Building Neural Networks with Tensorflow


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

Limited to 1000 students only, it can expire quickly

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