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
