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Soham Chatterjee
Master Deep Learning with Computer Vision in our online training course. Learn to train, fine-tune, and deploy deep learning models using Amazon SageMaker.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- PyTorch
- Deep learning
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/wm939a/deep
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What's inside
Syllabus
In this lesson, we will give a background around Deep Learning for Computer Vision and NLP and preparing you to be successful in the rest of this course.
Read more
Syllabus
In this lesson, we will give a background around Deep Learning for Computer Vision and NLP and preparing you to be successful in the rest of this course.
Read more
In this lesson, you will learn about neural networks, cost functions, optimization, and how to train a neural network.
In this lesson you will learn about Model Architectures, Convolutions, and Fine-tuning.
In this lesson, you will learn how to apply all you have learned about deep learning in AWS SageMaker.
In this project, you will use AWS SageMaker to finetune a pretrained model and perform a image classification using profiling, debugging, and hyperparameter tuning.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores deep learning for computer vision and NLP, setting learners on a path to success in the field
Taught by Soham Chatterjee, an experienced instructor in deep learning
Provides hands-on experience with AWS SageMaker, a popular cloud platform for machine learning
Covers fundamental concepts of deep learning, such as neural networks, cost functions, and optimization
Guides learners through building, fine-tuning, and deploying deep learning models, providing a comprehensive understanding of the process
Assumes prior knowledge of PyTorch and deep learning, making it suitable for learners with a solid foundation in these areas
Save this course
Save Deep Learning Topics with Computer Vision and NLP to your list so you can find it easily later:
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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 Deep Learning Topics with Computer Vision and NLP with these
activities:
Connect with Experienced Professionals
Show steps
Seek guidance and advice from experts in the field.
Show steps
-
Identify potential mentors through networking or online platforms
-
Reach out and introduce yourself
Read 'Deep Learning with Python'
Show steps
Supplement your course knowledge with a comprehensive overview of deep learning in Python.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read the book
-
Take notes and highlight important concepts
Join a Study Group
Show steps
Collaborate with others to enhance your understanding of deep learning principles.
Browse courses on
Deep Learning
Show steps
-
Find a study group or create your own
-
Discuss course material, share resources, and solve problems together
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Using PyTorch
Show steps
Strengthen your understanding of PyTorch and its applications in deep learning.
Browse courses on
PyTorch
Show steps
-
Work through PyTorch tutorials
-
Build a simple neural network using PyTorch
-
Perform data preprocessing and training
Follow SageMaker Tutorials
Show steps
Learn how to use Amazon SageMaker for deep learning model deployment.
Browse courses on
Amazon SageMaker
Show steps
-
Complete the SageMaker Getting Started tutorial
-
Deploy a pre-trained model using SageMaker
-
Perform hyperparameter tuning
Participate in Kaggle Competitions
Show steps
Challenge yourself and apply your skills in real-world deep learning competitions.
Browse courses on
Kaggle
Show steps
-
Find a suitable Kaggle competition
-
Build and train a model
-
Submit your model and track your progress
Develop an Image Classification Project
Show steps
Test your knowledge by applying deep learning in a practical image classification project.
Browse courses on
Image Classification
Show steps
-
Gather and prepare a dataset
-
Build a deep learning model
-
Train and evaluate the model
-
Deploy the model using SageMaker
Connect with Experienced Professionals
Show steps
Seek guidance and advice from experts in the field.
Show steps
Show steps
Show steps
- Identify potential mentors through networking or online platforms
- Reach out and introduce yourself
Read 'Deep Learning with Python'
Show steps
Supplement your course knowledge with a comprehensive overview of deep learning in Python.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read the book
- Take notes and highlight important concepts
Join a Study Group
Show steps
Collaborate with others to enhance your understanding of deep learning principles.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find a study group or create your own
- Discuss course material, share resources, and solve problems together
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Using PyTorch
Show steps
Strengthen your understanding of PyTorch and its applications in deep learning.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Work through PyTorch tutorials
- Build a simple neural network using PyTorch
- Perform data preprocessing and training
Follow SageMaker Tutorials
Show steps
Learn how to use Amazon SageMaker for deep learning model deployment.
Browse courses on
Amazon SageMaker
Show steps
Show steps
Show steps
- Complete the SageMaker Getting Started tutorial
- Deploy a pre-trained model using SageMaker
- Perform hyperparameter tuning
Participate in Kaggle Competitions
Show steps
Challenge yourself and apply your skills in real-world deep learning competitions.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Find a suitable Kaggle competition
- Build and train a model
- Submit your model and track your progress
Develop an Image Classification Project
Show steps
Test your knowledge by applying deep learning in a practical image classification project.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Gather and prepare a dataset
- Build a deep learning model
- Train and evaluate the model
- Deploy the model using SageMaker
Career center
Learners who complete Deep Learning Topics with Computer Vision and NLP will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists typically require a master's degree in computer science or a similar discipline. They also generally require 1-2 years of experience. Those who wish to stand out in this field will have knowledge of Deep Learning, PyTorch, and computer vision. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers typically require a master's degree in computer science or a similar discipline. They also generally require 1-2 years of experience. Those who wish to stand out in this field will have knowledge of Deep Learning, PyTorch, and computer vision. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers generally require a master's degree in computer science or a similar discipline, and many entry-level jobs require 1-2 years of experience. Those with knowledge of Deep Learning, PyTorch, and computer vision may find additional success in this field. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers typically require a bachelor's degree in a computer science, engineering, or mathematics related field. They may also require 1-2 years of experience. Those with knowledge of Deep Learning, PyTorch, and computer vision may find additional success in this field. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers typically require a master's degree in computer science or a similar discipline. They also generally require 1-2 years of experience. Those who wish to stand out in this field will have knowledge of Deep Learning, PyTorch, and computer vision. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers typically require a master's degree in computer science or a similar discipline. They also generally require 1-2 years of experience. Those who wish to stand out in this field will have knowledge of Deep Learning, PyTorch, and computer vision. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Research Scientist
Research Scientist
Research Scientists typically require a PhD in computer science or a similar discipline. They may also require 1-2 years of experience. Those with knowledge of Deep Learning, PyTorch, and computer vision may find additional success in this field. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Data Analyst
Data Analyst
Data Analysts typically require a bachelor's degree in a computer science, engineering, or mathematics related field. They may also require 1-2 years of experience. Those with knowledge of Deep Learning, PyTorch, and computer vision may find additional success in this field. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Technical Writer
Technical Writer
Technical Writers typically require a bachelor's degree in a computer science or similar related field. Those with knowledge of Deep Learning, PyTorch, and computer vision may find additional success in this field. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Consultant
Consultant
Consultants typically require a bachelor's degree in a business or computer science related field. They may also require 1-2 years of experience. Those with knowledge of Deep Learning, PyTorch, and computer vision may find additional success in this field. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Business Analyst
Business Analyst
Business Analysts typically require a bachelor's degree in a business or computer science related field. They may also require 1-2 years of experience. Those with knowledge of Deep Learning, PyTorch, and computer vision may find additional success in this field. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Software Engineer
Software Engineer
Software Engineers typically require a bachelor's degree in a computer science, engineering, or mathematics related field. They may also require 1-2 years of experience. Those with knowledge of Deep Learning, PyTorch, and computer vision may find additional success in this field. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Product Manager
Product Manager
Product Managers typically require a bachelor's degree in a business or computer science related field. They may also require 1-2 years of experience. Those with knowledge of Deep Learning, PyTorch, and computer vision may find additional success in this field. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Project Manager
Project Manager
Project Managers typically require a bachelor's degree in a business or computer science related field. They may also require 1-2 years of experience. Those with knowledge of Deep Learning, PyTorch, and computer vision may find additional success in this field. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
Sales Engineer
Sales Engineer
Sales Engineers typically require a bachelor's degree in a computer science or engineering related field. They may also require 1-2 years of experience. Those with knowledge of Deep Learning, PyTorch, and computer vision may find additional success in this field. This course teaches you how to train, fine-tune, and deploy deep learning models using Amazon SageMaker. These skills are essential for those who wish to succeed in this field.
For more career information including salaries, visit:
OpenCourser.com/course/wm939a/deep
Reading list
We've selected ten 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
Deep Learning Topics with Computer Vision and NLP.
Provides a comprehensive introduction to deep learning for computer vision, covering topics such as image classification, object detection, and image segmentation. It is written in a clear and concise style, and it includes numerous code examples and exercises.
Provides a comprehensive introduction to natural language processing with PyTorch, covering topics such as text classification, named entity recognition, and machine translation. It is written in a clear and concise style, and it includes numerous code examples and exercises.
Provides a comprehensive introduction to deep learning for coders, covering topics such as image classification, object detection, and image segmentation. It is written in a clear and concise style, and it includes numerous code examples and exercises.
Provides a comprehensive introduction to deep learning for natural language processing, covering topics such as text classification, named entity recognition, and machine translation. It is written in a clear and concise style, and it includes numerous code examples and exercises.
Save
Provides a comprehensive introduction to computer vision, covering topics such as image formation, image processing, and object recognition. It is written in a clear and concise style, and it includes numerous code examples and exercises.
Provides a comprehensive introduction to natural language processing, covering topics such as text classification, named entity recognition, and machine translation. It is written in a clear and concise style, and it includes numerous code examples and exercises.
Save
Provides a comprehensive introduction to data mining, covering topics such as data preprocessing, data clustering, and data classification. It is written in a clear and concise style, and it includes numerous code examples and exercises.
Provides a comprehensive introduction to machine learning from a probabilistic perspective, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It is written in a clear and concise style, and it includes numerous code examples and exercises.
Provides a comprehensive introduction to pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It is written in a clear and concise style, and it includes numerous code examples and exercises.
Provides a comprehensive introduction to statistical learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It is written in a clear and concise style, and it includes numerous code examples and exercises.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wm939a/deep
Share
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Effort
4 weeks
Via
Udacity
Instructor
Soham Chatterjee
Language
English
Good to know
Explores deep learning for computer vision and NLP, setting learners on a path to success in the field
Taught by Soham Chatterjee, an experienced instructor in deep learning
Provides hands-on experience with AWS SageMaker, a popular cloud platform for machine learning
Covers fundamental concepts of deep learning, such as neural networks, cost functions, and optimization
Guides learners through building, fine-tuning, and deploying deep learning models, providing a comprehensive understanding of the process
Assumes prior knowledge of PyTorch and deep learning, making it suitable for learners with a solid foundation in these areas
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