Building Deep Learning Models with TensorFlow from Coursera

What's inside

Syllabus

Introduction

In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models. You will also learn about the fundamentals of Deep Learning.

Supervised Learning Models

In this module, you will learn about about Convolutional Neural Networks, and the building blocks of a convolutional neural network, such as convolution and feature learning. You will also learn about the popular MNIST database. Finally, you will learn how to build a Multi-layer perceptron and convolutional neural networks in Python and using TensorFlow.

Supervised Learning Models (Cont'd)

In this module, you will learn about the recurrent neural network model, and special type of a recurrent neural network, which is the Long Short-Term Memory model. Also, you will learn about the Recursive Neural Tensor Network theory, and finally, you will apply recurrent neural networks to language modelling.

Unsupervised Deep Learning Models

In this module, you will learn about the applications of unsupervised learning. You will learn about Restricted Boltzmann Machines (RBMs), and how to train an RBM. Finally, you will apply Restricted Boltzmann Machines to build a recommendation system.

Unsupervised Deep Learning Models (Cont'd) and scaling

In this module, you will mainly learn about autoencoders and their architecture.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Demonstrates how Deep Learning can be applied to real-world problems, making it highly relevant in industry

Uses a popular library, TensorFlow, which is widely used in the industry and research community

Covers a wide range of Deep Learning models, from Convolutional Neural Networks to Recurrent Neural Networks

Taught by experienced instructors, Romeo Kienzler, Alex Aklson, Samaya Madhavan, and JEREMY NILMEIER, who are recognized for their work in the field

Requires learners to come in with some prior knowledge of Python and Machine Learning

Reviews summary

Comprehensively effective deep learning models with tensorflow

learners say that this course largely positive for understanding the creation of deep learning models with TensorFlow. Engaging assignments include coding neural networks and working with autoencoders. While the information provided largely positive, learners should be aware that some of the content and code is dependent on TensorFlow version 1.x and might need to be updated.

According to reviewers, the course provides clear explanations and covers a wide range of topics.

"The detail of prsenetation is awsome and make learning interesting."

"This is just introductory course, wish to see more content and in details concept."

"I would like to thank the lecturer for this fruitful course."

"I have enjoyed it, Thanks a lot!"

Learners have expressed concerns about the quality and accuracy of code examples in the course. Some learners have reported issues with errors and bugs in the code.

"I get some basic Idea about deep learning"

"The codes should be provided with Tensorflow 2.0."

"the code is SEVERELY out of date."

"This course was very informative and the labs are really well written.... however the code is SEVERELY out of date."

Unfortunately, some of the course content is outdated, including code examples that are based on TensorFlow 1.x and no longer applicable to the current version.

"The Material needs to be updated to Tensorflow 2.0 at least."

"The tensorflow version is outdated"

"The code in the course is obsolete using an old version of TF."

"All the codes are for tensorflow version 1 and not 2 which essentially makes them outdated"

Career center

Learners who complete Building Deep Learning Models with TensorFlow will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine learning engineers design and build machine learning models to solve real-world problems. This course can help you develop the skills needed to become a machine learning engineer. It provides hands-on experience with TensorFlow, a popular deep learning library, and covers topics such as data preprocessing, feature engineering, model training, and evaluation.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data scientists use advanced analytical techniques to extract insights from data. Deep learning is a powerful tool for data scientists, and this course can help you build a strong foundation in the field. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. You'll also gain hands-on experience with TensorFlow, a popular deep learning library.

See salaries and explore the career path for Data Scientist

Researcher

Researchers use scientific methods to study different aspects of the world. Deep learning is a powerful tool for researchers, and this course can help you build a foundation in the field. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. You'll also gain hands-on experience with TensorFlow, a popular deep learning library.

See salaries and explore the career path for Researcher

Statistician

Statisticians use data to solve problems and make informed decisions. This course can help you build a foundation in deep learning, a powerful technique used in statistics. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of statistics.

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Data Analyst

Data analysts use data to solve business problems and make informed decisions. This course can help you develop the skills needed to analyze and interpret data using deep learning. It provides hands-on experience with TensorFlow, a popular deep learning library, and covers topics such as data preprocessing, feature engineering, and model evaluation.

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Data Engineer

Data engineers design, build, and maintain data pipelines. This course can help you build a foundation in deep learning, a powerful technique used in data engineering. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of data engineering.

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Quantitative Analyst

Quantitative analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can help you build a foundation in deep learning, a powerful technique used in quantitative finance. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the financial industry.

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Financial Analyst

Financial analysts use data to make investment decisions and advise clients. This course can help you build a foundation in deep learning, a powerful technique used in financial analysis and forecasting. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the financial industry.

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Software Engineer

Software engineers design, develop, and maintain software systems. This course can help you build a foundation in deep learning, a powerful technique used in software development. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in software development.

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Business Analyst

Business analysts use data to help businesses make better decisions. This course can help you build a foundation in deep learning, a powerful technique used in business analysis. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of business analysis.

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Systems Analyst

Systems analysts design and implement computer systems. This course can help you build a foundation in deep learning, a powerful technique used in systems analysis and design. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of systems analysis.

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Web Developer

Web developers design and develop websites. This course can help you build a foundation in deep learning, a powerful technique used in web development. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of web development.

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Product Manager

Product managers develop and manage products. This course can help you build a foundation in deep learning, a powerful technique used in product management. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of product management.

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Actuary

Actuaries use mathematical and statistical models to assess risk in the insurance and finance industries. This course can help you build a foundation in deep learning, a powerful technique used in risk assessment and modeling. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the actuarial field.

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Teacher

Teachers educate students in various subjects. This course can help you build a foundation in deep learning, a powerful technique used in education. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of education.

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