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Deep Learning - Deep Neural Network for Beginners Using Python - Multi-Class Cross Entropy
In this video, you will learn about multi-class cross entropy. This clip is from the chapter "Basics of Deep Learning" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this section, we will cover the...
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Deep Learning - Deep Neural Network for Beginners Using Python - Introduction to Gradient Descent
In this video, we will get introduced to gradient descent. 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 learning.
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Deep Learning - Deep Neural Network for Beginners Using Python - Dropout
In this video, we will understand what Dropout is. This clip is from the chapter "Optimizations" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this section, we will cover optimizations of a neural...
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Deep Learning - Deep Neural Network for Beginners Using Python - Cross Entropy Implementation
In this video, you will learn how to implement cross entropy in our code. This clip is from the chapter "Basics of Deep Learning" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this section, we will...
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Deep Learning - Deep Neural Network for Beginners Using Python - Cross Entropy Formulation
In this video, we will understand the relation between cross entropy, error, and probability. This clip is from the chapter "Basics of Deep Learning" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this...
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Deep Learning - Deep Neural Network for Beginners Using Python - Cross Entropy
In this video, we will talk about cross entropy. This clip is from the chapter "Basics of Deep Learning" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this section, we will cover the basics of deep...
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Deep Learning - Deep Neural Network for Beginners Using Python - Combining Probabilities
In this video, we will work on non-linear boundaries and learn how to combine probabilities. This clip is from the chapter "Deep Learning" of the series "Deep Learning - Deep Neural Network for Beginners Using Python".In this section, we...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Statistical Based Methods
In this video, we will cover statistical based methods. 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)...
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Data Science and Machine Learning (Theory and Projects) A to Z - Expectations: Sample Mean
In this video, we will cover sample mean. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...
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Data Science and Machine Learning (Theory and Projects) A to Z - Expectations: Law of Large Numbers
In this video, we will cover law of large numbers. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...
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Data Science and Machine Learning (Theory and Projects) A to Z - Expectations: Definition
In this video, we will cover definition. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...
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Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN What is Loss Function Exercise
In this video, we will cover what is loss function 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 this...
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Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Weights Initializations
In this video, we will cover DNN weights initializations. 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...
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Data Science and Machine Learning (Theory and Projects) A to Z - Continuous Random Variables: Zero Probability to Individual Values
In this video, we will cover zero probability to individual values. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...
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Data Science and Machine Learning (Theory and Projects) A to Z - Continuous Random Variables: Probability Density Functions
In this video, we will cover probability density functions. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A...
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Data Science and Machine Learning (Theory and Projects) A to Z - Continuous Random Variables: Exponential
In this video, we will cover Exponential. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...
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Data Science and Machine Learning (Theory and Projects) A to Z - Random Variables: Random Variables Examples Solution 01
In this video, we will cover random variables examples solution 01. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...
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Data Science and Machine Learning (Theory and Projects) A to Z - Random Variables: Introduction
In this video, we will cover an introduction. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...
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Data Science and Machine Learning (Theory and Projects) A to Z - Random Variables: Homework
In this video, we will cover Homework. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...
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Data Science and Machine Learning (Theory and Projects) A to Z - Random Variables: Geometric Random Variable
In this video, we will cover geometric random variable. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...
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Data Science and Machine Learning (Theory and Projects) A to Z - Random Variables: Bernulli Trail Python Practice Solution 01
In this video, we will cover Bernulli Trail Python practice solution 01. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...
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Data Science and Machine Learning (Theory and Projects) A to Z - Probability Model: Probability Models More Examples
In this video, we will cover probability models more examples. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects)...
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Data Science and Machine Learning (Theory and Projects) A to Z - Probability Model: Probability Models Example
In this video, we will cover probability models example. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...
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Data Science and Machine Learning (Theory and Projects) A to Z - Probability Model: Probability Models Continuous
In this video, we will cover probability models continuous. This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and Projects) A...