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Deep Learning in Minneapolis MN

Deep Learning in Minneapolis MN

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    Data Analytics
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Course Highlights

Mathematics for Deep Learning

 

 

 

Basic linear algebra concepts.

 

Probability and statistics fundamentals.

 

Understanding gradients and optimization.

 

 

 

Neural Networks Basics

 

 

 

Structure of artificial neural networks.

 

Activation functions.

 

Loss functions and optimization techniques.

 

 

 

Python for Deep Learning

 

 

 

Using Python for data science.

 

Working with NumPy and Pandas basics.

 

Data preprocessing techniques.

 

 

 

Deep Learning Frameworks

 

 

 

Introduction to TensorFlow and PyTorch.

 

Building and training models.

 

 

 

Training Deep Neural Networks

 

 

 

Forward and backward propagation.

 

Gradient descent and variants.

 

Overfitting and regularization.

 

 

 

Convolutional Neural Networks (CNN)

 

 

 

Understanding CNN architecture.

 

Image classification and object detection basics.

 

Working with image datasets.

 

 

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