Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Python-based Video Classification with Deep Learning
Rating: 4.1 out of 5(16 ratings)
1,051 students

Python-based Video Classification with Deep Learning

Master Video Classification with Keras and Python: Build Robust Action Recognition Models from Scratch : Hands-on
Created byKarthik Karunakaran, Ph.D.
Last updated 3/2023
English

What you'll learn

  • Pre-processing and cleaning of video data
  • Extracting features from video frames using pre-trained models
  • Building and training a custom Keras deep learning model for video classification
  • Fine-tuning a pre-trained Transformer model for video classification
  • Custom prediction loop for predicting actions in new videos

Course content

2 sections • 28 lectures • 1h 11m total length

  • Introduction3:37
  • What is Video Classification?2:26

    Classify a video into predefined categories by processing frames with deep learning models such as cnn s, r nns, and transformers to produce frame and final video probabilities.

  • How Video Classification is done?2:03
  • About this Project1:20

    Explore video classification with a transformer-based model, using Python and Keras to build a deep learning classifier that labels videos by actions such as playing tennis and shaving beard.

  • Why Python and Keras?2:05

    Explore why Python powers machine learning and data science, and how Keras on TensorFlow offers a user-friendly interface with transformer-based models, data generators, and callbacks for video classification.

  • Why Google Colab?2:20

    Use Google Colab to develop your video classification project, leveraging a free cloud environment with pre-installed TensorFlow and Keras, GPUs and TPUs, and seamless Google Drive collaboration.

Requirements

  • Basic knowledge of Python Programming

Description

This course is designed to teach you how to build a video classification model using Keras and TensorFlow, with a focus on action recognition. Video classification has numerous applications, from surveillance to entertainment, making it an essential skill in today's data-driven world. Through this course, you will learn how to extract features from video frames using pre-trained convolutional neural networks, preprocess the video data for use in a custom prediction loop, and train a Transformer-based classification model using Keras.

By the end of this course, you will be able to build your own video classification model and apply it to various real-world scenarios. You will gain a deep understanding of deep learning techniques, including feature extraction, preprocessing, and training with Keras and TensorFlow. Additionally, you will learn how to optimize and fine-tune your model for better accuracy.

This course is suitable for anyone interested in deep learning and video classification, including data scientists, machine learning engineers, and computer vision experts. The demand for professionals skilled in deep learning and video classification is increasing rapidly in the industry, and this course will equip you with the necessary skills to stay ahead of the competition.

Join us today and take the first step towards becoming an expert in video classification using Keras and TensorFlow!

Who this course is for:

  • Python developers interested in machine learning and video classification
  • Data scientists looking to expand their knowledge in computer vision and deep learning
  • Students or professionals in the field of computer science and engineering interested in developing and deploying video classification models
  • Anyone interested in learning how to implement a state-of-the-art video classification model using Keras and TensorFlow.

Report abuse