Deep Learning: GANs and Variational Autoencoders

Learn to sample from a learned generative model by building a base classifier that models P(X|Y) and P(Y). Sample from X|Y with gaussians and from the joint P(X,Y) via P(Y).

Explore unsupervised learning by implementing a Bayes classifier, fitting Gaussians per class, and generating samples and mean images to visualize class distributions.

Model multimodal data with a gaussian mixture model to enhance the base classifier, using a latent variable z, Bayes rule for inference, and EM training.

Explore a bayesian gaussian mixture approach to sampling in unsupervised learning, generating class-specific samples and visualizing cluster means from a capped gaussian mixture model.

Explore why generative modeling matters by linking unsupervised learning to supervised learning and highlighting autoencoders, GANs, reinforcement learning, semi supervised learning, and image to image translation.

Explore neural networks and autoencoders in unsupervised learning, covering gradient descent, fitting versus predicting, and how autoencoders enable dimensionality reduction and generative models.

Perform a TensorFlow-based autoencoder warmup for unsupervised deep learning. Build a one-hidden-layer network with sigmoid output, train with cross-entropy or squared loss, and generate random reconstructions.

This Theano warmup teaches building a one hidden layer autoencoder for unsupervised learning, using cross-entropy loss and RMSProp updates, with training loops and data reconstruction.

Submit feedback through the suggestion box to improve the course on GANs and variational autoencoders, detailing background, difficulty, and missing explanations via the form on lazy programmers slash suggestions.

Explore the background of variational autoencoders, linking autoencoders with latent space z and Bayesian inference, and preview mechanics of training and sampling in subsequent lectures.

Explore the variational autoencoder architecture, detailing the encoder producing a latent distribution q(z), sampling from Gaussian z, and the decoder generating Bernoulli outputs via sigmoid for binary data.

The encoder outputs a 2D axis-aligned gaussian by doubling the final layer to produce mean and standard deviation, with softplus ensuring positivity.

Explore how autoencoders compress inputs into a latent space with an encoder and decoder, and relate posterior predictive and prior predictive samples from Gaussian and standard normal distributions.

Explore how variational autoencoders train by maximizing the evidence lower bound, combining reconstruction loss and a kl divergence toward a standard normal prior.

Learn to implement a variational autoencoder in TensorFlow, build encoder–decoder, sample with stochastic tensors, and minimize a cost with expected log likelihood and Khail divergence to produce posterior predictive sample.

Explore TensorFlow's stochastic tensor in unsupervised deep learning part two, sampling from a given mean and standard deviation to confirm outputs around five and three via a session histogram.

Explore TensorFlow implementation of a variational autoencoder, detailing encoder-decoder architecture with mean and standard deviation outputs, sampling from the latent vector, KL divergence, and prior/posterior predictive sampling using Bernoulli logits.

Learn how the reparameterization trick lets us sample z from q(z|x) as mu(x, theta) plus sigma(x, theta) times epsilon, making z differentiable with respect to theta.

This lecture implements a variational autoencoder in Theano, detailing encoder outputs for mean and std, reparameterization, a decoder with Bernoulli outputs, and training with KL divergence and reconstruction loss.

Visualize the autoencoder's two-dimensional latent space by building a 20 by 20 grid from -3 to 3, reconstructing digits to reveal how digits morph and blur at boundaries.

Explore the Bayesian perspective of variational autoencoders, deriving the evidence lower bound and linking reconstruction error and regularization to the divergence between encoder and prior.

Explore variational autoencoders that encode data as gaussian distributions, sample to the decoder, and optimize a cost combining expected log likelihood and KL divergence—the evidence lower bound.

Explore the basic principles of generative adversarial networks in unsupervised learning, where a generator and discriminator duel to produce realistic samples and reach a Nash equilibrium.

Explain GAN cost function where the discriminator classifies real versus fake images with binary cross entropy, while the generator tries to fool it, using two optimizers in a zero-sum game.

Investigate the GAN cost function, show how the generator’s gradient vanishes when the discriminator is strong, and apply a non saturating heuristic by flipping the target to one.

Demonstrates the dcgan architecture, a deep convolutional gan with generator and discriminator built from convolutional layers, batch normalization, and adam optimization, producing high quality 64x64 images from a latent vector.

Review batch normalization, with its forward pass: linear transform, batch normalization, and activation. Explain gamma and beta learnable parameters and how running mean and variance support test time.

Learn how fractionally strided convolution upscales images in generator networks, using transpose convolution and gradient techniques, clarifying the deconvolution terminology.

Review TensorFlow implementation notes for GANs and variational autoencoders, covering batch normalization, fractionally strided convolution, and the use of scopes and reuse flags across training and testing.

Implement a DCGAN in TensorFlow, detailing discriminator and generator architectures, data scaling, and batch normalization. Train on celeb faces and eminence data set, generate samples, and save progress images.

Review practical Theano notes for unsupervised deep learning, detailing convolution gradients, dimension ordering, output shapes, stride, normalization, and the Adam optimizer with coding practice.

Explore a Theano-based DCGAN implementation for unsupervised learning, covering data scaling, discriminator and generator design, batch normalization, and training on celeb and amnesty datasets.

Explore how GANs train a generator and a discriminator to produce realistic images using the Haraszti cost and DCGAN architecture.

Review theano basics by building symbolic variables, tensors, and shared variables, linking them in a graph to auto‑compute gradients, set updates, and run a training loop.

implement a neural network in theano, define placeholders and shared parameters, use the built-in theano softmax, build a cost function with regularisation, and create training and prediction functions for data.

Master TensorFlow basics by defining variables, placeholders, and expressions, running sessions, and performing matrix multiplications, then minimize a simple cost function with gradient descent using a learning rate.

Apply TensorFlow basics to build a neural network with an added hidden layer, train with the Adam optimizer using softmax cross-entropy, and monitor test error to observe potential overfitting.

Clarify the appendix as an optional faq that answers common questions, provides historical context, and directs you to the question and answer section for quick responses.

Understand why installation lectures are guidelines, focus on principles over syntax, and learn when to install or skip libraries such as Cntk, Theano, and OpenAI Gym.

Learn to install data science libraries on Windows with Anaconda, using a one-click setup, updates, and tools like NumPy, SciPy, Matplotlib, Pandas, TensorFlow, and OpenAI Gym.

Set up your development environment across Windows, Linux, and Mac, installing numpy, Theano, and TensorFlow, using VirtualBox with Ubuntu and guided pip or Anaconda installation.

Practice coding by yourself to build muscle memory, applying a simple fit and predict workflow in supervised learning, where all data behaves the same.

Learn why to code by yourself using test driven development to shape api design, and how alternating theory and coding builds intuition, hands-on learning, and independent problem solving.

Demonstrate that Jupiter notebook offers no real advantage over plain Python; code runs the same in a notebook or console, and print statements aid debugging.

Learners can choose Python 2 or Python 3 for this course, with updates aligned to Python 3. The lecture covers print as a function, range vs xrange, and division rules.

Argues that theano is not dead, citing the 1.8.1 release and its influence on open source libraries, while stressing fundamentals and practical deep learning.

Succeed in this deep learning course by asking questions in the q&a, meeting prerequisites, and implementing everything you learn to bridge theory and code.

Explore how the course balances rigorous math and practical coding, clarifying prerequisites and pacing, while implementing every algorithm in code on real world data such as text and images.

Discover a skill-building roadmap for learning machine learning topics, showing dependencies from linear regression to logistic regression, neural networks, and convolutional neural networks.

Navigate the machine learning prerequisite roadmap, moving from word embeddings and unsupervised learning to recurrent and dynamic networks, CNNs, and reinforcement learning.

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