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Mark J Grover,
Miguel Maldonado,
Xintong Li,
Joseph Santarcangelo,
and
Kopal Garg
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.
Read more
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.
Read more
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.
After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning.
By the end of this course you should be able to:
Explain the kinds of problems suitable for Unsupervised Learning approaches
Explain the curse of dimensionality, and how it makes clustering difficult with many features
Describe and use common clustering and dimensionality-reduction algorithms
Try clustering points where appropriate, compare the performance of per-cluster models
Understand metrics relevant for characterizing clusters
Who should take this course?
This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning.
What skills should you have?
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.
Register for this course and see more details by visiting:
OpenCourser.com/course/ppm31d/deep
What's inside
Syllabus
Introduction to Neural Networks
This module introduces Deep Learning, Neural Networks, and their applications. You will go through the theoretical background and characteristics that they share with other machine learning algorithms, as well as characteristics that make them stand out as great modeling techniques for specific scenarios. You will also gain some hands-on practice on Neural Networks and key concepts that help these algorithms converge to robust solutions.
Read more
Syllabus
Introduction to Neural Networks
This module introduces Deep Learning, Neural Networks, and their applications. You will go through the theoretical background and characteristics that they share with other machine learning algorithms, as well as characteristics that make them stand out as great modeling techniques for specific scenarios. You will also gain some hands-on practice on Neural Networks and key concepts that help these algorithms converge to robust solutions.
Read more
Back Propagation Training and Keras
In this module, you will learn about the maths behind the popular Back Propagation algorithm used to optimize neural networks. In the Back Propagation notebook, you will also see and understand the use of activation functions. The main purpose of most activation function is to introduce non-linearity in the network so it would be capable of learning more complex patterns. Last, but not least, you will learn to use functions and APIs from the Keras library to solve tasks that involve neural networks, and these tasks start with loading images.
Neural Network Optimizers
You can leverage several options to prioritize the training time or the accuracy of your neural network and deep learning models. In this module you learn about key concepts that intervene during model training, including optimizers and data shuffling. You will also gain hands-on practice using Keras, one of the go-to libraries for deep learning.
Convolutional Neural Networks
In this module you become familiar with convolutional neural networks, also known as space invariant artificial neural networks, a type of deep neural networks, frequently used in image AI applications. There are several CNN architectures, you will learn some of the most common ones to add to your toolkit of Deep Learning Techniques.
Transfer Learning
In this module, you will understand what is transfer learning and how it works. You will implement transfer learning in 5 general steps using a variety of popular pre-trained CNN architectures, such as VGG-16 and ResNet-50. You will study the differences among those CNN architectures and see how the invention of each solves the problem of its predecessors. Last, but not least, as we are moving to working with deeper neural networks, you will also be equipped with regularization techniques to prevent overfitting of complex models and networks.
Recurrent Neural Networks and Long-Short Term Memory Networks
In this module you become familiar with Recursive Neural Networks (RNNs) and Long-Short Term Memory Networks (LSTM), a type of RNN considered the breakthrough for speech to text recongintion. RNNs are frequently used in most AI applications today, and can also be used for supervised learning.
Autoencoders
In this module you become familiar with Autoencoders, an useful application of Deep Learning for Unsupervised Learning. Autoencoders are a neural network architecture that forces the learning of a lower dimensional representation of data, commonly images. In this module you will learn some Deep learning-based techniques for data representation, how autoencoders work, and to describe the use of trained autoencoders for image applications
Generative Models and Applications of Deep Learning
In this module, you will learn about two types of generative models, which are Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). We will look at the theory behind each model and then implement them in Keras for generating artificial images. The goal is usually to generate images that are as realistic as possible. In the last lesson of this module, we will touch on additional topics in deep learning, namely using Keras in a GPU environment for speeding up model training.
Reinforcement Learning
In this module you become familiar with other novel applications of Neural Networks. You will learn about Generative Adversarial Networks, frequently referred to as GANs, which are an application of Neural Networks to generate new data. Finally, you learn about Reinforcement Learning, one of the big promises for A.I., based on training algorithms by using rewards, instead of using a method to minimize error, which is what we have been using throughout the course.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Course covers Neural Networks, a topic that is standard in multiple industries
Taught by instructors recognized in Advanced Computing and Neural Networks
Provides experience with Keras, a go-to library for Deep Learning
Teaches skills that are highly relevant to industry
Suitable for experienced learners with foundational knowledge
Advises to take other courses as pre-requisites
Save this course
Save Deep Learning and Reinforcement Learning to your list so you can find it easily later:
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Reviews summary
Challenging but enjoyable deep learning course
Learners say this course is a largely positive introductory course to deep learning and reinforcement learning. Reviews indicate that the concepts in this course are well presented but at times difficult due to the instructor's accent. A lack of practical application and a need for clarification in key areas of deep learning such as hyperparameters, architectures, and Transformer models are key complaints in reviews.
Challenging but Rewarding
"The concepts in this course are well presented but at times difficult."
Clear and Engaging
"very well presented, clear amd methodic."
"appropriate tasks."
"Well prepared, gives a good intro to multiple Deep Learning algorithms and good examples to cover the major topics."
Great Overview for Beginners
"Learners say this course is a largely positive introductory course to deep learning and reinforcement learning."
"This was a very interesting and useful introduction to the topic."
Difficult to Understand
"The only difficulty I found was with the english accent of our dear trainer."
"Sometimes it was really very difficult to comprehend what was being said and one needed to rewind the video multiple times and read the subtitles."
Lack of Details in Some Areas
"This course has a larger scope than the other ML certificate courses and is a little out of date."
"While it introduces RL, it does not discuss TD learning or Deep RL."
"RL seems "tacked on"."
"Similarly, there is a brief introduction to Attention, but no substantial discussion of Transformer models (I suggest dropping LSTM and talking just about Transformers)."
Lack of Practical Applications
"Unlike the other courses, which introduced the concepts and also covered practical steps on using these methods, the DL/RL course is a little light on the practical side of DL."
"There is little discussion of why particular architectures are chosen for specific problems or how sensitive those architectures are to various hyperparameters."
"You will know what DL, CNN, RNN (and to a lesser extent, RL) are is when you finish this course, but there's a big gap for any practical use of these tools, which was less of an issue for the (admittedly simpler/more scoped) topics in earlier courses."
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 and Reinforcement Learning with these
activities:
Volunteer at a local organization focused on using Artificial Intelligence for social good
Show steps
Gain practical experience and contribute to the broader social impact of Artificial Intelligence by volunteering with an organization dedicated to its ethical and beneficial use.
Browse courses on
Artificial Intelligence
Show steps
-
Identify local organizations working in the field of Artificial Intelligence for social good.
-
Reach out to the organization and inquire about volunteer opportunities.
-
Attend volunteer training and orientation.
-
Participate in projects and initiatives that align with your interests and skills.
-
Reflect on your experience and its impact on your understanding of Artificial Intelligence.
Review the basics of calculus and linear algebra
Show steps
Refresh your understanding of calculus and linear algebra, which are essential mathematical foundations for Deep Learning and Reinforcement Learning.
Browse courses on
Calculus
Show steps
-
Review key concepts in calculus, such as derivatives, integrals, and limits.
-
Review key concepts in linear algebra, such as matrices, vectors, and eigenvalues.
-
Practice solving problems involving calculus and linear algebra.
-
Apply your refreshed knowledge to understand the mathematical concepts underlying Deep Learning and Reinforcement Learning.
Practice backpropagation algorithm with Keras
Show steps
Practice the Back Propagation algorithm using Keras to solidify understanding of neural network training.
Browse courses on
Neural Networks
Show steps
-
Set up your Python development environment with Keras.
-
Create a simple neural network model with one hidden layer.
-
Implement the Back Propagation algorithm in Python using Keras.
-
Train your neural network model on a dataset.
-
Evaluate the performance of your neural network model.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow a tutorial on how to build a Deep Learning model for text classification using RNNs
Show steps
Gain practical experience in building and training a Deep Learning model for text classification using Recurrent Neural Networks.
Browse courses on
Recurrent Neural Networks
Show steps
-
Choose a suitable tutorial on text classification with RNNs.
-
Set up your Python development environment with necessary libraries.
-
Follow the tutorial step-by-step to build and train your RNN model.
-
Fine-tune and evaluate your model's performance on a text dataset.
Classify images using pre-trained CNNs using transfer learning
Show steps
Gain hands-on experience with transfer learning using pre-trained CNNs for image classification tasks.
Browse courses on
Transfer Learning
Show steps
-
Set up your Python development environment with Keras and TensorFlow.
-
Load a pre-trained CNN model, such as VGG16 or ResNet50.
-
Create a new dataset for your image classification task.
-
Preprocess your images for use with the pre-trained CNN.
-
Fine-tune the pre-trained CNN model on your new dataset.
-
Evaluate the performance of your fine-tuned CNN model.
Create a presentation on the applications of Deep Learning in the healthcare industry
Show steps
Explore and present real-world applications of Deep Learning in the healthcare industry to enhance comprehension and practical relevance.
Browse courses on
Deep Learning
Show steps
-
Research and identify applications of Deep Learning in healthcare, such as medical image analysis, drug discovery, and personalized medicine.
-
Gather data and examples to support your presentation.
-
Design engaging slides with clear explanations and visuals.
-
Rehearse your presentation and incorporate feedback.
-
Present your findings to a target audience.
Participate in a Kaggle competition on image segmentation using Deep Learning
Show steps
Engage in a hands-on challenge to apply your Deep Learning skills in image segmentation by participating in a Kaggle competition.
Browse courses on
Deep Learning
Show steps
-
Familiarize yourself with the Kaggle competition rules and dataset.
-
Choose an appropriate Deep Learning model for image segmentation.
-
Train and optimize your model using Kaggle's platform.
-
Evaluate your model's performance and submit your results.
-
Analyze your results and learn from the competition.
Volunteer at a local organization focused on using Artificial Intelligence for social good
Show steps
Gain practical experience and contribute to the broader social impact of Artificial Intelligence by volunteering with an organization dedicated to its ethical and beneficial use.
Browse courses on
Artificial Intelligence
Show steps
Show steps
Show steps
- Identify local organizations working in the field of Artificial Intelligence for social good.
- Reach out to the organization and inquire about volunteer opportunities.
- Attend volunteer training and orientation.
- Participate in projects and initiatives that align with your interests and skills.
- Reflect on your experience and its impact on your understanding of Artificial Intelligence.
Review the basics of calculus and linear algebra
Show steps
Refresh your understanding of calculus and linear algebra, which are essential mathematical foundations for Deep Learning and Reinforcement Learning.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Review key concepts in calculus, such as derivatives, integrals, and limits.
- Review key concepts in linear algebra, such as matrices, vectors, and eigenvalues.
- Practice solving problems involving calculus and linear algebra.
- Apply your refreshed knowledge to understand the mathematical concepts underlying Deep Learning and Reinforcement Learning.
Practice backpropagation algorithm with Keras
Show steps
Practice the Back Propagation algorithm using Keras to solidify understanding of neural network training.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Set up your Python development environment with Keras.
- Create a simple neural network model with one hidden layer.
- Implement the Back Propagation algorithm in Python using Keras.
- Train your neural network model on a dataset.
- Evaluate the performance of your neural network model.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow a tutorial on how to build a Deep Learning model for text classification using RNNs
Show steps
Gain practical experience in building and training a Deep Learning model for text classification using Recurrent Neural Networks.
Browse courses on
Recurrent Neural Networks
Show steps
Show steps
Show steps
- Choose a suitable tutorial on text classification with RNNs.
- Set up your Python development environment with necessary libraries.
- Follow the tutorial step-by-step to build and train your RNN model.
- Fine-tune and evaluate your model's performance on a text dataset.
Classify images using pre-trained CNNs using transfer learning
Show steps
Gain hands-on experience with transfer learning using pre-trained CNNs for image classification tasks.
Browse courses on
Transfer Learning
Show steps
Show steps
Show steps
- Set up your Python development environment with Keras and TensorFlow.
- Load a pre-trained CNN model, such as VGG16 or ResNet50.
- Create a new dataset for your image classification task.
- Preprocess your images for use with the pre-trained CNN.
- Fine-tune the pre-trained CNN model on your new dataset.
- Evaluate the performance of your fine-tuned CNN model.
Create a presentation on the applications of Deep Learning in the healthcare industry
Show steps
Explore and present real-world applications of Deep Learning in the healthcare industry to enhance comprehension and practical relevance.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Research and identify applications of Deep Learning in healthcare, such as medical image analysis, drug discovery, and personalized medicine.
- Gather data and examples to support your presentation.
- Design engaging slides with clear explanations and visuals.
- Rehearse your presentation and incorporate feedback.
- Present your findings to a target audience.
Participate in a Kaggle competition on image segmentation using Deep Learning
Show steps
Engage in a hands-on challenge to apply your Deep Learning skills in image segmentation by participating in a Kaggle competition.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Familiarize yourself with the Kaggle competition rules and dataset.
- Choose an appropriate Deep Learning model for image segmentation.
- Train and optimize your model using Kaggle's platform.
- Evaluate your model's performance and submit your results.
- Analyze your results and learn from the competition.
Career center
Learners who complete Deep Learning and Reinforcement Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers bridge the gap between traditional software engineering and data science. Machine Learning Engineers are capable of collecting raw data, cleaning it for analysis, building machine learning models, and deploying those models to production. This course will help students become Machine Learning Engineers by introducing them to the essential concepts of Deep Learning and Reinforcement Learning, two critical areas of Machine Learning. This course in particular will provide a foundation in the core concepts of Deep Learning and Reinforcement Learning as well as valuable hands-on experience implementing Deep Learning using Keras.
Data Scientist
Data Scientist
Data Scientists collect, clean, and analyze data, then use it to make recommendations and predictions. It is typically recommended that Data Scientists possess a strong foundation in statistics, math, and computer science. This course will introduce students to Deep Learning and Reinforcement Learning, two of the most in-demand and powerful tools in a Data Scientist's toolbox.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software systems. Software Engineers may also be involved in the research and development of new technologies. This course will help Software Engineers expand their skillset and become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning. These technologies are becoming increasingly important in a variety of industries, and Software Engineers who are proficient in them will be in high demand.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. They are employed by a variety of organizations, including investment banks, hedge funds, and insurance companies. This course will help Quantitative Analysts stay ahead of the curve by introducing them to Deep Learning and Reinforcement Learning, two cutting-edge technologies that are revolutionizing the field of quantitative analysis.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, biology, and physics. This course will help Research Scientists stay at the forefront of their fields by introducing them to Deep Learning and Reinforcement Learning, two of the most promising and rapidly developing areas of research.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. They work with stakeholders to gather requirements, analyze data, and develop solutions. This course will help Business Analysts become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to solve a variety of business problems.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use this information to make recommendations and predictions. This course will help Data Analysts become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze data more efficiently and accurately.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use this information to make predictions and draw conclusions. This course will help Statisticians become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze data more efficiently and accurately.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They work with organizations to improve efficiency and productivity. This course will help Operations Research Analysts become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to solve a variety of business problems.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment recommendations. They work with clients to assess risk and return, and to develop investment strategies. This course will help Financial Analysts become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze data more efficiently and accurately.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. They work with insurance companies and other organizations to develop insurance policies and pricing strategies. This course may be useful for Actuaries as it will introduce them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to assess risk more efficiently and accurately.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risk. They work with organizations to develop risk management strategies. This course may be useful for Risk Analysts as it will introduce them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to identify and assess risk more efficiently and accurately.
Economist
Economist
Economists use data to analyze economic trends and make predictions. They work with governments, businesses, and other organizations to develop economic policies. This course may be useful for Economists as it will introduce them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze economic data more efficiently and accurately.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. They work with businesses to develop marketing strategies and products. This course may be useful for Market Researchers as it will introduce them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze consumer data more efficiently and accurately.
UX Researcher
UX Researcher
UX Researchers use data to understand user experience. They work with product teams to design and improve user interfaces. This course may be useful for UX Researchers as it will introduce them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze user data more efficiently and accurately.
For more career information including salaries, visit:
OpenCourser.com/course/ppm31d/deep
Reading list
We've selected seven 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 and Reinforcement Learning.
Save
Provides a comprehensive overview of deep learning, covering the theoretical foundations, algorithms, and applications. It valuable resource for anyone interested in learning about deep learning, whether for research or practical applications.
Provides a comprehensive overview of reinforcement learning, covering the theoretical foundations, algorithms, and applications. It valuable resource for anyone interested in learning about reinforcement learning, whether for research or practical applications.
Provides a practical guide to machine learning using Python, covering the theoretical foundations, algorithms, and applications. It valuable resource for anyone interested in learning about machine learning, whether for research or practical applications.
Provides a practical guide to deep learning using Python, covering the theoretical foundations, algorithms, and applications. It valuable resource for anyone interested in learning about deep learning, whether for research or practical applications.
Provides a comprehensive overview of deep learning for natural language processing, covering the theoretical foundations, algorithms, and applications. It valuable resource for anyone interested in learning about deep learning for natural language processing, whether for research or practical applications.
Provides a comprehensive overview of deep learning for finance, covering the theoretical foundations, algorithms, and applications. It valuable resource for anyone interested in learning about deep learning for finance, whether for research or practical applications.
Save
Provides a comprehensive overview of deep learning for the Internet of Things, covering the theoretical foundations, algorithms, and applications. It valuable resource for anyone interested in learning about deep learning for the Internet of Things, whether for research or practical applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ppm31d/deep
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Level
Intermediate
Via
Coursera
Institution
IBM
Instructors
Mark J Grover
Miguel Maldonado
Xintong Li
Joseph Santarcangelo
Kopal Garg
Language
English
Transcripts
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Good to know
Course covers Neural Networks, a topic that is standard in multiple industries
Taught by instructors recognized in Advanced Computing and Neural Networks
Provides experience with Keras, a go-to library for Deep Learning
Teaches skills that are highly relevant to industry
Suitable for experienced learners with foundational knowledge
Advises to take other courses as pre-requisites
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