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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