Packt
Fundamentals of Neural Networks - Convolution in 2D and 3D
This video explains Convolution in 2D and 3D. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you will start with...
Packt
Fundamentals of Neural Networks - Language Processing
NLP is a tool for structuring data in a way that AI systems can process that deals with language. NLP uses AI to 'read' through a document and extract key information. This clip is from the chapter "Recurrent Neural Networks" of the...
Curated Video
Deep Learning - Deep Neural Network for Beginners Using Python - Feed Forward for DEEP Net
In this video, you will learn about Feed Forward for DEEP Net. This clip is from the chapter "Deep Learning" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this section, we will dive deeper into deep...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Versus SVD
In this video, we will cover PCA versus SVD. 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 Projects) A to Z".In...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Properties
In this video, we will cover PCA properties. 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 Projects) A to Z".In...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Max Variance Formulation
In this video, we will cover PCA Max Variance Formulation. 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 - Feature Selection: Similarity Based Methods Criteria
In this video, we will cover similarity based methods criteria. 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 - Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis
In this video, we will cover supervised PCA and Fishers linear discriminant analysis. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Implementation
In this video, we will cover PCA implementation. 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 Projects) A to...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA For Small Sample Size Problems(DualPCA)
In this video, we will cover PCA for small sample size problems (DualPCA). This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Derivation
In this video, we will cover PCA derivation. 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 Projects) A to Z".In...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Kernel PCA
In this video, we will cover Kernel PCA. 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 Projects) A to Z".In...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Deep Neural Network Architecture: Nonvectorized Implementations of Conv2d and Pool2d
In this video, we will cover nonvectorized implementations of Conv2d and Pool2d. This clip is from the chapter "Deep learning: Convolutional Neural Networks 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 - DNN and Deep Learning Basics: DNN Why Activation Function Is Required
In this video, we will understand why activation function is required. 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 - Feature Engineering: Image Features
In this video, we will cover image features. 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 Projects) A to Z".In...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Engineering: Derived Features
In this video, we will cover derived features. 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 Projects) A to...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - RNN Implementation: Language Modelling Next Word Prediction Python 4
In this video, we will cover language modelling next word prediction Python 4. This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - RNN Architecture: Weight Sharing
In this video, we will cover weight sharing. This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Python for Data Science: NumPy Pandas and Matplotlib (Part 3)
In this video, we will cover NumPy Pandas and Matplotlib (part 3).
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Python for Data Science: NumPy Pandas and Matplotlib (Part 2)
In this video, we will cover NumPy Pandas and Matplotlib (part 2).
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - NumPy for Numerical Data Processing: NumPy BroadCasting and Concatenation
In this video, we will cover NumPy BroadCasting and concatenation. This clip is from the chapter "Basics for Data Science: Python for Data Science and Data Analysis" of the series "Data Science and Machine Learning (Theory and Projects)...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Neural Style Transfer: Problem Setup
In this video, we will cover problem setup. This clip is from the chapter "Deep learning: Convolutional Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Multivariate Gaussian
In this video, we will cover multivariate Gaussian.
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Classification
In this video, we will cover classification. 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...