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Fundamentals of Machine Learning - Multilayer Perceptron (MLP)
This video explains a lab session on neural networks and Multilayer Perceptron (MLP) models. This clip is from the chapter "Labs" of the series "Fundamentals of Machine Learning".This section explains the various lab exercises on linear...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Wrapper Methods
In this video, we will cover wrapper 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) A to...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Embedded Methods
In this video, we will cover embedded 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) A to...
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Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN PyTorch CIFAR10 Example
In this video, we will cover DNN PyTorch CIFAR10 example. 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 Implementation Stochastic Gradient Descent
In this video, we will cover DNN implementation stochastic gradient descent. 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...
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Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Gradient Descent Stochastic Batch Minibatch
In this video, we will cover DNN gradient descent stochastic batch minibatch. 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...
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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...
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Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Activation Functions in PyTorch
In this video, we will cover DNN activation functions in PyTorch. 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...
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Data Science and Machine Learning (Theory and Projects) A to Z - Deep Neural Networks and Deep Learning Basics: Feedforward Fully Connected MLP
In this video, we will cover Feedforward fully connected MLP. 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 - Deep Neural Networks and Deep Learning Basics: convergence Animation
In this video, we will cover convergence animation.
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Data Science and Machine Learning (Theory and Projects) A to Z - Deep Neural Networks and Deep Learning Basics: Calculating Number of Weights of DNN
In this video, we will cover calculating number of weights of DNN. 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...
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Data Science and Machine Learning (Theory and Projects) A to Z - Deep Neural Networks and Deep Learning Basics: Batch Minibatch Stochastic
In this video, we will cover batch minibatch stochastic.
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Data Science and Machine Learning (Theory and Projects) A to Z - Deep Learning Overview: Introduction to Deep Neural Networks (DNN)
In this video, we will cover an introduction to Deep Neural Networks (DNN). 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...
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Data Science and Machine Learning (Theory and Projects) A to Z - Classical CNNs: Resnet
In this video, we will cover Resnet. 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 cover...
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Data Science and Machine Learning (Theory and Projects) A to Z - Python for Data Science: TensorFlow for classification
In this video, we will cover TensorFlow for classification.
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Data Science and Machine Learning (Theory and Projects) A to Z - Project II_ Stock Price Prediction: Data Preparation
In this video, we will cover data preparation. 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...
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Data Science and Machine Learning (Theory and Projects) A to Z - Project I_ Book Writer: Data Mapping
In this video, we will cover data mapping. 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 cover...
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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,...
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Data Science and Machine Learning (Theory and Projects) A to Z - Overfitting, Underfitting, and Generalization: Overfitting Example in Python
In this video, we will cover an overfitting example in Python. 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...
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Data Science and Machine Learning (Theory and Projects) A to Z - Overfitting, Underfitting, and Generalization: Data Snooping and the Test Set
In this video, we will cover data snooping and the test set. 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,...
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Data Science and Machine Learning (Theory and Projects) A to Z - Hands-on Machine Learning Project Using Scikit-Learn: Cross-validation with Python
In this video, we will cover cross-validation with Python. 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...
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Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in RNN: Why Gradients
In this video, we will understand why gradients. 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...
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Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in RNN: Introduction to Gradient Descent Module
In this video, we will cover an introduction to gradient descent module. 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...
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Data Science and Machine Learning (Theory and Projects) A to Z - Transfer Learning: Practical Tips
In this video, we will cover practical tips. 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...