You will apply to your projects the most innovative techniques of Deep Learning thanks to this professional master’s degree 100% online" 

TensorFlow has become the most important tool for implementing and learning Deep Learning models. Developers use both its variety of tools and libraries to specialize models that perform automatic object detection, classification and natural language processing tasks. Along the same lines, this platform is useful for detecting anomalies in data, which is essential in areas such as cyber security, predictive maintenance and quality control. However, its use can involve a number of challenges for professionals, including the selection of the appropriate neural network architecture. 

Faced with this situation, TECH implements a professional master’s degree that will provide experts with a comprehensive approach to Deep Learning. Developed by experts in the field, the curriculum will delve into the mathematical foundations and principles of Deep Learning. This will enable graduates to build Neural Networks aimed at information processing involving pattern recognition, decision making and learning from data. In addition, the syllabus will delve deeper into Reinforcement Learning , taking into account factors such as reward optimization and policy search. In addition, the teaching materials will offer advanced optimization techniques and visualization of results. 

As for the format of the university degree, it is delivered through a 100% online methodology so that graduates can complete the program with ease. To access the academic content they will only need an electronic device with Internet access, since the schedules and evaluation chronograms are planned on an individual basis. On the other hand, the syllabus will be supported by the innovative Relearningteaching innovative system, of which TECH is a pioneer. This learning system consists of the reiteration of key aspects to guarantee the mastery of its different aspects.

Study through innovative multimedia didactic formats that will optimize your Deep Learning update process"

This professional master’s degree in Deep Learning contains the most complete and up-to-date program on the market. The most important features include:

• Practical cases studies are presented by experts in Data Engineer and Data Scientist
• The graphic, schematic and practical contents of the book provide technical 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

Looking to enrich your practice with the most advanced gradient optimization techniques? Achieve it with this program in just 12 months"

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational 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 Backward Pass to calculate the gradients of the loss function with respect to the parameters of the network"

Thanks to the Relearning methodology, you will be free to plan both your study schedules and educational timelines"

Thanks to this professional master’s degree, graduates will develop their skills and knowledge in the field of Deep Learning and Artificial Intelligence. In this way, they will implement the most advanced Deep Learning techniques in their projects to improve the performance of models in specific tasks. Likewise, experts will be able to develop intelligent systems that can automatically perform tasks such as pattern recognition in images, sentiment analysis in text or anomaly detection in data.

A university degree designed based on the latest trends in Deep Learning to guarantee you a successful learning"

General Objectives

• Fundamentalize the key concepts of mathematical functions and their derivatives
• Apply these principles to deep learning algorithms to learn automatically
• Examine the key concepts of Supervised Learning and how they apply to neural network models
• Analyze the program, evaluation and analysis of neural network models
• Fundamentals of the key concepts and main applications of deep learning
• Implement and optimize neural networks with Keras
• Develop expertise in the learning of deep neural networks
• Analyze the optimization and regularization mechanisms required for deep neural network training

Specific Objectives

Module 1. Mathematical Basis of Deep Learning

• Develop the chain rule for calculating derivatives of nested functions.
• Analyze how to create new functions from existing functions and how to compute the derivatives of these functions
• Examine the concept of Backward Pass and how derivatives of vector functions are applied to automatic learning
• Learn how to use TensorFlow to build custom models
• Understand how to load and process data using TensorFlow tools
• Fundamentalize the key concepts of NLP natural language processing with RNN and attention mechanisms
• Explore the functionality of Hugging Face transformer libraries and other natural language processing tools for application to vision problems
• Learn how to build and learn autoencoder models, GANs, and diffusion models
• Understand how autoencoders can be used to efficiently encode data

Module 2. Deep Learning Principles

• Analyze how linear regression works and how it can be applied to neural network models
• Understand the rationale for optimizing hyperparameters to improve the performance of neural network models
• Determine how the performance of neural network models can be evaluated using the learning set and the test set

Module 3. Neural Networks, the Basis of Deep Learning

• Analyze the architecture of neural networks and their principles of operation
• Determine how neural networks can be applied to a variety of problems
• Establish how to optimize the performance of deep learning models by tuning hyperparameters

Module 4. Deep Neural Networks Training

• Analyze gradient problems and how they can be avoided
• Determine how to reuse pre-trained layers to train deep neural networks
• Establish how to schedule the learning rate to get the best results

Module 5. Model Customization and Training with TensorFlow

• Determine how to use the TensorFlow API to define custom functions and graphicsand custom graphs
• Fundamentally use the tf.data API to load and preprocess data efficiently.
• Discuss the TensorFlow Datasets project and how it can be used to facilitate access to preprocessed datasets.

Module 6. Deep Computer Vision with Convolutional Neural Networks

• Explore and understand how convolutional and clustering layers work for Visual 
• Cortex architecture
• Develop CNN architectures with Keras
• Use pre-trained Keras models for object classification, localization, detection, and tracking, as well as semantic segmentation

Module 7. Processing Sequences using RNN and CNN

• Analyze the architecture of recurrent neurons and layers
• Examine the various training algorithms for training RNN models
• Evaluating the performance of RNN models using accuracy and sensitivity metrics

Module 8. NLP Natural Language Processing with RNN and Attention

• Generating text using recurrent neural networks
• Train an encoder-decoder network to perform neural machine translation
• Develop a practical application of natural language processing with RNN and attention

Module 9. Autoencoders, GANs, and Diffusion Models

• Implementing PCA techniques with an incomplete linear autoencoder
• Use convolutional and variational autoencoders to improve the performance of autoencoders
• Analyze how GANs and diffusion models can generate new and realistic images

Module 10. Reinforcement Learning

• Use gradients to optimize an agents policy.
• Evaluate the use of neural networks to improve the accuracy of an agent when making decisions
• Implement different boosting algorithms to improve the performance of an agent.

A key, unique and decisive training experience that will propel your professional development"