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Alex Aklson
Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
Read more
Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
Read more
Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
After completing this course, learners will be able to:
• Describe what a neural network is, what a deep learning model is, and the difference between them.
• Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.
• Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.
• Build deep learning models and networks using the Keras library.
Register for this course and see more details by visiting:
OpenCourser.com/course/iqweat/introduction
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What's inside
Syllabus
Introduction to Neural Networks and Deep Learning
In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. You will also learn about neural networks and how most of the deep learning algorithms are inspired by the way our brain functions and the neurons process data. Finally, you will learn about how neural networks feed data forward through the network.
Read more
Syllabus
Introduction to Neural Networks and Deep Learning
In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. You will also learn about neural networks and how most of the deep learning algorithms are inspired by the way our brain functions and the neurons process data. Finally, you will learn about how neural networks feed data forward through the network.
Read more
Artificial Neural Networks
In this module, you will learn about the gradient descent algorithm and how variables are optimized with respect to a defined function. You will also learn about backpropagation and how neural networks learn and update their weights and biases. Futhermore, you will learn about the vanishing gradient problem. Finally, you will learn about activation functions.
Keras and Deep Learning Libraries
In this module, you will learn about the diifferent deep learning libraries namely, Keras, PyTorch, and TensorFlow. You will also learn how to build regression and classification models using the Keras library.
Deep Learning Models
In this module, you will learn about the difference between the shallow and deep neural networks. You will also learn about convolutional networks and how to build them using the Keras library. Finally, you will also learn about recurrent neural networks and autoencoders.
Course Project
In this module, you will conclude the course by working on a final assignment where you will use the Keras library to build a regression model and experiment with the depth and the width of the model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explored advanced topics such as backpropagation and activation functions in neural networks
Taught by an experienced instructor with proven expertise in deep learning
Course developed core skills in deep learning using the Keras library
Examined unsupervised and supervised deep learning models
Covered foundational theories and concepts for beginners
Includes hands-on labs for practical application
Save this course
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Reviews summary
Introduction to deep learning & neural networks with keras
learners say this course is largely positive and a great introduction to deep learning and neural networks, especially for beginners. Students found the lectures, engaging assignments, and practical labs very helpful. Overall, learners enjoyed the course and felt they learned a lot about deep learning and Keras.
Some areas could be explored deeper
"The lessons and exercises could be deeper."
"The content is for new comer of tf.keras OK."
"This course gives intro to the beginner who start learning the concept of deep learning"
Reasonable and flexible
"The course is very concise and to the point."
"It is a nice very short introduction to Deep learning."
"The course is well-structured and provided a good overview of the covered topics."
Challenging and valuable
"The assignment really stretched me - but that was good."
"The challenges of the final project was good for reinforcing knowledge."
"The final exercise did ask for students to use tools not gone over in class"
Easy to follow and understand
"The course is well-structured, with clear and engaging lectures"
"The material was also very useful along with the labs."
"It is a great introduction to deep learning and neural networks"
Knowledgeable and experienced
"Alex is an outstanding teacher, I always appreciate his knowledge and enthusiasm."
"The instructor was great. Very knowledgeable and explained everything in a clear manner."
"The professor was awesome, broke down concepts and made them easy to understand."
Engaging and educational
"Lab assignments are really good as they help in building the concepts nicely"
"The labs are very informative and explains the basic concepts throughly."
"The course contents and labs are very informative and explains the basic concepts throughly."
Clear and concise lessons
"The course contents are very informative and explains the basic concepts throughly."
"The course is very good, the content of the explanation is very concise, there is no superfluous stuff"
"The explanations are clear and good"
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 Introduction to Deep Learning & Neural Networks with Keras with these
activities:
Connect with Experienced Deep Learning Professionals
Show steps
Accelerate your learning journey by seeking guidance and support from experienced deep learning professionals.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors through online platforms, professional organizations, or personal networks
-
Reach out to mentors and schedule meetings or conversations
-
Discuss your learning goals, challenges, and career aspirations
Join a Deep Learning Study Group or Community
Show steps
Enhance understanding and expand perspectives by engaging in active discussions and knowledge exchange with peers.
Show steps
-
Identify or join online or offline study groups or communities dedicated to deep learning
-
Participate in regular meetings or discussions, sharing insights and collaborating on projects
-
Provide feedback and support to other members of the group
Follow Expert-Led Deep Learning Tutorials
Show steps
Enhance understanding of deep learning concepts and techniques through structured tutorials led by experienced professionals.
Show steps
-
Identify reputable sources for deep learning tutorials
-
Select tutorials aligned with your learning objectives
-
Follow the tutorials step-by-step, completing exercises and assignments
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Deep Learning Coding Challenges
Show steps
Practice implementing deep learning algorithms in a coding environment to enhance understanding and problem-solving skills.
Browse courses on
Coding Challenges
Show steps
-
Identify online platforms or resources providing deep learning coding challenges
-
Select challenges appropriate to your skill level and start solving them
-
Analyze the solutions and identify areas for improvement
Develop a Deep Learning Model for a Specific Business Problem
Show steps
Build a deep learning model to address a specific business challenge, such as image classification, natural language processing, or time series analysis.
Browse courses on
Model Development
Show steps
-
Define the business problem and collect relevant data
-
Choose an appropriate deep learning model and architecture
-
Train and evaluate the model using the collected data
-
Deploy the model and monitor its performance
Participate in Deep Learning Competitions or Hackathons
Show steps
Challenge yourself, gain practical experience, and contribute to the deep learning community by participating in competitions or hackathons.
Show steps
-
Identify suitable deep learning competitions or hackathons
-
Form a team or work individually to develop a solution
-
Submit your solution and compete against other participants
-
Analyze the results and identify areas for improvement
Create a Deep Learning Blog or Article
Show steps
Solidify understanding and enhance communication skills by creating a blog or article that explains deep learning concepts or shares insights.
Browse courses on
Content Creation
Show steps
-
Choose a specific deep learning topic to focus on
-
Research and gather relevant information
-
Organize and structure the content into a coherent narrative
-
Proofread and refine the writing for clarity and accuracy
-
Publish and share the blog or article
Mentor Junior Deep Learning Enthusiasts
Show steps
Strengthen your understanding and communication skills while contributing to the growth of others by mentoring junior deep learning enthusiasts.
Browse courses on
Mentoring
Show steps
-
Identify opportunities to mentor others, such as through online forums or local meetups
-
Share your knowledge and expertise, providing guidance and support
-
Provide constructive feedback and encourage continuous learning
Connect with Experienced Deep Learning Professionals
Show steps
Accelerate your learning journey by seeking guidance and support from experienced deep learning professionals.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors through online platforms, professional organizations, or personal networks
- Reach out to mentors and schedule meetings or conversations
- Discuss your learning goals, challenges, and career aspirations
Join a Deep Learning Study Group or Community
Show steps
Enhance understanding and expand perspectives by engaging in active discussions and knowledge exchange with peers.
Show steps
Show steps
Show steps
- Identify or join online or offline study groups or communities dedicated to deep learning
- Participate in regular meetings or discussions, sharing insights and collaborating on projects
- Provide feedback and support to other members of the group
Follow Expert-Led Deep Learning Tutorials
Show steps
Enhance understanding of deep learning concepts and techniques through structured tutorials led by experienced professionals.
Show steps
Show steps
Show steps
- Identify reputable sources for deep learning tutorials
- Select tutorials aligned with your learning objectives
- Follow the tutorials step-by-step, completing exercises and assignments
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Deep Learning Coding Challenges
Show steps
Practice implementing deep learning algorithms in a coding environment to enhance understanding and problem-solving skills.
Browse courses on
Coding Challenges
Show steps
Show steps
Show steps
- Identify online platforms or resources providing deep learning coding challenges
- Select challenges appropriate to your skill level and start solving them
- Analyze the solutions and identify areas for improvement
Develop a Deep Learning Model for a Specific Business Problem
Show steps
Build a deep learning model to address a specific business challenge, such as image classification, natural language processing, or time series analysis.
Browse courses on
Model Development
Show steps
Show steps
Show steps
- Define the business problem and collect relevant data
- Choose an appropriate deep learning model and architecture
- Train and evaluate the model using the collected data
- Deploy the model and monitor its performance
Participate in Deep Learning Competitions or Hackathons
Show steps
Challenge yourself, gain practical experience, and contribute to the deep learning community by participating in competitions or hackathons.
Show steps
Show steps
Show steps
- Identify suitable deep learning competitions or hackathons
- Form a team or work individually to develop a solution
- Submit your solution and compete against other participants
- Analyze the results and identify areas for improvement
Create a Deep Learning Blog or Article
Show steps
Solidify understanding and enhance communication skills by creating a blog or article that explains deep learning concepts or shares insights.
Browse courses on
Content Creation
Show steps
Show steps
Show steps
- Choose a specific deep learning topic to focus on
- Research and gather relevant information
- Organize and structure the content into a coherent narrative
- Proofread and refine the writing for clarity and accuracy
- Publish and share the blog or article
Mentor Junior Deep Learning Enthusiasts
Show steps
Strengthen your understanding and communication skills while contributing to the growth of others by mentoring junior deep learning enthusiasts.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify opportunities to mentor others, such as through online forums or local meetups
- Share your knowledge and expertise, providing guidance and support
- Provide constructive feedback and encourage continuous learning
Career center
Learners who complete Introduction to Deep Learning & Neural Networks with Keras will develop knowledge and skills
that may be useful to these careers:
Ingeniero de Aprendizaje Automático
Ingeniero de Aprendizaje Automático
Como Ingeniero de Aprendizaje Automático, desarrollarás y mantendrás modelos de aprendizaje automático que automatizan tareas complejas. Este curso te proporcionará una comprensión integral del aprendizaje profundo y las redes neuronales, que son las tecnologías fundamentales en el campo del aprendizaje automático. Aprenderás a diseñar, entrenar y evaluar modelos de aprendizaje profundo utilizando la biblioteca Keras.
Investigador de aprendizaje automático
Investigador de aprendizaje automático
Como Investigador de Aprendizaje Automático, explorarás nuevos algoritmos y técnicas de aprendizaje automático para resolver problemas complejos. Este curso te proporcionará una sólida base teórica en aprendizaje profundo y redes neuronales, lo cual es esencial para desarrollar enfoques innovadores y avanzar en el campo del aprendizaje automático.
Profesor de aprendizaje automático
Profesor de aprendizaje automático
Como Profesor de Aprendizaje Automático, impartirás cursos y realizarás investigaciones en el campo del aprendizaje automático. Este curso te proporcionará una comprensión integral del aprendizaje profundo y las redes neuronales, lo que te permitirá transmitir conocimientos precisos y actualizados a tus estudiantes y colegas.
Data Scientist
Data Scientist
Como Científico de Datos, asumirás la responsabilidad de analizar y comprender grandes cantidades de datos para extraer información valiosa. Este curso te proporcionará una base sólida en aprendizaje profundo y redes neuronales, lo cual es esencial para construir modelos precisos y efectivos para el análisis de datos. Aprenderás a implementar redes neuronales profundas utilizando la biblioteca Keras, que es ampliamente utilizada en la industria.
Analista de Investigación
Analista de Investigación
Como Analista de Investigación, utilizarás técnicas analíticas para resolver problemas complejos y proporcionar información basada en datos. Este curso te proporcionará una base sólida en aprendizaje profundo y redes neuronales, que te permitirá desarrollar modelos analíticos avanzados para extraer información y generar predicciones a partir de grandes conjuntos de datos.
Actuario
Actuario
Como Actuario, utilizarás habilidades matemáticas, estadísticas y financieras para evaluar y gestionar los riesgos en las industrias de seguros y finanzas. Este curso te proporcionará una base sólida en aprendizaje profundo y redes neuronales, que se están utilizando cada vez más en la industria actuarial para mejorar la modelización de riesgos, la tarificación de seguros y la gestión de inversiones.
Físico
Físico
Como Físico, investigarás y aplicarás principios físicos para comprender y explicar el mundo natural. Este curso te proporcionará una base sólida en aprendizaje profundo y redes neuronales, que se utilizan cada vez más en el campo de la física para el análisis de datos, la simulación y el descubrimiento.
Ingeniero biomédico
Ingeniero biomédico
Como Ingeniero Biomédico, aplicarás principios de ingeniería para resolver problemas médicos y de atención médica. Este curso te proporcionará una base sólida en aprendizaje profundo y redes neuronales, lo cual es cada vez más importante en el campo de la ingeniería biomédica para el desarrollo de diagnósticos, tratamientos y dispositivos médicos mejorados.
Científico de Datos Junior
Científico de Datos Junior
Como Científico de Datos Junior, estarás involucrado en la recopilación, limpieza y análisis de datos para identificar patrones y tendencias. Este curso te proporcionará una base sólida en aprendizaje profundo y redes neuronales, lo cual es esencial para construir modelos de aprendizaje automático efectivos y extraer información valiosa de los datos.
Ingeniero de Aprendizaje Automático Junior
Ingeniero de Aprendizaje Automático Junior
Como Ingeniero de Aprendizaje Automático Junior, colaborarás con científicos de datos y otros ingenieros para desarrollar y mantener modelos de aprendizaje automático. Este curso te brindará una comprensión integral del aprendizaje profundo y las redes neuronales, lo que te permitirá contribuir al desarrollo de soluciones de aprendizaje automático innovadoras.
Analista de riesgos
Analista de riesgos
Como Analista de Riesgos, evaluarás y gestionarás los riesgos financieros y operativos para las instituciones financieras y otras organizaciones. Este curso te proporcionará habilidades valiosas en aprendizaje profundo y redes neuronales, que se utilizan cada vez más en la industria financiera para mejorar la gestión de riesgos, la detección de fraudes y el análisis predictivo.
Analista de Investigación Junior
Analista de Investigación Junior
Como Analista de Investigación Junior, utilizarás técnicas analíticas y estadísticas para recopilar y analizar datos con el fin de responder preguntas comerciales específicas. Este curso te proporcionará una base sólida en aprendizaje profundo y redes neuronales, lo que te permitirá desarrollar habilidades analíticas avanzadas y generar información valiosa a partir de grandes conjuntos de datos.
Ingeniero de Software Junior
Ingeniero de Software Junior
Como Ingeniero de Software Junior, colaborarás con desarrolladores de software experimentados para diseñar, desarrollar y mantener aplicaciones y sistemas de software. Este curso te brindará habilidades valiosas en aprendizaje profundo y redes neuronales, lo que te permitirá contribuir al desarrollo de aplicaciones innovadoras que aprovechen estas tecnologías emergentes.
Economista
Economista
Como Economista, analizarás y predecirás las tendencias económicas utilizando datos y modelos. Este curso te proporcionará habilidades valiosas en aprendizaje profundo y redes neuronales, que se utilizan cada vez más en el campo de la economía para mejorar la previsión económica, el análisis de políticas y la toma de decisiones.
Ingeniero de Software
Ingeniero de Software
Como Ingeniero de Software, diseñarás, desarrollarás y mantendrás aplicaciones y sistemas de software. Este curso te proporcionará habilidades valiosas en aprendizaje profundo y redes neuronales, que son tecnologías emergentes que se utilizan en una amplia gama de aplicaciones, como el reconocimiento de imágenes, el procesamiento del lenguaje natural y el análisis predictivo.
For more career information including salaries, visit:
OpenCourser.com/course/iqweat/introduction
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
Introduction to Deep Learning & Neural Networks with Keras.
Provides a comprehensive overview of deep learning, including its history, theory, and applications. It valuable resource for anyone who wants to learn more about this rapidly growing field.
Provides a practical introduction to machine learning, with a focus on deep learning. It covers a wide range of topics, including data preparation, model building, and evaluation.
Provides a comprehensive overview of deep learning for natural language processing using transformers. It valuable resource for anyone who wants to learn more about deep learning and how to use it in practice.
Provides a comprehensive overview of deep learning for natural language processing. It covers a wide range of topics, including text classification, sentiment analysis, and machine translation.
Provides a practical introduction to deep learning using the fastai and PyTorch libraries. It valuable resource for anyone who wants to learn more about deep learning and how to use it in practice.
Provides a visual introduction to deep learning. It uses clear and concise explanations, along with hundreds of illustrations, to help readers understand the concepts of deep learning.
Provides a comprehensive overview of deep learning, with a focus on using the R programming language. It valuable resource for anyone who wants to learn more about deep learning and how to use it in practice.
Provides a comprehensive overview of neural networks and deep learning. It valuable resource for anyone who wants to learn more about these topics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/iqweat/introduction
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Effort
2 - 3 hours/week
Level
Intermediate
Via
Coursera
Institution
IBM
Instructor
Alex Aklson
Language
English
Transcripts
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Good to know
Explored advanced topics such as backpropagation and activation functions in neural networks
Taught by an experienced instructor with proven expertise in deep learning
Course developed core skills in deep learning using the Keras library
Examined unsupervised and supervised deep learning models
Covered foundational theories and concepts for beginners
Includes hands-on labs for practical application
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