Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Overfitting, Underfitting, and Generalization: Generalization
In this video, we will cover generalization. This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will cover...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Dropout
In this video, we will cover DNN Dropout. This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will cover...
Curated Video
Practical Data Science using Python - Decision Tree - Hyperparameter Tuning
This video explains decision tree - hyperparameter tuning. This clip is from the chapter "Classification using decision trees" of the series "Practical Data Science Using Python".This section explains classification using decision trees.
Curated Video
Reinforcement Learning and Deep RL Python Theory and Projects - DNN Early Stopping
This video explains about DNN early stopping. This clip is from the chapter "DNN Foundation for Deep RL" of the series "Reinforcement Learning and Deep RL Python (Theory and Projects)".This section focuses on the DNN foundation for deep RL.
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Overfitting, Underfitting, and Generalization: Regularization
In this video, we will cover regularization. This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will cover...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Features in Data Science: Activity-Dimensionality Reduction
In this video, we will cover activity-dimensionality reduction. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Machine Learning Overfitting Generalization
In this video, we will cover machine learning overfitting generalization. This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Machine Learning Overfitting Exercise Solution Regularization
In this video, we will cover machine learning overfitting exercise solution regularization. This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Machine Learning Overfitting Exercise
In this video, we will cover machine learning overfitting exercise. This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Machine Learning Overfitting
In this video, we will cover machine learning overfitting. This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...
Curated Video
Reinforcement Learning and Deep RL Python Theory and Projects - DNN Dropout
This video explains about the DNN Dropout. This clip is from the chapter "DNN Foundation for Deep RL" of the series "Reinforcement Learning and Deep RL Python (Theory and Projects)".This section focuses on the DNN foundation for deep RL.
Packt
Fundamentals of Neural Networks - Residual Network
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. This clip is from the chapter "Convolutional Neural...
Curated Video
Deep Learning - Deep Neural Network for Beginners Using Python - Underfitting vs Overfitting
In this video, we will understand two different types of optimizations, which are underfitting and overfitting. This clip is from the chapter "Optimizations" of the series "Deep Learning - Deep Neural Network for Beginners Using...
Curated Video
Deep Learning - Deep Neural Network for Beginners Using Python - Solution and Regularization
In this video, we will look at the possible solution to our quiz question, using regularization. This clip is from the chapter "Optimizations" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Early Stopping
In this video, we will cover DNN Early Stopping. This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Overfitting, Underfitting, and Generalization: Overfitting Introduction
In this video, we will cover an introduction to overfitting. This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section,...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Introduction: Python Practical of the Course
In this video, we will cover a Python practical of the course. This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...