Introducción al Aprendizaje Profundo (Coursera)

Offered by Universidad Austral,
Introducción al Aprendizaje Profundo (Coursera)

Este curso te brindará los conocimientos introductorios sobre Aprendizaje Profundo, vas a entender los fundamentos teóricos y su implementación . Se comenzará entendiendo cómo evolucionó el campo hasta llegar a las redes profundas y cuáles son sus principales beneficios frente a otras técnicas de aprendizaje supervisado, así como también sus limitaciones y situaciones en donde no
posee un rendimiento superior

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

What You Will Learn

  • Interpretar qué es el aprendizaje profundo
  • Evaluar por qué usar técnicas de aprendizaje profundo y cuándo usarlo
  • Construir y entrenar una regresión logística para un problema de clasificación
  • Construir y entrenar una red neuronal completamente conectada para un problema de clasificación

Syllabus

WEEK 1
Introducción al aprendizaje profundo
Se estudiará qué es el aprendizaje supervisado, que son las redes neuronales, a qué llamamos aprendizaje profundo y cuál es su importancia actual

WEEK 2
Conceptos Básicos de Redes Neuronales
Se analizarán los conceptos más importantes referidos a las redes neuronales, partiendo desde la clasificación binaria con regresiones logísticas, el descenso del gradiente y la vectorización.

WEEK 3
Red Neuronal de una sola capa oculta
Se introducirán a las redes neuronales profundas, intuición de las funciones de activación, iniciaciones y propagaciones

WEEK 4
Red Neuronal Profundas
Se explorarán las Redes Neuronales denominadas profundas, es decir, aquellas con múltiples capas ocultas, que permiten una representación más compleja de los patrones en los datos. Se evaluará como cambia su implementación y optimización de parámetros

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Big Data: capstone project (Coursera) Coursera
Universitat Autònoma de Barcelona

Big Data: capstone project (Coursera)

En este último curso de la Especialización Big Data el estudiante tendrá la oportunidad de aplicar algunas de las herramientas y métodos aprendidos en los cursos anteriores en un caso práctico. El objetivo de este Capstone Project es mostrar un ejemplo del trabajo que se realiza diariamente en el departamento de Cosmología del Port d’Informació Científica, en Barcelona.

Nov 30th 2026
13-24 Weeks
Introduction to Machine Learning (Coursera) Coursera
Duke University

Introduction to Machine Learning (Coursera)

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.

Nov 16th 2026
5-12 Weeks
Decision Making and Reinforcement Learning (Coursera) Coursera
Columbia University

Decision Making and Reinforcement Learning (Coursera)

This course is an introduction to sequential decision making and reinforcement learning. We start with a discussion of utility theory to learn how preferences can be represented and modeled for decision making. We first model simple decision problems as multi-armed bandit problems in and discuss several approaches to evaluate feedback. We will then model decision problems as finite Markov decision processes (MDPs), and discuss their solutions via dynamic programming algorithms. We touch on the notion of partial observability in real problems, modeled by POMDPs and then solved by online planning methods.

Nov 23rd 2026
5-12 Weeks
Machine Learning Basics (Coursera) Coursera
Sungkyunkwan University - SKKU

Machine Learning Basics (Coursera)

In this course, you will: understand the basic concepts of machine learning; understand a typical memory-based method, the K nearest neighbor method; understand linear regression; understand model analysis. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.

Nov 23rd 2026
4 Weeks
Neural Networks and Deep Learning (Coursera) Coursera
DeepLearning.AI

Neural Networks and Deep Learning (Coursera)

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning.

Nov 9th 2026
4 Weeks
AI Strategy and Governance (Coursera) Coursera
University of Pennsylvania

AI Strategy and Governance (Coursera)

In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale.

Nov 9th 2026
4 Weeks
Combining and Analyzing Complex Data (Coursera) Coursera
University of Maryland, College Park

Combining and Analyzing Complex Data (Coursera)

In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis. The course will also cover the basics of record linkage and statistical matching—both of which are becoming more important as ways of combining data from different sources. Combining of datasets raises ethical issues which the course reviews. Informed consent may have to be obtained from persons to allow their data to be linked. You will learn about differences in the legal requirements in different countries.

Nov 16th 2026
4 Weeks
Attention Mechanism (Coursera) Coursera
Google Cloud

Attention Mechanism (Coursera)

This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering.

Nov 16th 2026
1 Week
Deep Learning and Reinforcement Learning (Coursera) Coursera
IBM

Deep Learning and Reinforcement Learning (Coursera)

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning.

Nov 9th 2026
5-12 Weeks
Create Image Captioning Models (Coursera) Coursera
Google Cloud

Create Image Captioning Models (Coursera)

This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images.

Nov 16th 2026
1 Week
Prediction Models with Sports Data (Coursera) Coursera
University of Michigan

Prediction Models with Sports Data (Coursera)

In this course the learner will be shown how to generate forecasts of game results in professional sports using Python. The main emphasis of the course is on teaching the method of logistic regression as a way of modeling game results, using data on team expenditures. The learner is taken through the process of modeling past results, and then using the model to forecast the outcome games not yet played.

Nov 9th 2026
5-12 Weeks