Thanks to this 100% online Postgraduate certificate, you will master the fundamentals of Deep Learning and design the most efficient architectures for specific tasks such as sentiment analysis"

Deep Learning is so versatile and offers so many applications that it has become one of the most relevant technologies today. In this sense, professionals use Deep Learning tools to better understand customer behavior and adapt their marketing strategies in order to build customer loyalty.  In addition, these models are used to predict consumer preferences based on aspects such as purchase history, website navigation and even ad clicks. In this way, specialists personalize product recommendations and offers for each individual, optimizing their experience while companies increase their conversion rates. 

In this scenario, TECH develops a pioneering program in Mathematical Basis of Deep Learning. Thanks to this program, developers will gain a solid understanding of Deep Learning algorithms and implement them to neural network models. The curriculum will delve into essential concepts such as derivatives of linear functions, Backward Pass and parameter optimization. The syllabus will also focus on the use of Supervised Learning machines. Students will nurture their practice with the most innovative models to be used in procedures with labeled data.  The syllabus will also emphasize the importance of model training, offering advanced techniques including Online Learning. Thanks to this, graduates will ensure that their devices learn from the data in order to perform activities accurately. 

Moreover, the program features the revolutionary Relearning methodology, based on the reiteration of key content and experience, offering simulation cases for a direct approach of professionals with the current challenges in Deep Learning. Students will enjoy a variety of didactic materials in different formats such as interactive videos, complementary readings and practical exercises.

You will master the Batch Learning approach at the world's best digital university according to Forbes"  

This Postgraduate certificate in Mathematical Basis of 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 Mathematical Basis of 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 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 master Decision Tree models to effectively solve you a variety of classification problems in different areas"

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.  

Do you want to specialize in hyperparameter adjustment? Achieve it with this program in only 300 hours"

With the Relearning system you will focus on the most relevant concepts without having to invest a large amount of study hours"

By means of 300 teaching hours, this degree will offer students a deep analysis of the Mathematical Basis of Deep Learning. After delving into key concepts ranging from functions to derivatives, the curriculum will focus on the Backward Pass. stage. This will allow students to adjust the weights of the neural network and improve the performance of the model during the program. Likewise, the syllabus will analyze the different systems of Supervised Learning taking into account factors such as linear regression or optimization methods. In this sense, the program will provide advanced regularization techniques. 

You will enrich your professional practice with the most cutting-edge Evaluation Metrics and you will evaluate the effectiveness of neural network models in specific tasks”

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

This academic itinerary is exclusive to TECH and you will be able to develop it at your own pace thanks to its 100% online Relearning methodology"