Udemy - Unleashing Unlabelled Data - Self-Supervised Learning

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Udemy - Unleashing Unlabelled Data - Self-Supervised Learning (Size: 1.3 GB)
  1. Introduction to Numpy.mp4 29.9 MB
  1. IntroductionWhat Is Self-Supervised Machine Learning (ML).mp4 12.3 MB
  1. What is Supervised Learning.mp4 72.5 MB
  1. What is Unsupervised Learning.mp4 28.8 MB
  10. Convolutional Autoencoders-Encoding.mp4 65.4 MB
  11. Convolutional Autoencoders-Decoding.mp4 49 MB
  2. Data and Code.html 102.4 B
  2. Theory Behind ANN and DNN.mp4 97.2 MB
  2. Theory Behind Autoencoders.mp4 60.5 MB
  2. What Is Pandas.mp4 74.9 MB
  3. Basic Data Cleaning With Pandas.mp4 34.4 MB
  3. Python Installation.mp4 42.6 MB
  3. The Link Between Self-Supervised Machine Learning (ML) and Autoencoders.mp4 18 MB
  3. What Are Activation Functions.mp4 87.5 MB
  4. Basics of Data Visualisation.mp4 73.9 MB
  4. Introduction To Convolutional Neural Networks (CNN).mp4 176 MB
  4. Lets Implement a Basic Auto-Encoder With H20.mp4 58.3 MB
  4. Start With Google Colaboratory Environment.mp4 40.4 MB
  5. Google Colabs and GPU.mp4 30.2 MB
  5. Variational Autoencoder (VAE) With H2O.mp4 36.6 MB
  6. Installing Packages In Google Colab.mp4 28.6 MB
  6. What Is Denoising.mp4 47.1 MB
  7. Autoencode the Image Data With H2O.mp4 39.5 MB
  7. Install H2O In Colab.mp4 28.6 MB
  8. Denoise the Data with H2O.mp4 25.1 MB
  8. Installing H2O Locally.mp4 15.5 MB
  9. Autoencoders With Keras Deep Learning.mp4 68.3 MB
  9. Course details.mp4 36.6 MB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 30 total files

Description


Unleashing Unlabelled Data: Self-Supervised Learning
https://FreeCourseWeb.com

Published 7/2023
Created by Minerva Singh
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 28 Lectures ( 2h 33m ) | Size: 1.34 GB

Master the Power of Unlabelled Data: Self-Supervised Machine Learning Techniques in Python for Artificial Intelligence

What you'll learn
Understanding the concepts behind basic machine learning tasks, including clustering and classification
Learn about the uses of self-supervised machine learning
Implement self-supervised machine learning frameworks such as autoencoders using Python
Learn about deep learning frameworks such as Keras and H2O

Requirements
Basic Python data science concepts
Basic Python syntax
Understanding of the Colab environment

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