removal using IsolationForest, eliminating 5% of the data as outliers.
Note
- IsolationForest's predict method returns a list indicating valid samples (1) or outliers (-1).
- To set up contamination rate you can set up contamination parameter of the IsolationForest constructor.
- Outliers should be removed only from the training set.
Rescaling: To ensure consistency and compatibility with our model, the data needs to be rescaled.

Завдання

Throughout this course, you'll engage extensively in data handling independently. Therefore, let's revisit how to preprocess data using the scikit-learn library, making it ready for use in a neural network developed with Keras.

Initialize an Isolation Forest with a 5% contamination rate.
Apply the Isolation Forest to the training set and discard outliers.
Rescale the data using MinMaxScaler.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 1. Розділ 4

toggle bottom row

Data Preprocessing

Now, let's explore setting up a model and apply it to a practical scenario. We'll aim to predict house prices using the well-known Boston Housing Price Regression Dataset.

Data Overview

First, we need to examine the data before loading it.

Note
For an in-depth description of the dataset, you can visit this link: Boston Housing price regression dataset.

Features

Below is a list of all the columns in the dataset:

Ethical Consideration: The dataset includes a variable "B," which implies a correlation between race and house prices. We will exclude this column in the preprocessing stage to avoid racial bias in our model.

Note
To understand more about ethical considerations, check out our Ethical Considerations in Deep Learning article.

Missing Values

We need to verify if there are any missing values in the dataset. This requires first loading the dataset from Keras and then evaluating for missing values.


              12345678910111213
            
import tensorflow.keras as keras
import pandas as pd

# Load the dataset
(X_train, y_train), (X_test, y_test) = keras.datasets.boston_housing.load_data()

# Convert each subset to Pandas DataFrame
X_train = pd.DataFrame(X_train)
y_train = pd.DataFrame(y_train)

# Sum up number of empty values of each set
print('Null values in X_Train:', X_train.isnull().sum().sum())
print('Null values in y_train:', y_train.isnull().sum().sum())

As it turns out, there are no empty values, so we don't need to address this issue.

Data Preprocessing

Outliers: Although Keras datasets are typically free of outliers, we will demonstrate outlier removal using IsolationForest, eliminating 5% of the data as outliers.
Note
- IsolationForest's predict method returns a list indicating valid samples (1) or outliers (-1).
- To set up contamination rate you can set up contamination parameter of the IsolationForest constructor.
- Outliers should be removed only from the training set.
Rescaling: To ensure consistency and compatibility with our model, the data needs to be rescaled.

Завдання

Throughout this course, you'll engage extensively in data handling independently. Therefore, let's revisit how to preprocess data using the scikit-learn library, making it ready for use in a neural network developed with Keras.

Initialize an Isolation Forest with a 5% contamination rate.
Apply the Isolation Forest to the training set and discard outliers.
Rescale the data using MinMaxScaler.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 1. Розділ 4

toggle bottom row

Data Preprocessing

Now, let's explore setting up a model and apply it to a practical scenario. We'll aim to predict house prices using the well-known Boston Housing Price Regression Dataset.

Data Overview

First, we need to examine the data before loading it.

Note
For an in-depth description of the dataset, you can visit this link: Boston Housing price regression dataset.

Features

Below is a list of all the columns in the dataset:

Ethical Consideration: The dataset includes a variable "B," which implies a correlation between race and house prices. We will exclude this column in the preprocessing stage to avoid racial bias in our model.

Note
To understand more about ethical considerations, check out our Ethical Considerations in Deep Learning article.

Missing Values

We need to verify if there are any missing values in the dataset. This requires first loading the dataset from Keras and then evaluating for missing values.


              12345678910111213
            
import tensorflow.keras as keras
import pandas as pd

# Load the dataset
(X_train, y_train), (X_test, y_test) = keras.datasets.boston_housing.load_data()

# Convert each subset to Pandas DataFrame
X_train = pd.DataFrame(X_train)
y_train = pd.DataFrame(y_train)

# Sum up number of empty values of each set
print('Null values in X_Train:', X_train.isnull().sum().sum())
print('Null values in y_train:', y_train.isnull().sum().sum())

As it turns out, there are no empty values, so we don't need to address this issue.

Data Preprocessing

Outliers: Although Keras datasets are typically free of outliers, we will demonstrate outlier removal using IsolationForest, eliminating 5% of the data as outliers.
Note
- IsolationForest's predict method returns a list indicating valid samples (1) or outliers (-1).
- To set up contamination rate you can set up contamination parameter of the IsolationForest constructor.
- Outliers should be removed only from the training set.
Rescaling: To ensure consistency and compatibility with our model, the data needs to be rescaled.

Завдання

Throughout this course, you'll engage extensively in data handling independently. Therefore, let's revisit how to preprocess data using the scikit-learn library, making it ready for use in a neural network developed with Keras.

Initialize an Isolation Forest with a 5% contamination rate.
Apply the Isolation Forest to the training set and discard outliers.
Rescale the data using MinMaxScaler.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Now, let's explore setting up a model and apply it to a practical scenario. We'll aim to predict house prices using the well-known Boston Housing Price Regression Dataset.

Data Overview

First, we need to examine the data before loading it.

Note
For an in-depth description of the dataset, you can visit this link: Boston Housing price regression dataset.

Features

Below is a list of all the columns in the dataset:

Ethical Consideration: The dataset includes a variable "B," which implies a correlation between race and house prices. We will exclude this column in the preprocessing stage to avoid racial bias in our model.

Note
To understand more about ethical considerations, check out our Ethical Considerations in Deep Learning article.

Missing Values

We need to verify if there are any missing values in the dataset. This requires first loading the dataset from Keras and then evaluating for missing values.


              12345678910111213
            
import tensorflow.keras as keras
import pandas as pd

# Load the dataset
(X_train, y_train), (X_test, y_test) = keras.datasets.boston_housing.load_data()

# Convert each subset to Pandas DataFrame
X_train = pd.DataFrame(X_train)
y_train = pd.DataFrame(y_train)

# Sum up number of empty values of each set
print('Null values in X_Train:', X_train.isnull().sum().sum())
print('Null values in y_train:', y_train.isnull().sum().sum())

As it turns out, there are no empty values, so we don't need to address this issue.

Data Preprocessing

Outliers: Although Keras datasets are typically free of outliers, we will demonstrate outlier removal using IsolationForest, eliminating 5% of the data as outliers.
Note
- IsolationForest's predict method returns a list indicating valid samples (1) or outliers (-1).
- To set up contamination rate you can set up contamination parameter of the IsolationForest constructor.
- Outliers should be removed only from the training set.
Rescaling: To ensure consistency and compatibility with our model, the data needs to be rescaled.

Завдання

Throughout this course, you'll engage extensively in data handling independently. Therefore, let's revisit how to preprocess data using the scikit-learn library, making it ready for use in a neural network developed with Keras.

Initialize an Isolation Forest with a 5% contamination rate.
Apply the Isolation Forest to the training set and discard outliers.
Rescale the data using MinMaxScaler.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Секція 1. Розділ 4

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

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