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Andres Rodriguez,
Hanlin Tang,
and
Nikhil Murthy
This course provides an introduction to Deep Learning, a field that aims to harness the enormous amounts of data that we are surrounded by with artificial neural networks, allowing for the development of self-driving cars, speech interfaces, genomic sequence analysis and algorithmic trading.
You will explore important concepts in Deep Learning, train deep networks using Intel Nervana Neon, apply Deep Learning to various applications and explore new and emerging Deep Learning topics.
Register for this course and see more details by visiting:
OpenCourser.com/course/au8w2x/an
What's inside
Syllabus
Introduction to Deep Learning and Deep Learning Basics
Convolutional Neural Networks (CNN), Fine-Tuning and Detection
Recurrent Neural Networks (RNN)
Read more
Syllabus
Introduction to Deep Learning and Deep Learning Basics
Convolutional Neural Networks (CNN), Fine-Tuning and Detection
Recurrent Neural Networks (RNN)
Read more
Training Tips and Multinode Distributed Training
Hot Research and Intel's Roadmap
Final Quiz
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores Deep Learning, which is standard in industries that include self-driving cars, speech interfaces, genomic sequence analysis and algorithmic trading
Taught by Andres Rodriguez, Hanlin Tang, Nikhil Murthy, who are renowned in their field
Develops skills in Convolutional Neural Networks (CNN), Fine-Tuning and Detection, and Recurrent Neural Networks (RNN), which are core in Deep Learning
Covers important concepts in Deep Learning, training deep networks using Intel Nervana Neon, and applying Deep Learning to various applications
Offers hands-on training tips and Multinode Distributed Training, which enhance practical skills
Provides insights into new and emerging Deep Learning topics, ensuring currency in knowledge
Save this course
Save An Introduction to Practical Deep Learning to your list so you can find it easily later:
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Reviews summary
Deep learning practical introduction
Learners say this is a decent course that offers informative lectures and engaging assignments. However, outdated exercises, lack of instructor involvement, and insufficient detail on some topics leave room for improvement.
Lectures are well-presented and informative.
"All the lectures were well presented and with quality."
"The course lectures are great for getting an overview of DNN and CNN."
Instructors are not very involved in discussion forums.
"The teachers don't check discussion Forms."
"And the most frustrating thing, the instructors are completely absent from the discussion forums & even doesn't come to see what's happening overall & how're the students reacting for it."
Some exercises have errors or are outdated.
"It is a good course but its Labs have problems. They have errors."
"Most of the code are outdated and don't run correctly."
"Many of the exercises would not work because they were outdated or had files missing."
Some topics are not explained in sufficient detail.
"Some of the topics are not explained in detail are i referred other resource for that topics."
"The homework is not very explanatory."
"While the material was fine, there were some issues with this course."
"This course does not feel like an "introduction" class; it seems like there should be a pre-requisite to it, or at least a list of what-you-will-need before taking it."
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 An Introduction to Practical Deep Learning with these
activities:
Review Linear Algebra and Calculus
Show steps
Deep Learning models benefit from a solid mathematical foundation.
Browse courses on
Linear Algebra
Show steps
-
Review vector and matrix operations.
-
Review the concept of derivatives and gradients.
-
Review the chain rule for derivatives.
Explore the Intel Nervana Neon Framework
Show steps
This course uses the Intel Nervana Neon framework, so it's helpful to familiarize yourself with it.
Show steps
-
Watch tutorials on Intel Nervana Neon.
-
Follow along with examples and exercises.
-
Build a small project using Intel Nervana Neon.
Practice Neural Network Implementation
Show steps
Coding a neural network will help you understand how they operate.
Browse courses on
Neural Networks
Show steps
-
Implement a simple feedforward neural network from scratch.
-
Try different activation functions, such as ReLU, sigmoid, and tanh.
-
Experiment with different loss functions, such as mean squared error and cross-entropy.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Study Group
Show steps
Collaboration can enhance your learning experience.
Browse courses on
Deep Learning
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss the course material.
-
Work on projects together.
Attend a Deep Learning Meetup or Conference
Show steps
Connecting with others in the field can expand your knowledge and provide support..
Browse courses on
Deep Learning
Show steps
-
Find a local Deep Learning meetup or conference.
-
Attend the event and engage with other attendees.
-
Share your experiences and ask questions.
Compile a List of Deep Learning Resources
Show steps
Having a collection of useful resources can save time and effort.
Browse courses on
Deep Learning
Show steps
-
Search for and identify valuable Deep Learning resources, such as articles, videos, and datasets.
-
Organize the resources into a structured and accessible format.
-
Share the compilation with other students or the community.
Create a Visualization of a Deep Learning Model
Show steps
Visualizing your models can help you understand their behavior and identify potential issues.
Browse courses on
Deep Learning
Show steps
-
Choose a deep learning model and dataset.
-
Use a visualization tool to create a visual representation of the model's architecture.
-
Train the model and observe how the visualization changes.
-
Analyze the visualization to identify patterns and make inferences.
Attend a Deep Learning Workshop
Show steps
Workshops provide focused, hands-on training.
Browse courses on
Deep Learning
Show steps
-
Find a Deep Learning workshop that aligns with your learning goals.
-
Attend the workshop and actively participate in the activities.
-
Apply the knowledge and skills you gained in your own projects.
Review Linear Algebra and Calculus
Show steps
Deep Learning models benefit from a solid mathematical foundation.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review vector and matrix operations.
- Review the concept of derivatives and gradients.
- Review the chain rule for derivatives.
Explore the Intel Nervana Neon Framework
Show steps
This course uses the Intel Nervana Neon framework, so it's helpful to familiarize yourself with it.
Show steps
Show steps
Show steps
- Watch tutorials on Intel Nervana Neon.
- Follow along with examples and exercises.
- Build a small project using Intel Nervana Neon.
Practice Neural Network Implementation
Show steps
Coding a neural network will help you understand how they operate.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Implement a simple feedforward neural network from scratch.
- Try different activation functions, such as ReLU, sigmoid, and tanh.
- Experiment with different loss functions, such as mean squared error and cross-entropy.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Study Group
Show steps
Collaboration can enhance your learning experience.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss the course material.
- Work on projects together.
Attend a Deep Learning Meetup or Conference
Show steps
Connecting with others in the field can expand your knowledge and provide support..
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find a local Deep Learning meetup or conference.
- Attend the event and engage with other attendees.
- Share your experiences and ask questions.
Compile a List of Deep Learning Resources
Show steps
Having a collection of useful resources can save time and effort.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Search for and identify valuable Deep Learning resources, such as articles, videos, and datasets.
- Organize the resources into a structured and accessible format.
- Share the compilation with other students or the community.
Create a Visualization of a Deep Learning Model
Show steps
Visualizing your models can help you understand their behavior and identify potential issues.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Choose a deep learning model and dataset.
- Use a visualization tool to create a visual representation of the model's architecture.
- Train the model and observe how the visualization changes.
- Analyze the visualization to identify patterns and make inferences.
Attend a Deep Learning Workshop
Show steps
Workshops provide focused, hands-on training.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find a Deep Learning workshop that aligns with your learning goals.
- Attend the workshop and actively participate in the activities.
- Apply the knowledge and skills you gained in your own projects.
Career center
Learners who complete An Introduction to Practical Deep Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
El curso te presenta los fundamentos de Deep Learning, un campo que busca aprovechar la gran cantidad de data de la que estamos rodeados con redes neuronales artificiales, lo que permite el desarrollo de carros autónomos, interfaces de voz, análisis de secuencias genómicas y trading algorítmico. Como Machine Learning Engineer, utilizarás redes neuronales y Deep Learning para resolver problemas complejos de reconocimiento de patrones y toma de decisiones para diversos dominios. Este curso puede ayudarte a desarrollar las habilidades y los conocimientos necesarios para sobresalir en este puesto.
Data Scientist
Data Scientist
El curso proporciona una introducción al Deep Learning, que es un campo crucial en el arsenal de un Data Scientist. Los Data Scientists utilizan el Deep Learning para extraer información significativa de grandes conjuntos de datos, lo que les permite desarrollar modelos predictivos precisos y sistemas de inteligencia artificial. Este curso te ayudará a construir una base sólida en Deep Learning, lo cual es esencial para tener éxito en este puesto.
Software Engineer
Software Engineer
El curso te presenta los conceptos esenciales del Deep Learning, que desempeña un papel cada vez más importante en el desarrollo de software. Los Software Engineers utilizan el Deep Learning para crear aplicaciones innovadoras, como sistemas de reconocimiento de imagen y procesamiento del lenguaje natural. Este curso puede ayudarte a adquirir las habilidades de Deep Learning necesarias para sobresalir en este campo.
Research Scientist
Research Scientist
El curso ofrece una inmersión en Deep Learning, un campo emocionante en el ámbito de la investigación. Los Research Scientists utilizan el Deep Learning para avanzar en el conocimiento científico y tecnológico, lo que lleva a nuevos descubrimientos e innovaciones. Este curso puede ayudarte a desarrollar la experiencia en Deep Learning necesaria para tener éxito en la investigación de vanguardia.
Data Analyst
Data Analyst
El curso proporciona una introducción al Deep Learning, un campo que está transformando el análisis de datos. Los Data Analysts utilizan el Deep Learning para extraer información valiosa de grandes conjuntos de datos, lo que permite a las empresas tomar decisiones basadas en datos. Este curso puede ayudarte a desarrollar las habilidades de Deep Learning necesarias para tener éxito en este puesto.
Business Analyst
Business Analyst
El curso te presenta los conceptos fundamentales del Deep Learning, que está ganando importancia en el análisis empresarial. Los Business Analysts utilizan el Deep Learning para comprender mejor a los clientes, optimizar las operaciones y predecir tendencias comerciales. Este curso puede ayudarte a adquirir las habilidades de Deep Learning necesarias para sobresalir en este campo.
Product Manager
Product Manager
El curso ofrece una introducción al Deep Learning, un campo que influye cada vez más en el desarrollo de productos. Los Product Managers utilizan el Deep Learning para crear productos innovadores y centrados en el cliente. Este curso puede ayudarte a adquirir las habilidades de Deep Learning necesarias para tener éxito en este puesto.
Consultant
Consultant
El curso te presenta los conceptos esenciales del Deep Learning, que se está volviendo cada vez más importante en la consultoría. Los Consultants utilizan el Deep Learning para ayudar a las empresas a resolver problemas complejos y tomar decisiones informadas. Este curso puede ayudarte a desarrollar las habilidades de Deep Learning necesarias para tener éxito en este puesto.
Quantitative Analyst
Quantitative Analyst
El curso proporciona una inmersión en el Deep Learning, un campo que es crucial en el análisis cuantitativo. Los Quantitative Analysts utilizan el Deep Learning para desarrollar modelos financieros sofisticados y estrategias de inversión. Este curso puede ayudarte a adquirir las habilidades de Deep Learning necesarias para tener éxito en este campo.
Actuary
Actuary
El curso te presenta los fundamentos del Deep Learning, un campo que está ganando importancia en el campo actuarial. Los Actuaries utilizan el Deep Learning para desarrollar modelos de riesgo y seguros más precisos. Este curso puede ayudarte a adquirir las habilidades de Deep Learning necesarias para tener éxito en este campo.
Statistician
Statistician
El curso ofrece una inmersión en el Deep Learning, que se está convirtiendo cada vez más en una herramienta esencial en las estadísticas. Los Statisticians utilizan el Deep Learning para analizar grandes conjuntos de datos y desarrollar modelos estadísticos más precisos. Este curso puede ayudarte a adquirir las habilidades de Deep Learning necesarias para tener éxito en este campo.
Operations Research Analyst
Operations Research Analyst
El curso te presenta los conceptos esenciales del Deep Learning, que se está utilizando cada vez más en la investigación de operaciones. Los Operations Research Analysts utilizan el Deep Learning para desarrollar modelos y algoritmos más eficientes para la toma de decisiones. Este curso puede ayudarte a adquirir las habilidades de Deep Learning necesarias para tener éxito en este campo.
Financial Analyst
Financial Analyst
El curso proporciona una introducción al Deep Learning, que está ganando importancia en el análisis financiero. Los Financial Analysts utilizan el Deep Learning para desarrollar modelos de valoración y gestión de carteras más precisos. Este curso puede ayudarte a adquirir las habilidades de Deep Learning necesarias para tener éxito en este campo.
Risk Manager
Risk Manager
El curso te presenta los fundamentos del Deep Learning, que se está utilizando cada vez más en la gestión de riesgos. Los Risk Managers utilizan el Deep Learning para desarrollar modelos de riesgo más precisos y estrategias de mitigación de riesgos. Este curso puede ayudarte a adquirir las habilidades de Deep Learning necesarias para tener éxito en este campo.
Auditor
Auditor
El curso ofrece una inmersión en el Deep Learning, que se está volviendo cada vez más importante en la auditoría. Los Auditors utilizan el Deep Learning para analizar grandes conjuntos de datos y detectar fraudes e irregularidades. Este curso puede ayudarte a adquirir las habilidades de Deep Learning necesarias para tener éxito en este campo.
For more career information including salaries, visit:
OpenCourser.com/course/au8w2x/an
Reading list
We've selected eight 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
An Introduction to Practical Deep Learning.
Save
Provides a comprehensive overview of the field of deep learning, covering the latest advances in neural networks, deep learning algorithms, and applications. It valuable resource for researchers, students, and practitioners who want to learn more about deep learning.
Provides a practical introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It valuable resource for beginners who want to learn how to apply machine learning to real-world problems.
Provides a practical introduction to deep learning using Python. It valuable resource for beginners who want to learn how to build and train deep learning models.
Provides a comprehensive overview of TensorFlow, a popular deep learning library. It valuable resource for beginners who want to learn how to use TensorFlow to build and train deep learning models.
Provides a visual introduction to deep learning. It valuable resource for beginners who want to learn more about deep learning without getting bogged down in the mathematics.
Provides a comprehensive overview of artificial intelligence, covering the key concepts and algorithms. It valuable resource for beginners who want to learn more about artificial intelligence.
Provides a practical introduction to machine learning using neural networks and TensorFlow. It valuable resource for beginners who want to learn how to build and train machine learning models.
Provides a comprehensive overview of deep learning using JavaScript. It valuable resource for beginners who want to learn how to use JavaScript to build and train deep learning models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/au8w2x/an
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Effort
3-5 hours/week
Via
Coursera
Institution
Intel
Instructors
Andres Rodriguez
Hanlin Tang
Nikhil Murthy
Language
English
Transcripts
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Good to know
Explores Deep Learning, which is standard in industries that include self-driving cars, speech interfaces, genomic sequence analysis and algorithmic trading
Taught by Andres Rodriguez, Hanlin Tang, Nikhil Murthy, who are renowned in their field
Develops skills in Convolutional Neural Networks (CNN), Fine-Tuning and Detection, and Recurrent Neural Networks (RNN), which are core in Deep Learning
Covers important concepts in Deep Learning, training deep networks using Intel Nervana Neon, and applying Deep Learning to various applications
Offers hands-on training tips and Multinode Distributed Training, which enhance practical skills
Provides insights into new and emerging Deep Learning topics, ensuring currency in knowledge
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