TECH's exclusive Relearning system will allow you to update your knowledge and skills on Visual Cortex Architecture in the most rigorous way"

There are an infinite number of resources for the development and training of Neural Network models. In this regard, Keras has become the most widely used by practitioners due to its ease of use, flexibility and compatibility with other libraries. This open source library provides developers with a high-level Application Programming Interface, which allows them to build Deep Learning models with immediacy. In this way, it contributes to the creation of image classification models that can identify different objects in snapshots. This is beneficial for aspects such as face recognition systems, medical image classification or the creation of generative art. 

In this context, TECH implements a postgraduate diploma that will focus on Deep Computer Vision with Convolutional Neural Networks. For this purpose, the academic itinerary will delve into the Transfer Learning Training by attending to factors such as weight initialization techniques and smoothing terms. In this way, the graduates will take advantage of the previous knowledge acquired by pre-trained models to improve the performance of new machine learning tasks. The program will also address the construction of a Deep Learning application using TensorFlow and NumPy. This will enable professionals to use these tools in an optimal way in areas such as performance prediction. 

The university program will be taught 100% online, on a virtual study platform that is not governed by predefined schedules. Far from this, each student will have the opportunity to self-manage their progress in a personalized way at any time of the 24 hours a day. One of the most distinctive features of the methodology is the learning process that TECH uses in all its programs:Relearning. Also, you will have Relearning, an innovative study methodology that will promote the acquisition of skills in a fast and flexible way from the analysis of theoretical content and exemplification of cases based on the real context of profession.

You will be equipped to contribute to the advancement of knowledge in the area of Deep Learning through research and development of new algorithms"

This postgraduate diploma in Neural Networks and Deep Learning Training 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 Neural Networks and Deep Learning Training
• 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 optimize your skills in the design of Deep Learning models to generate effective solutions to projects”

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.  

You will master the NumPy computational environment and perform numerical operations on multidimensional arrays"

You will have access to a multimedia library full of dynamic content that will enhance your learning in a more visual way"

With this immersive learning experience, graduates will have at their disposal a plurality of knowledge and skills to experience a quality leap in the field of Deep Learning Professionals will have a solid knowledge of Deep Neural Network training, so they will apply its principles to algorithms effectively.  In addition, they will master advanced software such as TensorFlow, designed to create different neural network architectures quickly and easily. All this will allow them to design and execute advanced Artificial Intelligence projects to stand out in an ever-expanding technological sector. 

In just 6 months, you will give your career the technological boost it needs and offer innovative solutions to Gradient's problems"

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 training, 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 training of deep neural networks
• Analyze the optimization and regularization mechanisms required for deep neural network training

Specific Objectives

Module 1. 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 2. 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 efficiently
• Discuss the TensorFlow Datasets project and how it can be used to facilitate access to preprocessed datasets

Module 3. 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

The didactic material of this degree will lead you to delve in a more visual way into the knowledge of Transfer Learning"