Revolutionize the technology sector thanks to this professional master’s degree in Deep Learning”

The rapid technological evolution of recent years has meant that the self-driving vehicle, the early diagnosis of serious illnesses through high-precision imaging devices or facial recognition with mobile applications are not so far away. Thus, at present, these emerging innovations seek to improve the precision of automatisms and improve the quality of the results obtained.

A scenario, where the IT professional who must have exhaustive knowledge about Deep Learning plays a determining role, being also able to take another step in this race in the sector to create authentic Artificial Intelligence. For this reason, TECH has created this 12-month Master's Degree with the most advanced and current syllabus, prepared by true experts in this field.

A program with a theoretical-practical perspective that will lead students to acquire intensive learning about mathematical fundamentals, the construction of neural networks, model customization, and training with TensorFlow. A breadth of content that will be much easier to assimilate thanks to the video summaries of each topic, the videos in focus the specialized readings and the case studies. Likewise, with the Relearningsystem, used by TECH, the computer scientist will progress more naturally through this program, consolidating the new concepts more easily, thus reducing the long hours of study.

A university education that focuses on the knowledge that will make the student grow professionally, who also wants to make a first-level academic option compatible with their daily activities. And it is that all you need is a digital device with an internet connection to access this degree at the academic forefront at any time.

Succeed with your AI projects in sectors such as the automotive, finance or medical sectors with the teaching provided by TECH”

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:

• The development of practical cases 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 self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions for 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

Delve whenever you want into the Hugging Face transformer libraries and other natural language processing tools to apply to vision problems”

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 designed 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 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 have an advanced agenda in Deep Learning, 24 hours a day, from any digital device with an internet connection"

A 12-month professional master’s degree with the application of Deep Learning techniques in real problems"

The students who take this proposal of 1,500 teaching hours will have the opportunity to acquire learning that increases their options for progression in the technology sector, more specifically in the development of AI. In order for the graduate to reach this goal more easily, this academic institution provides innovative pedagogical tools that are easily accessible and also an excellent faculty that will answer any questions they may have during this teaching process at the highest level.

You will gain strong analytical, problem-solving, and algorithm-building skills to hone Artificial Intelligence”

General Objectives

• Fundamental 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
• Discuss the training, evaluation, and analysis of neural network models
• Provide a foundation for the key concepts and main applications of deep learning
• Implement and optimize neural networks with Keras
• Develop specialized knowledge about training deep neural networks
• Analyze the optimization and regularization mechanisms necessary for the training of deep networks

Specific Objectives

Module 1. Deep Learning Fundamentals

• Develop the chain rule to calculate derivatives of nested functions
• Analyze how new functions are created from existing functions and how their derivatives are computed
• Examine the concept of the Backward Pass and how derivatives of vector functions are applied to learn automatically
• Learn about how to use TensorFlow to build custom models
• Understand how to load and process data using TensorFlow tools
• Ground the key concepts of NLP natural language processing with RNN and attention mechanisms
• Explore the functionality of the Hugging Face transformer libraries and other natural language processing tools to apply to vision problems
• Learn to build and train models of autoencoders, GANs and diffusion models
• Understand how autoencoders can be used to efficiently encode data

Module 2. Deep Learning Principles

• Analyze the operation of linear regression and how it can be applied to neural network models
• Fundamental hyperparameter optimization to improve the performance of neural network models
• Determine how the performance of neural network models can be evaluated by using the training set and the test set

Module 3. Neural networks, the basis of Deep Learning

• Analyze the architecture of neural networks and their operating principles
• 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. Training of Deep Neural Networks

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

Module 5. Customization of Models and training with TensorFlow

• Determine how to use the TensorFlow API to define custom graphs and functions
• Fundamental use of 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 the convolutional and pooling layers work for the Visual Cortex architecture
• Develop CNN architectures with Keras
• Use pre-trained Keras models for object classification, location, detection and tracking, as well as semantic segmentation

Module 7. Processing sequences using RNN (Recurrent Neural Networks) and CNN (Convolutional Neural Networks)

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

Module 8. Natural Language Processing (NLP) with Recursive Natural Networks (RNN) and Attention

• Generate text using recurrent neural networks
• Training 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

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

Module 10. Reinforcement Learning

• Using gradients to optimize an agent's policy
• Evaluate the use of neural networks to improve the accuracy of an agent when making decisions
• Implement different reinforcement algorithms to improve the performance of an agent

TECH adapts to your professional needs and motivations, which is why it has designed the most complete and flexible program on Deep Learning”