You will be able to download all the content to any electronic device from the Virtual Campus and consult it whenever you need it, even without an Internet connection” 

The impact of Deep Learning on improving the efficiency and accuracy of systems is undeniable, and is being reflected in a wide variety of fields, from medicine to transportation to security. The applications are numerous, including computer-aided medical diagnosis, autonomous vehicle driving, security system anomaly detection and product supply chain optimization. As new Deep Learning techniques continue to be researched and developed, a wide range of possibilities in complex problem solving and real-time decision making are opening up. 

As a result, the demand for professionals who know how to apply Deep Learning continues to increase, and the trend is expected to continue in the future. To summarize, studying Deep Learning applications can be a solid option due to its growing demand in various industries, its ability to improve the efficiency and accuracy of systems, its wide variety of applications, the resources and support communities available, and the employment opportunities and competitive salaries in the field. 

This program designed by TECH is based on the Relearning methodology to facilitate the student's learning through the progressive and natural repetition of the fundamental concepts. This way, the graduate will acquire the necessary competencies by adjusting the study to their life style. In addition, the completely online format will allow the professional to focus on their learning, without the need to travel or adjust to a fixed schedule, and to access the theoretical and practical content from anywhere and at any time using a device with an Internet connection. 

Take advantage of the unique opportunity for professional and personal growth offered exclusively by this TECH Postgraduate diploma” 

This Postgraduate diploma in Deep Learning Applications 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 Deep Learning 
• The graphic, schematic, and practical contents with which they are created, provide practical information on the 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 work
• Content that is accessible from any fixed or portable device with an Internet connection

A Postgraduate diploma that provides you with resources and strategies for you to implement PCA techniques with an automatic linear encoder effectively and, in addition, 100% online!”

The program includes in its teaching staff professionals from the sector who bring to this program the experience of their work, as well as recognized 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. This will be done with the help of an innovative system of interactive videos made by renowned experts. 

Enroll now and you will be able to generate texts using recurrent neural networks thanks to the skills you will acquire with this Postgraduate diploma"

You will have at your disposal a Virtual Campus available 24 hours a day, without the usual pressure of adapting to rigid academic calendars or unchangeable class schedules"

Through the Relearning method, the engineer will be able to obtain advanced and effective learning on the codification of deep learning models throughout their academic path. This method is based on the continuous reiteration of key concepts, which will allow them to achieve their goal without having to spend large amounts of time studying. With this approach, the engineer will be able to delve into a complete syllabus on the subject in question. 

In addition to a prestigious teaching team, we offer the most cutting-edge content in the digital academic panorama and the most effective methodology on the market. Don't wait to become an elite professional and access endless job opportunities” 

Module 1. Processing Sequences using RNNs (Recurrent Neural Networks) and CNNs (Convolutional Neural Networks) 

1.1. Recurrent Neurons and Layers 

1.1.1. Types of Recurrent Neurons 
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. Hyperparameters Adjustment 

1.4. Prerenal RNNs 

1.4.1. Prenetrated Networks 
1.4.2. Transfer of Learning 
1.4.3. Fine Tuning 

1.5. Time Series Forecasting 

1.5.1. Statistical Models for Forecasting 
1.5.2. Time Series Models 
1.5.3. Neural Network-Based Models 

1.6. Interpretation of the Time Series Analysis Results 

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

1.7. Management 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 Classic Results 

1.10.1. Classic vs. RNN Methods 
1.10.2. Classic vs. CNN Methods 
1.10.3. Difference in Training Time 

Module 2. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) 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. Creation of the Training Dataset 

2.2.1. Preparation of the Data for RNN Training 
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 Topics in Comments 
2.3.3. Sentiment Analysis with Deep Learning Algorithms 

2.4. Encoder-Decoder Network for Neural Machine Translation 

2.4.1. Training a 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 a RNN 

2.5. Attention Mechanisms 

2.5.1. Application of Attention Mechanisms in RNN 
2.5.2. Use of Attention 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 Pre-Processing 
2.7.3. Transformer Model Training for Vision 

2.8. Hugging Face Transformer Library 

2.8.1. Use of 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 of the Different Transformers Libraries 
2.9.2. Use of the Other Transformers Libraries 
2.9.3. Advantages of 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. Assessment 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 Performance 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 Autocoders 

3.4.1.  Convolutional Model Design 
3.4.2. Convolutional Model Training 
3.4.3. Results Evaluation 

3.5. Noise Elimination of Automatic Encoders 

3.5.1. Filter Application 
3.5.2. Coding Model Design 
3.5.3. Use of Regularization Techniques 

3.6. Dispersed Automatic Encoders 

3.6.1. Increasing Coding Efficiency 
3.6.2. Minimizing the Parameter Number 
3.6.3. Use of 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 Trend MNIST Images 

3.8.1. Pattern Recognition 
3.8.2. Image Generation 
3.8.3. Deep Neural Network Training 

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. Models implementation. Practical Application 

3.10.1. Models Implementation 
3.10.2. Use of Real Data 
3.10.3. Results Evaluation  

A unique curriculum designed to help you acquire advanced skills in Deep Learning Applications"