You will master the most innovative Deep Learning  tools, such as TensorFlow, to enrich your projects thanks to this Postgraduate certificate"

Thanks to the constant technological evolution driven by Industry 4.0, Artificial Intelligence is impacting all sectors to improve the quality of life of citizens. In this sense, experts use Deep Learning mechanisms to design new medicines, optimize energy consumption and even adapt educational content for students according to their specific needs. However, this discipline requires professionals to frequently update their knowledge in order to incorporate the most advanced techniques into their daily practice. It is also essential that they acquire new skills for the correct handling of emerging technologies.  

For this reason, TECH is launching a revolutionary Postgraduate certificate in Deep Learning that will delve into all the advances that have been made in this area and will provide students with a wide range of resources to optimize their work projects. The curriculum will delve into the construction of Neural Networks, so that graduates can solve a variety of complex problems in areas such as computer vision or natural language processing. In line with this, the syllabus will explore fundamental concepts such as linear regression, perceptron, loss function, regularization and normalization. In addition, the learning materials will provide advanced tools for visualizing results. In this way, graduates will optimize the performance of machine learning models. 

As for the program's methodology, TECH uses its revolutionary Relearning teaching method. Through the gradual repetition of the fundamental concepts, the students will acquire all the knowledge in a natural way. They will not have to resort to techniques that require extra effort, such as memorization. In this sense, the only thing students will need to access the Virtual Campus is an electronic device with Internet access (their own smartphone, computer or Tablet). 

You will use the Confusion Matrix to get a detailed view of model performance and identify areas of improvement for optimization”

This Postgraduate certificate in 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 deep learning
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the 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 delve into the evaluation metrics of Neural Networks to assess their performance and check that they perform their tasks efficiently"

The program’s teaching staff includes professionals from the industry 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.   

Looking to renew your knowledge in the field of Machine Learning? With this program, you will stay at the forefront of it in just 6 weeks"

You will position yourself in the job market thanks to this 100% online university program, which adapts to your needs and provides you with a solid learning experience"

Thanks to this university program, graduates will acquire a comprehensive view of Deep Learning, one of the most important branches of Artificial Intelligence. The curriculum will thoroughly analyze the use of Neural Networks, paying attention to processes such as Forward propagation and activation functions. The students will be able to calculate the outputs of the systems from the provided inputs, introducing nonlinearities in the archetype. On the other hand, the syllabus will provide the keys for graduates to correctly handle tools such as Caffe, Keras or Pytorch to train deep learning models. 

You will implement the most innovative Machine Learning tools in your daily practice, developing algorithms to provide personalized recommendations"

Module 1. Deep Learning

1.1. Artificial Intelligence

1.1.1. Machine Learning
1.1.2. Deep Learning
1.1.3. The Explosion of Deep Learning Why Now

1.2. Neural Networks

1.2.1. The Neural Network
1.2.2. Uses of Neural Networks
1.2.3. Linear Regression and Perception
1.2.4. Forward Propagation
1.2.5. Backpropagation
1.2.6. Feature Vectors

1.3. Loss Functions

1.3.1. Loss Functions
1.3.2. Types of Loss Functions
1.3.3. Choice of Loss Functions

1.4. Activation Functions

1.4.1. Activation Function
1.4.2. Linear Functions
1.4.3. Non-Linear Functions
1.4.4. Output vs. Hidden Layer Activation Functions

1.5. Regularization and Normalization

1.5.1. Regularization and Normalization
1.5.2. Overfitting and Data Augmentation
1.5.3. Regularization Methods: L1, L2 and Dropout
1.5.4. Normalization Methods: Batch, Weight, Layer

1.6. Optimization

1.6.1. Gradient Descent
1.6.2. Stochastic Gradient Descent
1.6.3. Mini Batch Gradient Descent
1.6.4. Momentum
1.6.5. Adam

1.7. Hyperparameter Tuning and Weights

1.7.1. Hyperparameters
1.7.2. Batch Size vs Learning Rate vs Step Decay
1.7.3. Weights

1.8. Evaluation Metrics of a Neural Network

1.8.1. Accuracy
1.8.2. Dice Coefficient
1.8.3. Sensitivity vs Specificity / Recall vs precision
1.8.4. ROC Curve (AUC)
1.8.5. F1-Score
1.8.6. Matrix Confusion
1.8.7. Cross-Validation

1.9. Frameworks and Hardware

1.9.1. Tensor Flow
1.9.2. Pytorch
1.9.3. Caffe
1.9.4. Keras
1.9.5. Hardware for the Learning Phase

1.10. Creation of a Neural Network. Training and Validation

1.10.1. Dataset
1.10.2. Network Construction
1.10.3. Education
1.10.4. Visualization of Results

Practical exercises based on real cases and videos in detail elaborated by the teachers themselves will be the key to your success in this Postgraduate certificate"