Coursera

Optimización de Redes Neuronales Profundas (Coursera)

Offered by Universidad Austral,

Este curso se centrará en la optimización de Redes Neuronales Profundas, cambiando la idea de que todo el proceso es una “caja negra”. Comprenderá qué impulsa el rendimiento y podrá obtener mejores resultados de manera más sistemática. Entenderá cómo optimizar los principales Hiperparámetros y su implementación. Además, aprenderá nuevos conceptos útiles para el entrenamiento de las redes como los mini-batch y las regularizaciones. También, aprenderá a implementar una red neuronal utilizando TensorFlow

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

What You Will Learn

Utilizar eficazmente los "trucos" comunes de la red neuronal
Comprender las mejores prácticas de la industria para crear aplicaciones de aprendizaje profundo.
Implementar y aplicar una variedad de algoritmos de optimización.

Syllabus

WEEK 1
Aspectos prácticos del aprendizaje profundo
Se estudiará cómo configurar su aplicación de aprendizaje automático, separando los sets de entrenamiento y testeo. Se entenderá que es la regularización en una red neuronal y cómo definir el problema para poder optimizarlo.

WEEK 2
Algoritmos de Optimización
Se estudiarán los distintos métodos de optimización que se pueden utilizar en el entrenamiento de redes neuronales profundas. Además, se analizarán las ventajas de trabajar con minibatches para acelerar el proceso y los beneficios de aplicar una diminución progresiva a la tasa de aprendizaje.

WEEK 3
Ajuste de Hiperparámetros, Normalización por lotes e implementación en Tensorflow
Se aprenderán las principales técnicas y opciones en el ajuste de Hiperparámetros, la normalización por lotes y se introducirá la librería Tensorflow para la implementación de redes neuronales en Python

Go to Class

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

Related Courses

Coursera

DeepLearning.AI

Custom and Distributed Training with TensorFlow (Coursera)

Data Science

In this course, you will: • Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow, and learn how to use a TensorFlow tool to calculate gradients; • Build your own custom training loops using GradientTape and TensorFlow Datasets to gain more flexibility and visibility with your model training; • Learn about the benefits of generating code that runs in graph mode, take a peek at what graph code looks like, and practice generating this more efficient code automatically with TensorFlow’s tools; • Harness the power of distributed training to process more data and train larger models, faster, get an overview of various distributed training strategies, and practice working with a strategy that trains on multiple GPU cores, and another that trains on multiple TPU cores.

Sep 21st 2026

4 Weeks

Machine Learning GPU Gradient

Coursera

University of Colorado Boulder

Computational Vision (Coursera)

Computer Science

In this course, we will expand on vision as a cognitive problem space and explore models that address various vision tasks. We will then explore how the boundaries of these problems lead to a more complex analysis of the mind and the brain and how these explorations lead to more complex computational models of understanding.

Sep 21st 2026

4 Weeks

Machine Learning Neural Networks Mind and Machine Specialization

Coursera

LearnQuest

Neural Networks and Random Forests (Coursera)

Data Science

In this course, we will build on our knowledge of basic models and explore advanced AI techniques. We’ll start with a deep dive into neural networks, building our knowledge from the ground up by examining the structure and properties. Then we’ll code some simple neural network models and learn to avoid overfitting, regularization, and other hyper-parameter tricks.

Sep 21st 2026

3 Weeks

Neural Networks AI Random Forest

Coursera

University of Illinois at Urbana-Champaign

Health Data Science Foundation (Coursera)

Data Science

This course is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.

Sep 21st 2026

4 Weeks

Machine Learning Neural Networks Health Data

Coursera

New York Institute of Finance,Google Cloud

Introduction to Trading, Machine Learning & GCP (Coursera)

Data Science

In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks.

Sep 21st 2026

4 Weeks

Finance Machine Learning Neural Networks

Coursera

DeepLearning.AI

Sequences, Time Series and Prediction (Coursera)

Data Science

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

Sep 21st 2026

4 Weeks

Machine Learning Forecasting Prediction

Coursera

University of Chicago

Machine Learning: Concepts and Applications (Coursera)

Data Science

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.

Sep 21st 2026

5-12 Weeks

Python Machine Learning Clustering

Coursera

DeepLearning.AI

Calculus for Machine Learning and Data Science (Coursera)

Data Science Computer Science

After completing this course, learners will be able to: analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients; approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods; visually interpret differentiation of different types of functions commonly used in machine learning; perform gradient descent in neural networks with different activation and cost functions.

Sep 21st 2026

3 Weeks

Calculus Machine Learning Neural Networks

Coursera

Starweaver

Effective Inventory Management and Optimization (Coursera)

Management & Leadership Business

In today's competitive business landscape, efficient inventory management is a critical factor for the success of any organization. Businesses face challenges in maintaining the right balance between having enough inventory to meet customer demands and avoiding excess stock that ties up capital and increases storage costs. This course, titled "Effective Inventory Management and Optimization," addresses a need for professionals to understand the intricacies of inventory control and optimization.

Sep 28th 2026

1 Week

Forecasting Optimization Inventory Management

Coursera

Universidad Nacional Autónoma de México

Cómputo evolutivo (Coursera)

Computer Science

La computación evolutiva (evolutionary computation, EC), aplica la teoría de la evolución natural y la genética en la adaptación evolutiva de estructuras computacionales, proporcionando un medio alternativo para atacar problemas complejos en diversas áreas, como la ingeniería, economía, química, medicina y, porque no, las artes. Una población de posibles soluciones de un problema dado es análoga a una población de organismos vivos que evolucionan cada generación, al recombinar los mejores individuos de la población y transmitir sus características de dichos individuos padres, a sus descendientes.

Sep 21st 2026

4 Weeks

Research Methods Genetic Algorithms Optimization

Coursera

CertNexus

Build Decision Trees, SVMs, and Artificial Neural Networks (Coursera)

Data Science

There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more. Adding all of these algorithms to your skillset is crucial for selecting the best tool for the job.

Sep 21st 2026

5-12 Weeks

ML Algorithms Machine Learning

Coursera

National Taiwan University

Operations Research (2): Optimization Algorithms (Coursera)

Computer Science

Operations Research (OR) is a field in which people use mathematical and engineering methods to study optimization problems in Business and Management, Economics, Computer Science, Civil Engineering, Electrical Engineering, etc. The series of courses consists of three parts, we focus on deterministic optimization techniques, which is a major part of the field of OR. As the second part of the series, we study some efficient algorithms for solving linear programs, integer programs, and nonlinear programs.

Sep 28th 2026

5-12 Weeks

Algorithms Optimization Business Analytics