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Autoencoders | Deep Learning Animated

In this video, we dive into the world of autoencoders, a fundamental concept in deep learning. You'll learn how autoencoders simplify complex data into essential representations, known as latent spaces. We'll break down the architecture, training process, and real-world applications of autoencoders, explaining how and why we use the latent space of these models. We start by defining what an autoencoder is and how it works, showcasing the role of the encoder, bottleneck, and decoder. Through practical examples, we'll illustrate how autoencoders compress data, the importance of the latent dimension, and how to measure reconstruction accuracy using mean squared error (MSE). We'll also explore how latent spaces evolve and organize during training, and their application in tasks like image classification and medical data analysis. If you want to explore more about autoencoders, here are some classic papers from Yoshua Bengio Geoffrrey Hintin, and Pascal Vincent : - Reducing the Dimensionality of Data with Neural Networks https://www.science.org/doi/10.1126/s... - Extracting and composing robust features with denoising autoencoders https://www.cs.toronto.edu/~larocheh/... - Contractive auto-encoders: explicit invariance during feature extraction https://www.iro.umontreal.ca/~lisa/po... Chapters: 00:00 Intro 00:50 Autoencoder basics 04:15 Latent Space 06:05 Latent Dimension 08:50 Application 10:03 Limitations 11:25 Outro This video features animations created with Manim, inspired by Grant Sanderson's work at @3blue1brown. If you enjoyed the content, please like, comment, and subscribe to support the channel! #DeepLearning #Autoencoders #ArtificialIntelligence #DataScience #LatentSpace #UNet #Manim #Tutorial #machinelearning #education