Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
AI: Images Classification for beginners
3 students

AI: Images Classification for beginners

Artificial Intelligence: Deep Learning
Created byHanane M
Last updated 6/2021
English
English [Auto],

What you'll learn

  • Artificial Intelligence
  • Deep Learning
  • Artificial neural network
  • Convolutional Neural Networks
  • Transfer Learning
  • Code: VGG16

Course content

1 section • 6 lectures • 33m total length

  • Part 15:04

    Learn the basics of artificial intelligence, deep learning, artificial neural networks, convolutional neural networks, and transfer learning, with hands-on coding and an overview of algorithms.

  • Part 24:46

    Learn how convolutional neural networks extract features through repeated convolution and pooling, then classify with a fully connected layer, using transfer learning and pretrained models for faster image classification.

  • Part 34:21

    Practice fine-tuning a convolutional neural network for image classification using feature extraction or partial training, freezing initial layers and training higher ones, with Python, Jupyter notebook, and the Chiros library.

  • Part 48:32

    Load the model with a fixed 224 by 224 input size, resize a cat image accordingly, convert it to an array, reshape to a 4D batch, and apply preprocessing.

  • Part 53:34

    Predict image classes with a 1000-class model, retrieve the final network output, and extract the top three probable classes with their probabilities (e.g., 76% tiger, 15% Iturbi, 6% egyptian cats).

  • Bonus7:35

    Develop beginner-friendly ai-driven image classification skills, and leverage bonus content to reinforce core concepts and practical techniques.

Requirements

  • No prior knowledge required

Description

Session: AI: Images Classification for beginners

One of the basic human abilities is to analyze their environment. This involves in most cases recognizing the elements of our field of vision: finding others, identifying cars, animals, etc. This task was difficult for a computer until the emergence of convolutional neural networks in 2012. Luckily, the approach of these networks inspired by our visual cortex has opened many applications, whether in medical imaging, or autonomous cars…ect.


CNNs receive input images, detect the features of each of them, then train a classifier on them. However, features are learned automatically. The CNNs themselves do all the tedious work of extracting and describing features: during the training phase, the classification error is minimized in order to optimize the parameters of the classifier and the features. In addition, the specific architecture of the network makes it possible to extract features of different complexities, from the simplest to the most sophisticated. One of the strengths of CNNs is the automatic extraction and hierarchy of features, which adapt to the given problem: no need to implement an extraction algorithm “by hand”, like SIFT (Scale-invariant Feature Transform) or Harris-Stephens.

First of all I will give a brief introduction on Deep Learning and Artificial Neural networks then i will explain how a convolutional neural network works. In particular, I will present the different elements of a CNN architecture (convolutions, pooling, ReLU, flattening, dense ...) and real networks used in production.



Key Takeaways

  • Artificial Intelligence /Deep learning

  • Artificial neural networks /Convolutional Neural Networks

  • Transfer Learning

This course is for extreme beginners but anyone is welcomed to take it.

If you want more of this content; leave comments.

Who this course is for:

  • Beginners

Report abuse