Thanks to this Postgraduate diploma, you will apply to your projects the most advanced optimization methods to train Deep Neural Networks"

Natural Language Processing through Deep Learning has completely revolutionized the way computers understand and generate human language. This technology has a wide range of applications, from automating text-based tasks to improving online security. One of the fields in which these resources are most widely used is in commercial enterprises. In this way, businesses include virtual assistants such as chatbots in their web platforms to resolve consumer questions in real time. Therefore, Deep Learning contributes to provide relevant answers based on the content of large databases.

In this context, TECH implements a Postgraduate diploma that will deal thoroughly with Language Processing with Natural Recurrent Networks. Designed by experts in this subject, the curriculum will analyze the keys to the creation of the training dataset. In this sense, the steps to follow for the students to perform a correct cleaning and transformation of the information will be analyzed.  The agenda will also delve into sentiment analysis with algorithms to detect emerging opinions and trends. Furthermore, the program will address the construction of

OpenAi environments for graduates to develop and evaluate reinforcement learning algorithms.  

The program's methodology will reflect the need for flexibility and adaptation to contemporary professional demands. With a 100% online format, it will allow students to advance their learning without compromising their job responsibilities. In addition, the application of the Relearning system, based on the reiteration of key concepts, ensures a deep and lasting understanding. This pedagogical approach reinforces the ability of professionals to effectively apply the knowledge acquired in their daily practice. At the same time, the only thing students will only need a device with Internet access to complete this academic itinerary.

You will master the Architecture of the Visual Cortex and be able to reconstruct three-dimensional models of objects in only 6 months with this course"  

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 graphical, schematic and practical contents with which it is conceived gather technological 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 be able to create Artificial Intelligence models with top-notch natural language"

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.  

With the interactive summaries of each topic, you will consolidate in a more dynamic way the concepts of 2D Seizure"

The Relearning methodology, of which TECH is a pioneer, will guarantee you a gradual and natural learning process"

This program will immerse students in the creation of Artificial Neural Network architectures. The curriculum will delve into Deep Computer Vision, taking into account image processing models. In addition, the syllabus will delve into object tracking algorithms through different tracking and localization techniques. Moreover, students will acquire a solid understanding of natural language processing to automate activities such as translation and coherent text production. Developers will handle the OpenAi Gym platform for the development, evaluation and research of reinforcement learning algorithms. 

You will maximize your skills by analyzing real cases and solving complex situations in simulated learning environments"

Module 1. Deep Computer Vision with Convolutional Neural Networks

1.1. The Visual Cortex 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. Deep Computer Vision Classification and Localization

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 (NRN) 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. Using TransformerModels for Natural Language Processing
2.6.2. Application of Transformer Models for Vision
2.6.3. Advantages of Transformer Models

2.7. Transformers for Vision

2.7.1. Use of Transformer Models for Vision
2.7.2. Image Data Preprocessing
2.7.3. Training a Transform 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 different TransformersLibraries
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 ofQ-LearningParameters

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. Reinforment 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 have access to the most comprehensive learning materials in academia, available in a variety of multimedia formats to optimize your learning"