You will master the principles of Deep Learning and generate the most accurate predictions with this 100% online Postgraduate diploma"

Neural Networks are the fundamental basis of Deep Learning. Inspired by the functioning of the human brain and composed of neurons, these systems provide the computational foundation for machines to learn from data efficiently and automatically. In this way, they perform complex tasks with similar or even better performance than humans in multiple tasks such as machine translation or the analysis of large data sets. However, these tools still face several challenges that limit their effectiveness and applicability in certain areas. It is therefore the responsibility of experts to update their knowledge frequently in order to keep abreast of all developments in this field and to incorporate them into their practice in order to optimize their procedures. 

In this context, TECH creates a Postgraduate diploma that will offer a solid understanding of how Deep Learning works, as well as the most advanced tools to build Neural Networks. The curriculum will range from key mathematical fundamentals (such as functions or derivatives) to the principles of Supervised Learning (including different models, evaluation metrics and hyperparameter selection). The syllabus will also focus on the numerous utilities of Deep Learning, so that graduates will be aware of the current situation of the labor market and multiply their chances of success in fields such as automotive, computer science, biology or finance. It should be noted that the university degree will include the analysis of real cases in simulated learning environments. Students will learn valuable lessons that they will incorporate into their procedures to ensure their viability.  

To consolidate all these contents, TECH uses the innovative methodology of Relearning. This is based on constant feedback and adaptation to the individual needs of the students based on targeted repetition. With any electronic device with Internet access, students will be able to access the Virtual Campus and get the most complete didactic contents in the educational market.  

Do you want to specialize in the use of Supervised Learning Machines? Get it through 450 hours of the best digital teaching"

This Postgraduate diploma 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 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 self-assessment can be used 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 world of deep learning algorithms and acquire technical knowledge that will allow you to excel in the area of Social Sciences"

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 delve into the architecture of Neural Networks and their different types to solve everyday problems through Deep Learning"

A complete syllabus that incorporates all the knowledge you need to take a step towards the highest quality in Computer Vision"

The Postgraduate diploma is designed to provide students with a comprehensive view of the various applications of Deep Learning. For this reason, the academic itinerary will cover from its mathematical principles to the training of deep neural networks. Furthermore, the curriculum will focus on the evaluation of Deep Learning models and the visualization of results. During the training, students will acquire advanced skills that will allow them to effectively implement the multilayer perceptron with Keras. In this way, graduates will perform learning tasks in different domains and carry out a variety of data processing tasks. 

In just 6 months, you will be able to develop from start to finish a complete Neural Network"

Module 1. Mathematical Basis of Deep Learning

1.1. Functions and Derivatives

1.1.1. Linear Functions
1.1.2. Partial Derivative
1.1.3. Higher Order Derivatives

1.2. Multiple Nested Functions

1.2.1. Compound Functions
1.2.2. Inverse Functions
1.2.3. Recursive Functions

1.3. Chain Rule

1.3.1. Derivatives of Nested Functions
1.3.2. Derivatives of Compound Functions
1.3.3. Derivatives of Inverse Functions

1.4. Functions with Multiple Inputs

1.4.1. Multi-variable Functions
1.4.2. Vectorial Functions
1.4.3. Matrix Functions

1.5. Derivatives of Functions with Multiple Inputs

1.5.1. Partial Derivative
1.5.2. Directional Derivatives
1.5.3. Mixed Derivatives

1.6. Functions with Multiple Vector Inputs

1.6.1. Linear Vector Functions
1.6.2. Non-linear Vector Functions
1.6.3. Matrix Vector Functions

1.7. Creating New Functions from Existing Functions

1.7.1. Addition of Functions
1.7.2. Product of Functions
1.7.3. Composition of Functions

1.8. Derivatives of Functions with Multiple Vector Entries

1.8.1. Derivatives of Linear Functions
1.8.2. Derivatives of Nonlinear Functions
1.8.3. Derivatives of Compound Functions

1.9. Vector Functions and their Derivatives: A Step Further

1.9.1. Directional Derivatives
1.9.2. Mixed Derivatives
1.9.3. Matrix Derivatives

1.10. The Backward Pass

1.10.1. Error Propagation
1.10. 2 Application of Update Rules
1.10.3. Parameter Optimization

Module 2. Deep Learning Principles

2.1. Supervised Learning

2.1.1. Supervised Learning Machines
2.1.2. Uses of Supervised Learning
2.1.3. Differences between Supervised and Unsupervised Learning

2.2. Supervised Learning Models

2.2.1. Linear Models
2.2.2. Decision Tree Models
2.2.3. Neural Network Models

2.3. Linear Regression

2.3.1. Simple Linear Regression
2.3.2. Multiple Linear Regression
2.3.3. Regression Analysis

2.4. Model Training

2.4.1. Batch Learning
2.4.2. Online Learning
2.4.3. Optimization Methods

2.5. Model Evaluation: Training Set vs. Test Set

2.5.1. Evaluation Metrics
2.5.2. Cross Validation
2.5.3. Comparison of Data Sets

2.6. Model Evaluation: The Code

2.6.1. Prediction Generation
2.6.2. Error Analysis
2.6.3. Evaluation Metrics

2.7. Variables Analysis

2.7.1. Identification of Relevant Variables
2.7.2. Correlation Analysis
2.7.3. Regression Analysis

2.8. Explainability of Neural Network Models

2.8.1. Interpretable Models
2.8.2. Visualization Methods
2.8.3. Evaluation Methods

2.9. Optimization

2.9.1. Optimization Methods
2.9.2. Regularization Techniques
2.9.3. The Use of Graphs

2.10. Hyperparameters

2.10.1. Selection of Hyperparameters
2.10.2. Parameter Search
2.10.3. Hyperparameter Tuning

Module 3. Neural Networks, the Basis of Deep Learning

3.1. Deep Learning

3.1.1. Types of Deep Learning
3.1.2. Applications of Deep Learning
3.1.3. Advantages and Disadvantages of Deep Learning

3.2. Operations

3.2.1. Sum
3.2.2. Product
3.2.3. Transfer

3.3. Layers

3.3.1. Input Layer
3.3.2. Cloak
3.3.3. Output Layer

3.4. Union of Layers and Operations

3.4.1. Architecture Design
3.4.2. Connection between Layers
3.4.3. Forward Propagation

3.5. Construction of the First Neural Network

3.5.1. Network Design
3.5.2. Establish the Weights
3.5.3. Network Training

3.6. Trainer and Optimizer

3.6.1. Optimizer Selection
3.6.2. Establishment of a Loss Function
3.6.3. Establishing a Metric

3.7. Application of the Principles of Neural Networks

3.7.1. Activation Functions
3.7.2. Backward Propagation
3.7.3. Parameter Adjustment

3.8. From Biological to Artificial Neurons

3.8.1. Functioning of a Biological Neuron
3.8.2. Transfer of Knowledge to Artificial Neurons
3.8.3. Establish Relations between the Two

3.9. Implementation of MLP (Multilayer Perceptron) with Keras

3.9.1. Definition of the Network Structure
3.9.2. Model Compilation
3.9.3. Model Training

3.10. Fine Tuning  Hyperparameters of Neural Networks

3.10.1. Selection of the Activation Function
3.10.2. Set the Learning Rate
3.10.3. Adjustment of Weights

You have a wide range of learning resources You will 24 hours a day, 7 days a week"