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Requirements
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You should know Python programming, have basic math knowledge, and basic concepts of machine learning before enrolling.
Description
You’re going to learn the most popular library to build networks and machine learning algorithms.
In this hands-on, practical course, you will be working your way through with Python, Tensorflow, and Jupyter notebooks.
What you will learn:
- Basics of Tensorflow
- Artificial Neurons
- Feed Forward Neural Networks
- Activations and Softmax Output
- Gradient Descent
- Backpropagation
- Loss Function
- MSE
- Model Optimization
- Cross-Entropy
- Linear Regression
- Logistic Regression
- Convolutional Neural Networks (with examples)
- Text and Sequence Data
- Recurrent Neural Networks (with examples)
- Neural Style Transfer (in progress)
Who this course is for:
- You want to get into machine learning and artificial neural networks
- You already work in ML/AI and need to learn Tensorflow
- You are a student, know some coding, and want to get into machine learning
