Neural Networks with Tensorflow

A Primer

Language: english

Note: 4.2/5 (47 notes) 20,172 students

Instructor(s): Cristi Zot

Last update: 2020-12-28

What you’ll learn

• Building Neural Networks with Tensorflow

 

Requirements

• 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

 

Course content

• Lessons
  • Artificial Neurons
  • Feed Forward Networks and Activations
  • Softmax Output
  • Gradient Descent
  • Backpropagation
  • Basics of TensorFlow – 1
  • Basics of TensorFlow – 2
  • Computational Graph, Ops, Sessions, Placeholders
  • Loss Function, MSE, Cross Entropy
  • Linear Regression
  • Logistic Regression
  • Handwriting Recognition with MNIST
  • Convolutional Neural Networks – 1
  • Convolutional Neural Networks – 2
  • Convolutional Neural Networks – 3
  • Convolutional Neural Networks – 4
  • Convolutional Neural Networks – 5
  • Convolutional Neural Networks – 6
  • CNN and Cifar10 – 1
  • CNN and Cifar10 – 2
  • CNN and Cifar10 – 3
  • CNN and Cifar10 – 4
  • CNN and Cifar10 – 5
  • Tactics to Improve the Model
  • Text and Sequence Data – Intro
  • Recurrent Neural Networks – 1
  • Recurrent Neural Networks – 2
  • Recurrent Neural Networks – 3
  • Recurrent Neural Networks – 4
  • Recurrent Neural Networks – 5
  • Recurrent Neural Networks – 6
  • Recurrent Neural Networks – 7
  • Recurrent Neural Networks – 8
• In Progress
  • Neural Style Transfer with VGG19 – 1
  • Neural Style Transfer with VGG19 – 2
  • Neural Style Transfer with VGG19 – 3
  • Neural Style Transfer with VGG19 – 4
• Conclusion
  • Get in Touch

 

Time remaining or 905 enrolls left

 

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