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Data Science and Machine Learning (Theory and Projects) A to Z - Object Detection: Object Detection Activity
In this video, we will cover object detection activity. 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...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - NumPy for Numerical Data Processing: Slicing-Part 2
In this video, we will cover slicing-part 2. 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) A to Z".In this...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - NumPy for Numerical Data Processing: Arrange, Random, and Reshape-Part 2
In this video, we will cover arrange, random, and reshape-part 2. 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) A...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Engineering: Feature Scaling
In this video, we will cover feature scaling. 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 Engineering: Categorical Features Python
In this video, we will cover categorical features of Python. 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 - Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 2
In this video, we will cover linear regression from scratch- part 2. 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...
Curated Video
Business Intelligence with Microsoft Power BI - with Material - Matrix
This video focuses on matrix in Power BI. This clip is from the chapter "Tables and Matrix in Power BI" of the series "Business Intelligence with Microsoft Power BI - with Material".This section provides an introduction to tables and...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Kernel PCA Versus ISOMAP
In this video, we will cover Kernel PCA versus ISOMAP. 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)...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Singular Value Decomposition (SVD)
In this video, we will cover Singular Value Decomposition (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...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Machine Learning Model Linearity
In this video, we will cover machine learning model linearity. 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
Selenium WebDriver with Java - Basics to Advanced and Frameworks - Practice exercise - Print the Smallest Number in a 3*3 Matrix
This video presents practice exercise on printing the smallest number in a 3*3 matrix. This clip is from the chapter "Java Object Oriented Programming System (OOPS) Basic for Selenium Part - 1" of the series "Selenium WebDriver with Java...
Curated Video
Deep Learning CNN Convolutional Neural Networks with Python - NonVectorized Implementations of Conv2d and Pool2d
This video helps in NonVectorized implementations of Conv2d and Pool2d. This clip is from the chapter "Deep Neural Network Architecture" of the series "Deep Learning CNN: Convolutional Neural Networks with Python".This section focuses on...
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Deep Learning CNN Convolutional Neural Networks with Python - MaxPooling Exercise
This is an exercise video on MaxPooling. This clip is from the chapter "Deep Neural Network Architecture" of the series "Deep Learning CNN: Convolutional Neural Networks with Python".This section focuses on the deep neural network...
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Deep Learning CNN Convolutional Neural Networks with Python - Image Processing
This video explains how the image is formed and processed. This clip is from the chapter "Image Processing" of the series "Deep Learning CNN: Convolutional Neural Networks with Python".This section focuses on image processing.
Curated Video
Deep Learning CNN Convolutional Neural Networks with Python - Gradients of MaxPooling Layer Solution
This is a solution video on gradients of the MaxPooling layer. This clip is from the chapter "Gradient Descent in CNNs" of the series "Deep Learning CNN: Convolutional Neural Networks with Python".This section focuses on gradient descent...
Curated Video
Deep Learning CNN Convolutional Neural Networks with Python - Gradients of MaxPooling Layer
This video explains gradients of the MaxPooling layer. This clip is from the chapter "Gradient Descent in CNNs" of the series "Deep Learning CNN: Convolutional Neural Networks with Python".This section focuses on gradient descent in CNNs.
Curated Video
Salesforce Platform App Builder Certification Training - Filters
This video explains filters in Lightning reports and dashboards. This clip is from the chapter "Lightning Reports and Dashboards" of the series "Salesforce Platform App Builder Certification Training".This section explains Lightning...
Curated Video
Practical Data Science using Python - NumPy Arrays 2
This video explains returning the dot product of two arrays. 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.
Curated Video
Reinforcement Learning and Deep RL Python Theory and Projects - DNN Why Activation Function Is Required
This video explains why activation function is required in DNN. 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...
Curated Video
Python for Machine Learning - The Complete Beginners Course - Implementation in Python: Results Prediction and Confusion Matrix - Classification Algorithms: Logistic Regression
In this video, you will learn about results prediction and confusion matrix. This clip is from the chapter "Classification Algorithms: Logistic Regression" of the series "Python for Machine Learning - The Complete Beginner's Course".In...
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Fundamentals of Neural Networks - Stride
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...
Packt
Fundamentals of Neural Networks - Padding
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...
Packt
Fundamentals of Neural Networks - Image Data
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...
Packt
Fundamentals of Neural Networks - Convolutional Operation
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...