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Introducción al Deep Learning

Este proyecto es un curso práctico y efectivo para aprender los fundamentos del Deep Learning con ejercicios aplicados. Aprenderemos desde cero los fundamentos del Deep Learning, de la librería Keras y a como programar una red neuronal. Todo ello con proyectos prácticos que nos permitirán consolidar los conocimientos.

Gracias a este curso aprenderemos a programar diferentes redes neuronales para predecir el precio de venta de una vivienda o si un cliente alquilará una bicicleta.

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What's inside

Syllabus

Introducción al Deep Learning

En este curso se aprenderá a programar redes neuronales con Keras

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores Deep Learning with practical exercises, which is standard in the tech industry

Taught by Leire Ahedo

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Reviews summary

Excellent course for deep learning

This course is very practical and didactic.

The course material is organized and well-structured.

"...y didáctico el curso"

The course provides hands-on exercises for practical learning.

"muy práctico ..."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introducción al Deep Learning with these activities:

Review machine learning concepts

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Strengthens foundational knowledge in machine learning, providing context and relevance for understanding deep learning concepts

Browse courses on Machine Learning

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Review basic machine learning algorithms, such as supervised and unsupervised learning.
Understand the concepts of data preprocessing, feature engineering, and model evaluation.
Explore practical applications of machine learning in different domains.

Create a course notebook

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Provides a structured way to organize and review course materials, aiding in knowledge retention and recall

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Gather all course materials, including notes, slides, and assignments.
Organize the materials into a notebook or digital format.
Review the notebook regularly to reinforce learning.

Revise linear algebra

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Refreshes and solidifies the knowledge of some mathematical concepts necessary to understand the course

Browse courses on Linear Algebra

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Review matrix operations, including addition, subtraction, multiplication, and inversion.
Practice solving systems of linear equations.
Review concepts of vector spaces, linear independence, and bases.

Four other activities

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Show all seven activities

Join a study group or discussion forum

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Encourages collaboration, sharing of knowledge, and diverse perspectives, leading to better understanding and retention

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Find a study group or discussion forum related to deep learning.
Participate in discussions, ask questions, and share your insights.
Collaborate with others on projects or assignments.

Learn to use Keras

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Provides hands-on experience with the library used in the course, solidifying understanding of its concepts and functions

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Find online tutorials or documentation on Keras.
Follow the tutorials to build simple neural networks.
Experiment with different Keras functions and layers.

Practice building neural networks

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Reinforces practical skills in designing and implementing neural networks, improving proficiency in applying course concepts

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Find online resources or exercises that provide neural network building challenges.
Attempt to build neural networks for different problems.
Compare your solutions with others or seek feedback from experts.

Develop a neural network project

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Provides a tangible outcome that demonstrates understanding of course concepts, promoting deeper engagement and retention

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Identify a problem or dataset that can be addressed with a neural network.
Design and implement a neural network solution.
Evaluate the performance of your neural network and present your findings.

Career center

Learners who complete Introducción al Deep Learning will develop knowledge and skills that may be useful to these careers:

Deep Learning Research Scientist

A Deep Learning Research Scientist conducts research in the field of deep learning. Deep Learning Research Scientists may develop new deep learning algorithms and models. By taking this course, aspiring Deep Learning Research Scientists can build a foundation in Deep Learning, which is a very active area of research. This course may be particularly useful for Deep Learning Research Scientists who wish to specialize in a particular area of deep learning.

See salaries and explore the career path for Deep Learning Research Scientist

Deep Learning Engineer

A Deep Learning Engineer specializes in developing and maintaining deep learning models. Deep learning models are powerful machine learning models that can be used to solve a wide range of problems, from image recognition to natural language processing. By taking this course, aspiring deep learning engineers can build a strong foundation in deep learning, including programming deep learning models with Keras. This course will prepare you to successfully work as a Deep Learning Engineer.

See salaries and explore the career path for Deep Learning Engineer

Artificial Intelligence Engineer

An Artificial Intelligence Engineer designs, develops, and maintains artificial intelligence systems. These systems can perform a variety of tasks, from natural language processing to image recognition. By enrolling in this course, aspiring AI Engineers can build a strong foundation in Deep Learning, which is a very useful approach to creating AI systems. The course covers using Keras to program Deep Learning models, which is a very useful skill for AI Engineers.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

A Machine Learning Engineer builds and maintains machine learning models. By applying machine learning to solve real-world problems, Machine Learning Engineers help businesses leverage data to make better decisions. By taking this course, aspiring Machine Learning Engineers can build a strong foundation in Deep Learning, which is a very important approach to machine learning. The course covers topics like training and evaluating Deep Learning models. This understanding forms a strong foundation for building successful machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist is someone who works on data to extract relevant information. Using machine learning and deep learning models, Data Scientists can create business solutions to drive economic value. Enrolling in this course can help someone who aspires to be a Data Scientist build a foundational understanding of Deep Learning, which is a core aspect of the role. Deep Learning is particularly useful for solving problems in areas like fraud prevention and image recognition. This foundational understanding can equip a Data Scientist with the skills needed to apply Deep Learning to a variety of business challenges.

See salaries and explore the career path for Data Scientist

Artificial Intelligence Researcher

An Artificial Intelligence Researcher conducts research in the field of artificial intelligence. Artificial Intelligence Researchers may develop new artificial intelligence algorithms and models. By taking this course, aspiring Artificial Intelligence Researchers can build a foundation in Deep Learning, which is a very important approach to artificial intelligence. This course may be particularly useful for Artificial Intelligence Researchers who wish to specialize in using deep learning models.

See salaries and explore the career path for Artificial Intelligence Researcher

Computer Vision Engineer

A Computer Vision Engineer designs, develops, and maintains computer vision systems. Computer Vision Engineers may use machine learning and deep learning models to develop new features for computer vision systems. By taking this course, aspiring Computer Vision Engineers can build a foundation in Deep Learning, which is a very useful approach to developing new computer vision features. This course may be particularly useful for Computer Vision Engineers who wish to specialize in using deep learning models.

See salaries and explore the career path for Computer Vision Engineer

Quantitative Analyst

A Quantitative Analyst is someone who uses mathematics and statistics to solve problems in the financial industry. Quantitative Analysts may use machine learning and deep learning models to make predictions about financial markets. By taking this course, aspiring Quantitative Analysts can build a foundation in Deep Learning, which is a very useful approach to solving problems in the financial industry. This course may be particularly useful for Quantitative Analysts who wish to specialize in using deep learning models.

See salaries and explore the career path for Quantitative Analyst

Natural Language Processing Engineer

A Natural Language Processing Engineer designs, develops, and maintains natural language processing systems. Natural Language Processing Engineers may use machine learning and deep learning models to develop new features for natural language processing systems. By taking this course, aspiring Natural Language Processing Engineers can build a foundation in Deep Learning, which is a very useful approach to developing new natural language processing features. This course may be particularly useful for Natural Language Processing Engineers who wish to specialize in using deep learning models.

See salaries and explore the career path for Natural Language Processing Engineer

Machine Learning Researcher

A Machine Learning Researcher conducts research in the field of machine learning. Machine Learning Researchers may develop new machine learning algorithms and models. By taking this course, aspiring Machine Learning Researchers can build a foundation in Deep Learning, which is a very active area of research in machine learning. This course may be particularly useful for Machine Learning Researchers who wish to specialize in deep learning.

See salaries and explore the career path for Machine Learning Researcher

Speech Recognition Engineer

A Speech Recognition Engineer designs, develops, and maintains speech recognition systems. Speech Recognition Engineers may use machine learning and deep learning models to develop new features for speech recognition systems. By taking this course, aspiring Speech Recognition Engineers can build a foundation in Deep Learning, which is a very useful approach to developing new speech recognition features. This course may be particularly useful for Speech Recognition Engineers who wish to specialize in using deep learning models.

See salaries and explore the career path for Speech Recognition Engineer

Software Engineer

A Software Engineer designs, develops, and maintains software applications. Software Engineers may use machine learning and deep learning models to develop new features for software applications. By taking this course, aspiring Software Engineers can build a foundation in Deep Learning, which is a very useful approach to developing new software features. This course may be particularly useful for Software Engineers who wish to specialize in using deep learning models.

See salaries and explore the career path for Software Engineer

Data Science Consultant

A Data Science Consultant helps businesses use data to make better decisions. Data Science Consultants may use machine learning and deep learning models to help businesses solve problems. By taking this course, aspiring Data Science Consultants can build a foundation in Deep Learning, which is a very useful approach to solving business problems. This course may be particularly useful for Data Science Consultants who wish to specialize in using deep learning models.

See salaries and explore the career path for Data Science Consultant

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. Data Analysts may use machine learning and deep learning models to extract insights from data. By taking this course, aspiring Data Analysts can build a foundation in Deep Learning, which can be useful for extracting insights from complex data. This course may also be useful for Data Analysts who wish to specialize in working with deep learning models.

See salaries and explore the career path for Data Analyst

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introducción al Deep Learning.

Deep Learning

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Este libro proporciona una introducción completa al Deep Learning, cubriendo los fundamentos teóricos y prácticos. Es un recurso valioso para comprender los conceptos y algoritmos subyacentes al Deep Learning.

Introducción al Deep Learning

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Excellent course for deep learning

Activities

Career center

Reading list

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