Coursera

Optimización de Redes Neuronales Profundas (Coursera)

Offered by Universidad Austral,
Optimización de Redes Neuronales Profundas (Coursera)

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

Natural Language Processing with Probabilistic Models (Coursera) Coursera
DeepLearning.AI

Natural Language Processing with Probabilistic Models (Coursera)

Data Science

In Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming; b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is important for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model.

Sep 21st 2026
4 Weeks
NLP Machine Learning Neural Networks
Details
Natural Language Processing with Sequence Models (Coursera) Coursera
DeepLearning.AI

Natural Language Processing with Sequence Models (Coursera)

Data Science

In Course 3 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will: a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning.

Sep 21st 2026
4 Weeks
Neural Networks Natural Language Natural Language Processing
Details
Advanced Learning Algorithms (Coursera) Coursera
Stanford University,DeepLearning.AI

Advanced Learning Algorithms (Coursera)

Data Science

In the second course of the Machine Learning Specialization, you will: build and train a neural network with TensorFlow to perform multi-class classification; apply best practices for machine learning development so that your models generalize to data and tasks in the real world; build and use decision trees and tree ensemble methods, including random forests and boosted trees.

Sep 21st 2026
4 Weeks
Algorithms Machine Learning Neural Networks
Details
Custom and Distributed Training with TensorFlow (Coursera) 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
Details
Computational Vision (Coursera) 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
Details
Getting Started with Machine Learning at the Edge on Arm (Coursera) Coursera
Arm

Getting Started with Machine Learning at the Edge on Arm (Coursera)

Computer Science

The age of machine learning has arrived! Arm technology is powering a new generation of connected devices with sophisticated sensors that can collect a vast range of environmental, spatial and audio/visual data. Typically this data is processed in the cloud using advanced machine learning tools that are enabling new applications reshaping the way we work, travel, live and play.

Sep 28th 2026
5-12 Weeks
Machine Learning Computer Vision IoT
Details
Deep Learning for Business (Coursera) Coursera
Yonsei University

Deep Learning for Business (Coursera)

Management & Leadership

Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers.

Sep 21st 2026
5-12 Weeks
Business ML Artificial Intelligence
Details
Probabilistic Deep Learning with TensorFlow 2 (Coursera) Coursera
Imperial College London

Probabilistic Deep Learning with TensorFlow 2 (Coursera)

Data Science

Welcome to this course on Probabilistic Deep Learning with TensorFlow! This course builds on the foundational concepts and skills for TensorFlow taught in the first two courses in this specialisation, and focuses on the probabilistic approach to deep learning. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets. This is a crucial aspect when using deep learning models in applications such as autonomous vehicles or medical diagnoses; we need the model to know what it doesn't know.

Sep 21st 2026
5-12 Weeks
TensorFlow Deep Learning TensorFlow 2
Details
Machine Learning: Concepts and Applications (Coursera) 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
Details
Convolutional Neural Networks (Coursera) Coursera
DeepLearning.AI

Convolutional Neural Networks (Coursera)

Data Science

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.

Sep 21st 2026
4 Weeks
Machine Learning Object Detection TensorFlow
Details