You will master Convolutional Model Training to make accurate image predictions, thanks to this 100% online Postgraduate certificate"

Do you aspire to specialize in Data Distribution Modeling? Thanks to this program you will achieve it in only 150 hours"  

This Postgraduate certificate in Autoencoders, GANs, and Diffusion Models in Deep Learning contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Autoencoders, GANs, and Deep Learning Diffusion Models
• The graphic, schematic and practical contents of the program provide Sports and practical information on those disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies 
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

You will implement state-of-the-art techniques in your models to improve both their performance and generalization capacity"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this program, as well as renowned specialists from leading societies and prestigious universities. 

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations. 

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.  

You will delve into the construction of Coding Architectures and feed Machine Learning models for multiple tasks"

This program will make you learn in a theoretical and practical way with virtual learning systems, so that you can develop your work with a guarantee of successful results"

The didactic materials that make up this Postgraduate certificate will provide students with solid knowledge in  Autoencoders, GANs, and Diffusion Models in Deep Learning. To this end, the syllabus will analyze the keys to represent efficient data by addressing issues such as dimensionality reduction or Deep Learning. Likewise, the syllabus will delve into the performance of Principal Component Analysis with an incomplete linear automatic encoder. Graduates will identify patterns in the data and express these data in terms of new variables.  Generative Adversarial Networks will also be addressed, to generate new information using other input data.

TECH's 100% online methodology will allow you to enjoy effective learning without leaving your home"

Module 1. Autoencoders, GANs, and Diffusion Models

1.1. Representation of Efficient Data

1.1.1. Dimensionality Reduction
1.1.2. Deep Learning
1.1.3. Compact Representations

1.2. PCA Realization with an Incomplete Linear Automatic Encoder

1.2.1. Training Process
1.2.2. Implementation in Python
1.2.3. Use of Test Data

1.3. Stacked Automatic Encoders

1.3.1. Deep Neural Networks
1.3.2. Construction of Coding Architectures
1.3.3. Use of Regularization

1.4. Convolutional Autoencoders

1.4.1. Design of Convolutional Models
1.4.2. Convolutional Model Training
1.4.3. Results Evaluation

1.5. Automatic Encoder Denoising

1.5.1. Application of Filters
1.5.2. Design of Coding Models
1.5.3. Use of Regularization Techniques

1.6. Sparse Automatic Encoders

1.6.1. Increasing Coding Efficiency
1.6.2. Minimizing the Number of Parameters
1.6.3. Using Regularization Techniques

1.7. Variational Automatic Encoders

1.7.1. Use of Variational Optimization
1.7.2. Unsupervised Deep Learning
1.7.3. Deep Latent Representations

1.8. Generation of Fashion MNIST Images

1.8.1. Pattern Recognition
1.8.2. Image Generation
1.8.3. Deep Neural Networks Training

1.9. Generative Adversarial Networks and Diffusion Models

1.9.1. Content Generation from Images
1.9.2. Modeling of Data Distributions
1.9.3. Use of Adversarial Networks

1.10. Implementation of the Models. Practical Application

1.10.1. Implementation of the Models
1.10.2. Use of Real Data
1.10.3. Results Evaluation

You will update your knowledge from the experience of the best professionals in Deep Learning, which will make you a more skilled developer. Enroll now!”