Convolutional Neural Networks with TensorFlow in Python
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Convolutional Neural Networks with TensorFlow in Python at 365DataScience Overview
Duration | 4 hours |
Mode of learning | Online |
Credential | Certificate |
Convolutional Neural Networks with TensorFlow in Python at 365DataScience Highlights
- Earn a certificate from 365 Data Science
- Learn from 48 High Quality Lessons
- Gain access to 8 practical asks
Convolutional Neural Networks with TensorFlow in Python at 365DataScience Course details
- Learn the fundamentals of CNNs
- Get the hang of convolution
- Perform computer vision
- Master working with TensorFlow and Tensorboard
- Approach multilabel classification
- Understand kernels
- This course offers a deep dive into an advanced neural network construction -convolutional neural networks
- First, the program explains the concept of image kernels, and how it relates to CNNs
- Then, students will get familiar with the CNN itself, its building blocks, and what makes this kind of network necessary for computer vision
- Students will apply the theoretical bit to the MNIST example using TensorFlow, and understand how to track and visualize useful metrics using TensorBoard in a dedicated practical section
- Later in the course, students will be introduced to a handful of techniques to improve the performance of neural networks, and a huge real-world practical project for classifying fashion item pictures
- Finally, students will cap it all off with an intriguing look through the history of the most influential CNN architectures
Convolutional Neural Networks with TensorFlow in Python at 365DataScience Curriculum
Introduction To The Course
What does the course cover?
Why CNNs?
Kernels
Introduction to image kernels
How do image transformations work?
Kernels as matrices
Convolution - applying kernels
Edge handling
CNN Introduction
CNNs motivation
Feature maps
Pooling and Stride
Dimensions
Neural Network Techniques (Revision)
Activation functions
Overfitting and early stopping
Optimizers
Setting Up The Environment
Setting up the environment - Do not skip, please!
Installing the packages
CNN Assembling - MNIST
Road plan
A simple CNN architecture
Preprocessing the data
Building and training the CNN
Testing the trained CNN
Tensorboard: Visualization Tool For TensorFlow
Common Techniques For Better Performance Of Neural Networks
A Practical Project: Labelling Fashion Items
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