Enroll now in a degree that will help you create the most Advanced Deep Learning algorithms"

The progress in the field of Deep Learning has been significant in recent years thanks to the development of new techniques and methodologies that allow training deep learning models with higher performance and efficiency. Therefore, there is a great demand for highly trained professionals in this area to apply these techniques to innovative and challenging projects, so the computer scientist of today is facing a fantastic opportunity.

That is why this Postgraduate diploma in Advanced Deep Learning has been created, which consists of several thematic units that address the most relevant aspects of Deep Learning, from supervised learning to reinforcement learning and text generation. In addition, participants will have the opportunity to master advanced techniques such as the use of recurrent neural networks.

Likewise, the Postgraduate diploma in Advanced Deep Learning is taught online, allowing students to access the degree content anytime, anywhere. Similarly, the pedagogical methodology of Relearning focuses on autonomous and directed learning through the reiteration of concepts, boosting the educational progress of students. In addition, the program offers great flexibility in organizing academic resources, allowing students to adapt their learning to their specific schedules and needs.

Stand out with a Postgraduate diploma that will allow you to lay the foundations to replicate the success of AI companies such as OpenAI or DeepMind"

This Postgraduate diploma in Advanced 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 Advanced Deep Learning
• 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

Launch your career as a computer scientist creating advanced Deep Computer Vision models"

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.

You will be a reference when it comes to creating AI models that produce natural language with amazing quality"

You will undergo useful case studies that will enhance your skills to optimize an agent's policy"

The Postgraduate diploma in Advanced Deep Learning is an educational program that will provide students with a broad academic background, covering all the key aspects for the creation of the most advanced artificial neural network architectures and techniques such as Reinforcement Learning, key in well-known AI models such as ChatGPT. The curriculum is comprehensive and is complemented by a variety of innovative teaching resources available on the program's Virtual Campus.

A highly comprehensive curriculum that will provide you with the most global and up-to-date view of Advanced Deep Learning"

Module 1. Deep Computer Vision with Convolutional Neural Networks

1.1. The Cortex Visual Architecture

1.1.1. Functions of the Visual Cortex
1.1.2. Theories of computational vision
1.1.3. Models of image processing

1.2. Convolutional layers

1.2.1. Reuse of weights in convolution
1.2.2. 2D convolution
1.2.3. Activation Functions

1.3. Grouping layers and implementation of grouping layers with Keras

1.3.1. Pooling and Striding
1.3.2. Flattening
1.3.3. Types of Pooling

1.4. CNN Architecture

1.4.1. VGG Architecture
1.4.2. AlexNet architecture
1.4.3. ResNet Architecture

1.5. Implementation of a ResNet-34 CNN using Keras

1.5.1. Weight initialization
1.5.2. Input layer definition
1.5.3. Output definition

1.6. Use of pre-trained Keras models

1.6.1. Characteristics of pre-trained models
1.6.2. Uses of pre-trained models
1.6.3. Advantages of pre-trained models

1.7. Pre-trained models for transfer learning

1.7.1. Transfer learning
1.7.2. Transfer learning process
1.7.3. Advantages of transfer learning

1.8. Classification and Localization in Deep Computer Vision

1.8.1. Image Classification
1.8.2. Localization of objects in images
1.8.3. Object Detection

1.9. Object detection and object tracking

1.9.1. Object detection methods
1.9.2. Object tracking algorithms
1.9.3. Tracking and localization techniques

1.10. Semantic Segmentation

1.10.1. Deep learning for semantic segmentation
1.10.2. Edge Detection
1.10.3. Rule-based segmentation methods 

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. Reinforcement Learning

3.1. Optimization of rewards and policy search

3.1.1. Reward optimization algorithms
3.1.2. Policy search processes
3.1.3. Reinforcement learning for reward optimization

3.2. OpenAI

3.2.1. OpenAI Gym environment
3.2.2. Creation of OpenAI environments
3.2.3. Reinforcement Learning Algorithms in OpenAI

3.3. Neural network policies

3.3.1. Convolutional neural networks for policy search
3.3.2. Deep learning policies
3.3.3. Extending neural network policies

3.4. Stock evaluation: the credit allocation problem

3.4.1. Risk analysis for credit allocation
3.4.2. Estimating the profitability of loans
3.4.3. Credit evaluation models based on neural networks

3.5. Policy Gradients

3.5.1. Reinforcement learning with policy gradients
3.5.2. Optimization of policy gradients
3.5.3. Policy gradient algorithms

3.6. Markov decision processes

3.6.1. Optimization of Markov decision processes
3.6.2. Reinforcement learning for Markov decision processes
3.6.3. Models of Markov decision processes

3.7. Temporal difference learning and Q-Learning

3.7.1. Application of temporal differences in learning
3.7.2. Application of Q-Learning in learning
3.7.3. Optimization of Q-Learning parameters

3.8. Implementation of Deep Q-Learning and Deep Q-Learning variants

3.8.1. Construction of deep neural networks for Deep Q-Learning
3.8.2. Implementation of Deep Q-Learning
3.8.3. Variations of Deep Q-Learning

3.9. Reinforcement Learning Algorithms

3.9.1. Reinforcement Learning Algorithms
3.9.2. Reward Learning Algorithms
3.9.3. Punishment learning algorithms

3.10. Design of a Reinforcement Learning Environment. Practical Application

3.10.1. Design of a reinforcement learning environment
3.10.2. Implementation of a reinforcement learning algorithm
3.10.3. Evaluation of a reinforcement learning algorithm

You will only need a PC or Tablet to access educational content that is a reference in the specialization of Advanced Deep Learning techniques”