Master the future of technology with this Postgraduate diploma in Neural Networks and Deep Learning Training” 

Artificial Intelligence has become one of the most influential technologies today, and its use has spread to numerous sectors, from healthcare to manufacturing to retail. In this sense, the training of artificial neural networks is a fundamental component of AI and is essential for the development of complex algorithms that can learn and improve through experience.  

In this context, the Postgraduate diploma in Neural Networks and Deep Learning Training is a TECH program designed to provide practical skills in cutting-edge technologies, such as TensorFlow and Keras. Similarly, students will specialize in implementing advanced deep learning solutions in Python.  

In addition, the degree is designed to be 100% online, allowing students to complete the program according to their own schedule. The Relearning pedagogical methodology is also a highlight of the degree, as it focuses on experiential learning and practical problem solving to better internalize the concepts. Likewise, students will have great flexibility, with dynamic study resources that they can organize at their convenience.

Design and train complex neural network algorithms to solve real-world problems.What are you waiting for to enroll?”

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 eminently 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

Enroll in this Postgraduate diploma and boost skills in building deep learning models and advanced solutions for your projects”

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 programmed 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 the 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.

Delve into the world of deep learning and discover how Artificial Intelligence is transforming society"

Specialize by consulting dynamic case studies, interactive diagrams or in-depth videos on how to train artificial networks"

The Postgraduate diploma in Neural Networks and Deep Learning Training offers a comprehensive educational program that will take students on a broad academic journey: from neural network training to deep learning.

Computer Vision with convolutional neural networks. In addition, the curriculum is extremely detailed and is supported by a variety of innovative teaching resources that are available to students on the Virtual Campus of the degree.

A comprehensive curriculum with which you will master the reuse of pre-trained layers”

Module 1. Deep Neural Networks Training

1.1. Gradient Problems 

1.1.1. Gradient Optimization Techniques 
1.1.2. Stochastic Gradients 
1.1.3. Weight initialization techniques

1.2. Reuse of pre-trained layers

1.2.1. Learning transfer training 
1.2.2. Feature Extraction 
1.2.3. Deep Learning

1.3. Optimize

1.3.1. Stochastic gradient descent optimizers. 
1.3.2. Adam and RMSprop optimizers 
1.3.3. Moment optimizers

1.4. Learning rate scheduling

1.4.1. Automatic learning rate control 
1.4.2. Learning cycles 
1.4.3. Smoothing terms

1.5. Overfitting

1.5.1. Cross Validation 
1.5.2. Regularization 
1.5.3. Evaluation Metrics

1.6. Practical Guidelines

1.6.1. Model desing 
1.6.2. Selection of metrics and evaluation parameters 
1.6.3. Hypothesis testing

1.7. Transfer Learning

1.7.1. Learning transfer training 
1.7.2. Feature Extraction 
1.7.3. Deep Learning

1.8. Data Augmentation 

1.8.1. Image Transformations 
1.8.2. Synthetic data Generation  
1.8.3. Text transformation

1.9. Practical Application of Transfer Learning

1.9.1. Learning transfer training 
1.9.2. Feature Extraction 
1.9.3. Deep Learning

1.10. Regularization

1.10.1. L1 and L2 
1.10.2. Maximum entropy regularization 
1.10.3. Dropout

Module 2. Model customization and training with TensorFlow

2.1. TensorFlow

2.1.1. Using the TensorFlow Library 2.1.2. Model training with TensorFlow 
2.1.3. Operations with graphs in TensorFlow

2.2. TensorFlow and NumPy

2.2.1. NumPy computational environment for TensorFlow 
2.2.2. Using NumPy arrays with TensorFlow 
2.2.3. NumPy operations for TensorFlow graphs 

2.3. Model customization and training algorithms 

2.3.1. Building custom models with TensorFlow 
2.3.2. Management of training parameters 
2.3.3. Use of optimization techniques for training 

2.4. TensorFlow functions and graphs 

2.4.1. Functions with TensorFlow 
2.4.2. Use of graphs for model training 
2.4.3. Optimization of graphs with TensorFlow operations 

2.5. Data loading and preprocessing with TensorFlow 

2.5.1. Loading of datasets with TensorFlow 
2.5.2. Data preprocessing with TensorFlow 
2.5.3. Using TensorFlow tools for data manipulation

2.6. The tf.data API 

2.6.1. Using the tf.data API for data processing 
2.6.2. Constructing data streams with tf.data 
2.6.3. Use of the tf.data API for training models 

2.7. The TFRecord format 

2.7.1. Using the TFRecord API for Data Serialization 
2.7.2. Loading TFRecord files with TensorFlow 
2.7.3. Using TFRecord files for training models 

2.8. Keras preprocessing layers 

2.8.1. Using the Keras preprocessing API 
2.8.2. Construction of preprocessing pipelined with Keras 
2.8.3. Using the Keras preprocessing API for model training 

2.9. The TensorFlow Datasets project 

2.9.1. Using TensorFlow Datasets for data loading 
2.9.2. Data preprocessing with TensorFlow Datasets 
2.9.3. Using TensorFlow Datasets for Model Training 
2.10. Construction of a Deep Learning Application with TensorFlow. Practical Application 
2.10.1. Building a Deep Learning application with TensorFlow. 
2.10.2. Training a model with TensorFlow 
2.10.3. Use of the application for the prediction of results

Module 3. Deep Computer Vision with Convolutional Neural Networks

3.1. The Cortex Visual Architecture 

3.1.1. Functions of the Visual Cortex 
3.1.2. Theories of computational vision 
3.1.3. Models of image processing 

3.2. Convolutional layers 

3.2.1. Reuse of weights in convolution 
3.2.2. 2D convolution 
3.2.3. Activation Functions 

3.3. Grouping layers and implementation of grouping layers with Keras 

3.3.1. Pooling and Striding 
3.3.2. Flattening 
3.3.3. Types of Pooling 

3.4. CNN Architecture 

3.4.1. VGG Architecture 
3.4.2. AlexNet architecture 
3.4.3. ResNet Architecture 

3.5. Implementation of a ResNet-34 CNN using Keras 

3.5.1. Weight initialization 
3.5.2. Input layer definition 
3.5.3. Output definition 

3.6. Use of pre-trained Keras models 

3.6.1. Characteristics of pre-trained models 
3.6.2. Uses of pre-trained models 
3.6.3. Advantages of pre-trained models 

3.7. Pre-trained models for transfer learning 

3.7.1. Transfer learning 
3.7.2. Transfer learning process 
3.7.3. Advantages of transfer learning 

3.8. Classification and Localization in Deep Computer Vision 

3.8.1. Image Classification 
3.8.2. Localization of objects in images 
3.8.3. Object Detection 

3.9. Object detection and object tracking 

3.9.1. Object detection methods 
3.9.2. Object tracking algorithms 
3.9.3. Tracking and localization techniques 

3.10. Semantic Segmentation 

3.10.1. Deep learning for semantic segmentation 
3.10.2. Edge Detection 
3.10.3. Rule-based segmentation methods

Take the opportunity to get up to speed on the creation of object detection and tracking algorithms”