Deep Learning with Tensorflow from edX

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Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures withinunlabeled and unstructured data (i.e. images, sound, and text), which consitutes the vast majority of data in the world.

TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

In this TensorFlow course, you will learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.

This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

What's inside

Learning objectives

Explain foundational tensorflow concepts such as the main functions, operations and the execution pipelines.
Describe how tensorflow can be used in curve fitting, regression, classification and minimization of error functions.

Understand different types of deep architectures, such as convolutional networks, recurrent networks and autoencoders.
Apply tensorflow for backpropagation to tune the weights and biases while the neural networks are being trained.

Explain foundational tensorflow concepts such as the main functions, operations and the execution pipelines.
Describe how tensorflow can be used in curve fitting, regression, classification and minimization of error functions.
Understand different types of deep architectures, such as convolutional networks, recurrent networks and autoencoders.
Apply tensorflow for backpropagation to tune the weights and biases while the neural networks are being trained.

Syllabus

Module 1 – Introduction to TensorFlowHelloWorld with TensorFlow Linear RegressionNonlinear Regression Logistic Regression

Module 2 – Convolutional Neural Networks (CNN)CNN Application Understanding CNNs

Module 3 – Recurrent Neural Networks (RNN)Intro to RNN Model Long Short-Term memory (LSTM)

Module 4 - Restricted Boltzmann MachineRestricted Boltzmann Machine Collaborative Filtering with RBM

Module 5 - AutoencodersIntroduction to Autoencoders and Applications Autoencoders* Deep Belief Network

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides foundational concepts, functions, and operations for beginners in TensorFlow

Covers various types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders

Explores the application of TensorFlow for backpropagation to adjust weights and biases during Neural Network training

Includes hands-on labs and interactive materials for practical application of concepts

May require prior experience in programming and data analysis for optimal comprehension

Does not provide in-depth coverage of advanced topics in TensorFlow

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 with Tensorflow with these activities:

Seek a Mentor in Deep Learning

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Accelerate your learning by establishing a connection with an experienced deep learning practitioner.

Browse courses on Deep Learning

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Identify potential mentors through online communities, research groups, or industry connections.
Reach out to mentors and express your interest in learning from them.
Establish a structured mentorship plan that outlines the goals and expectations of both parties.

TensorFlow Concepts Review

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Reinforce your understanding of foundational TensorFlow concepts by revisiting core principles.

Browse courses on TensorFlow

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Review class materials on TensorFlow concepts such as functions, operations, and execution pipelines.
Complete online tutorials or exercises on TensorFlow basics.

Study Group on Recurrent Neural Networks (RNNs)

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Enhance your understanding of RNNs through collaborative discussions and problem-solving with peers.

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Form a study group with other course participants.
Meet regularly to discuss RNN concepts, solve problems, and share insights.
Work together on mini-projects or case studies related to RNNs.

Five other activities

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Tutorials on Backpropagation and Weight Tuning

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Strengthen your grasp of backpropagation and weight tuning by exploring guided tutorials.

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Search for and identify comprehensive tutorials on backpropagation.
Step through the tutorials, implementing the concepts in your own code.
Experiment with different weight tuning methods to observe their impact on model performance.

Convolutional Neural Network (CNN) Project

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Deepen your understanding of CNNs by implementing a project that utilizes them to solve a problem.

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Identify a problem or application that can be solved using CNNs.
Gather and prepare the necessary data for training the CNN model.
Design and implement the CNN architecture.
Evaluate the performance of the CNN model and make necessary adjustments.

Autoencoder Resource Collection

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Enrich your knowledge of autoencoders by gathering and organizing valuable resources.

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Conduct research to identify relevant articles, tutorials, and code repositories on autoencoders.
Create a structured collection, categorizing the resources based on their focus and level of difficulty.
Share the resource collection with other course participants or the broader learning community.

Contribute to Open Source Deep Learning Projects

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Make a meaningful contribution to the deep learning community and gain valuable hands-on experience.

Browse courses on Deep Learning

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Identify open source deep learning projects that align with your skills and interests.
Join the project community and contribute to code development, bug fixes, or documentation.
Participate in discussions and share your knowledge to support the project's growth.

Kaggle Competition on Deep Learning Applications

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Push your skills to the limit and gain practical experience by participating in a Kaggle competition.

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Identify a Kaggle competition that aligns with the topics covered in the course.
Form a team or work independently to develop and submit solutions.
Analyze the competition results and learn from the approaches of other participants.

Career center

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

Data Scientist

Data Scientists leverage their knowledge of statistics, mathematics, and computer science to collect, clean, and analyze data. They are also responsible for interpreting the data, making predictions, developing models, and building applications. This course may be useful for aspiring Data Scientists as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Data Scientists can build and train complex models to solve real-world problems.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, build, and maintain machine learning models. They use a variety of tools and techniques to collect, clean, and analyze data, and then develop and train models to make predictions or solve problems. This course may be useful for aspiring Machine Learning Engineers as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Machine Learning Engineers can build and train complex models to solve real-world problems.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to identify and solve business problems. They work with stakeholders to understand their needs and develop solutions that meet those needs. This course may be useful for aspiring Business Analysts as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Business Analysts can build and train complex models to solve real-world problems.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve problems in business and industry. They use this information to make recommendations and improve efficiency. This course may be useful for aspiring Operations Research Analysts as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Operations Research Analysts can build and train complex models to solve real-world problems.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians collect, analyze, and interpret data. They use this information to make recommendations and solve problems. This course may be useful for aspiring Statisticians as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Statisticians can build and train complex models to solve real-world problems.

See salaries and explore the career path for Statistician

Software Engineer

Software Engineers design, develop, and maintain software applications. They use a variety of programming languages and tools to create software that meets the needs of users. This course may be useful for aspiring Software Engineers as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Software Engineers can build and train complex models to solve real-world problems.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. They use this information to make recommendations and solve problems. This course may be useful for aspiring Data Analysts as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Data Analysts can build and train complex models to solve real-world problems.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial Analysts use mathematical and statistical models to analyze financial data. They use this information to make investment decisions. This course may be useful for aspiring Financial Analysts as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Financial Analysts can build and train complex models to solve real-world problems.

See salaries and explore the career path for Financial Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines. They work with data scientists and other stakeholders to ensure that data is available and accessible for analysis. This course may be useful for aspiring Data Engineers as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Data Engineers can build and train complex models to solve real-world problems.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They use this information to make investment decisions. This course may be useful for aspiring Quantitative Analysts as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Quantitative Analysts can build and train complex models to solve real-world problems.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk. They use this information to calculate insurance premiums and make recommendations to businesses. This course may be useful for aspiring Actuaries as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Actuaries can build and train complex models to solve real-world problems.

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Computer Vision Engineer

Computer Vision Engineers design, build, and maintain computer vision systems. These systems use deep learning to analyze images and videos to identify objects, faces, and other features. This course may be useful for aspiring Computer Vision Engineers as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Computer Vision Engineers can build and train complex models to solve real-world problems.

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Artificial Intelligence Researcher

Artificial Intelligence Researchers develop new artificial intelligence algorithms and techniques. They work with other researchers and engineers to push the boundaries of artificial intelligence and develop new applications for the technology. This course may be useful for aspiring Artificial Intelligence Researchers as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Artificial Intelligence Researchers can build and train complex models to solve real-world problems.

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Natural Language Processing Engineer

Natural Language Processing Engineers design, build, and maintain natural language processing systems. These systems use deep learning to analyze text and speech to identify patterns and extract meaning. This course may be useful for aspiring Natural Language Processing Engineers as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Natural Language Processing Engineers can build and train complex models to solve real-world problems.

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Deep Learning Researcher

Deep Learning Researchers develop new deep learning algorithms and techniques. They work with other researchers and engineers to push the boundaries of deep learning and develop new applications for the technology. This course may be useful for aspiring Deep Learning Researchers as it provides a strong foundation in TensorFlow, one of the most popular libraries for deep learning. With TensorFlow, Deep Learning Researchers can build and train complex models to solve real-world problems.

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