Learn to build and train autoencoders using Python, TensorFlow, and Keras in this comprehensive tutorial video. Explore the process of chaining encoder and decoder architectures to create an autoencoder, and gain hands-on experience training it with the MNIST dataset. Follow along as the instructor guides you through building the autoencoder, updating the summary method, implementing compile and train methods, and creating a training script. Discover insights into the MNIST dataset and observe the training process in action. By the end of this 27-minute tutorial, you'll have a solid understanding of autoencoder implementation and training techniques using popular deep learning frameworks.