This video explains returning the dot product of two arrays.<br<br/>/>

This clip is from the chapter "Python for Data Science" of the series "Practical Data Science Using Python".This section explains Python for data science.

This video explains why activation function is required in DNN.
<
br/>
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...

In this video, you will learn about results prediction and confusion matrix.
<
br/>
This clip is from the chapter "Classification Algorithms: Logistic Regression" of the series "Python for Machine Learning - The Complete Beginner's...

For a convolutional or pooling operation, the stride denotes the number of pixels by which the window moves after each operation. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural...

This video explains padding in convolutional neural networks. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you...

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...

This video explains image data in CNN (Convolutional Neural Network). This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where...

The Convolution layer (CONV) uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. Its hyperparameters include the filter size and stride. The resulting output is called a feature...

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...

In this video, you will learn about Feed Forward for DEEP Net.
<
br/>
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...

In this video, we will cover similarity based methods criteria.
<
br/>
This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning...

In this video, we will cover supervised PCA and Fishers linear discriminant analysis.
<
br/>
This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science...

In this video, we will cover PCA versus SVD.
<
br/>
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...

In this video, we will cover PCA properties.
<
br/>
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...

In this video, we will cover PCA Max Variance Formulation.
<
br/>
This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning...

In this video, we will cover PCA implementation.
<
br/>
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...

In this video, we will cover PCA for small sample size problems (DualPCA).
<
br/>
This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine...

In this video, we will cover PCA derivation.
<
br/>
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...

In this video, we will cover Kernel PCA.
<
br/>
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...

In this video, we will cover image features.
<
br/>
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...

In this video, we will cover derived features.
<
br/>
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...

In this video, we will cover nonvectorized implementations of Conv2d and Pool2d.
<
br/>
This clip is from the chapter "Deep learning: Convolutional Neural Networks with Python" of the series "Data Science and Machine Learning...

In this video, we will understand why activation function is required.
<
br/>
This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and...

In this video, we will cover language modelling next word prediction Python 4.
<
br/>
This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and...