Specialize in the various applications of Deep Learning to contribute to the technological transformation of society"

Deep Learning has enabled the advancement of areas such as Computer Vision, Natural Language Processing and Robotics. Currently, the application of these techniques is increasingly in demand in different sectors such as Medicine, Engineering, Marketing or Security, among others. For example, in Medicine, Deep Learning has proven to be very useful in the early detection of diseases through the analysis of medical images. In Marketing, it can be used to make accurate predictions of consumer behavior and personalize offers. 

These are just a few examples that illustrate the importance of specialization in this area. Thus, the Postgraduate diploma in Deep Learning Applications has been designed, a program that aims to prepare professionals capable of using these techniques in different contexts. The degree consists of modules that address the most popular applications of Deep Learning and enrollees will be updated in the design and training of recurrent neural networks, Autoencoders, GAN and Diffusion Models, among other key points. 

In addition, the degree uses the Relearning pedagogical methodology to assimilate the concepts more quickly. Likewise, the flexibility to organize academic resources allows students to adapt their study time to their personal and professional needs. And always completely online.

You will develop highly demanded skills to excel in an increasingly global industry such as Deep Learning"

This Postgraduate diploma in Deep Learning Applications contains the most complete and updated educational program on the market. Its most outstanding features are:  

• The development of case studies presented by experts in Deep Learning Applications
• The graphic, schematic and eminently practical contents with which it is conceived gather technological and practical information on those disciplines that are essential for professional practice 
• Practical exercises where self-assessment can be used 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 

Gain a competitive advantage in the job market by generating text through recurrent neural networks"

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

Its 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 an immersive education programmed to learn in real situations.  

The design of this program focuses on Problem-Based Learning, by means of which the professional must try to solve the different professional practice situations that are presented throughout the academic course. For this purpose, the student will be assisted by an innovative interactive video system created by renowned experts.

Expertly evaluate the use of neural networks to improve the accuracy of an agent when making decisions"

Implement advanced reinforcement algorithms to improve agent performance with this Postgraduate diploma"

The Deep Learning Applications Postgraduate diploma covers a broad academic spectrum, from Natural Language Processing to processing sequences using RNN and CNN. In fact, the curriculum has been designed in a thorough and detailed way, and is supported by several innovative teaching resources that are available to students on the Virtual Campus of the degree. Some of them are videos in detail, case studies or interactive schemes. 

A syllabus that proposes a comprehensive tour of recurrent neural networks"

Module 1. Processing sequences using RNN (Recurrent Neural Networks) and CNN (Convolutional Neural Networks)

1.1. Recurrent neurons and layers 

1.1.1. Types of Neurons Recurring 
1.1.2. Architecture of a recurrent layer 
1.1.3. Applications of recurrent layers 

1.2. Recurrent Neural Network (RNN) Training 

1.2.1. Backpropagation over time (BPTT). 
1.2.2. Stochastic Downward Gradient 
1.2.3. Regularization in RNN training 

1.3. Evaluation of RNN models 

1.3.1. Evaluation Metrics 
1.3.2. Cross Validation 
1.3.3. Hyperparameter tuning 

1.4. Prerenal RNNs 

1.4.1. Prenetrated networks 
1.4.2. Transfer of learning 
1.4.3. Fine Tuning 

1.5. Forecasting a time series 

1.5.1. Statistical models for forecasting 
1.5.2. Time series models 
1.5.3. Models based on neural networks 

1.6. Interpretation of time series analysis results. 

1.6.1. Main Component Analysis 
1.6.2. Cluster analysis 
1.6.3. Correlation Analysis 

1.7. Handling of long sequences 

1.7.1. Long Short-Term Memory (LSTM) 
1.7.2. Gated Recurrent Units (GRU) 
1.7.3. 1D Convolutional 

1.8. Partial Sequence Learning 

1.8.1. Deep learning methods 
1.8.2. Generative models 
1.8.3. Reinforcement learning 

1.9. Practical Application of RNN and CNN 

1.9.1. Natural Language Processing 
1.9.2. Pattern Recognition 
1.9.3. Computer vision 

1.10. Differences in classical results 

1.10.1. Classical vs. RNN methods 
1.10.2. Classical vs. CNN methods 
1.10.3. Difference in training time

Module 2. Natural Language Processing (NLP) with Natural Recurrent Networks (NNN) and Attention

2.1. Text generation using RNN 

2.1.1. Training an RNN for text generation 
2.1.2. Natural language generation with RNN 
2.1.3. Text generation applications with RNN 

2.2. Training data set creation 

2.2.1. Preparation of the data for training an RNN 
2.2.2. Storage of the training dataset 
2.2.3. Data cleaning and transformation 

2.3. Sentiment Analysis 

2.3.1. Classification of opinions with RNN 
2.3.2. Detection of themes in comments 
2.3.3. Sentiment analysis with deep learning algorithms 

2.4. Encoder-decoder network for neural machine translation. 

2.4.1. Training an RNN for machine translation 
2.4.2. Use of an encoder-decoder network for machine translation 
2.4.3. Improving the accuracy of machine translation with RNNs 

2.5. Attention mechanisms 

2.5.1. Application of care mechanisms in RNN 
2.5.2. Use of care mechanisms to improve the accuracy of the models 
2.5.3. Advantages of attention mechanisms in neural networks 

2.6. Transformer models 

2.6.1. Use of Transformers models for natural language processing
2.6.2. Application of Transformers models for vision 
2.6.3. Advantages of Transformers models 

2.7. Transformers for vision 

2.7.1. Use of Transformers models for vision 
2.7.2. Image data preprocessing 
2.7.3. Training of a Transformer model for vision 

2.8. Hugging Face Transformer Library 

2.8.1. Using the Hugging Face Transformers Library 
2.8.2. Application of the Hugging Face Transformers Library 
2.8.3. Advantages of the Hugging Face Transformers library 

2.9. Other Transformers Libraries. Comparison 

2.9.1. Comparison between the different Transformers libraries. 
2.9.2. Use of the other Transformers libraries 
2.9.3. Advantages of the other Transformers libraries 

2.10. Development of an NLP Application with RNN and Attention. Practical Application 

2.10.1. Development of a natural language processing application with RNN and attention
2.10.2. Use of RNN, attention mechanisms and Transformers models in the application
2.10.3. Evaluation of the practical application 

Module 3. Autoencoders, GANs, and Diffusion Models 

3.1. Efficient Data Representations 

3.1.1. Dimensionality Reduction 
3.1.2. Deep Learning 
3.1.3. Compact representations 

3.2. PCA realization with an incomplete linear automatic encoder 

3.2.1. Training process 
3.2.2. Python implementation 
3.2.3. Use of test data 

3.3. Stacked automatic encoders 

3.3.1. Deep Neural Networks 
3.3.2. Construction of coding architectures 
3.3.3. Use of regularization 

3.4. Convolutional autoencoders 

3.4.1. Design of convolutional models 
3.4.2. Convolutional model training 
3.4.3. Results Evaluation 

3.5. Automatic encoder denoising 

3.5.1. Application of filters 
3.5.2. Design of coding models 
3.5.3. Use of regularization techniques 

3.6. Sparse automatic encoders 

3.6.1. Increasing coding efficiency 
3.6.2. Minimizing the number of parameters 
3.6.3. Using regularization techniques 

3.7. Variational automatic encoders 

3.7.1. Use of variational optimization 
3.7.2. Unsupervised deep learning 
3.7.3. Deep latent representations 

3.8. Generation of fashion MNIST images 

3.8.1. Pattern recognition 
3.8.2. Image generation 
3.8.3. Training of deep neural networks 

3.9. Generative adversarial networks and diffusion models 

3.9.1. Content generation from images 
3.9.2. Modeling of data distributions 
3.9.3. Use of adversarial networks 

3.10. Implementation of the Models. Practical Application Practical Application 

3.10.1. Implementation of the models 
3.10.2. Use of real data 
3.10.3. Results Evaluation

Make the most of this opportunity to learn about the latest advances in this subject to apply it to your daily practice"