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.
Offered By
IBMAbout this Course
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Intermediate Level
Approx. 8 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Skills you will gain
Deep LearningArtificial Neural NetworkArtificial Intelligence (AI)Machine Learningkeras
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 8 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Offered by
IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Syllabus - What you will learn from this course
2 hours to complete
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.
2 hours to complete
4 videos (Total 17 min), 1 reading, 2 quizzes
1 hour to complete
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.
1 hour to complete
4 videos (Total 22 min)
3 hours to complete
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.
3 hours to complete
3 videos (Total 16 min)
2 hours to complete
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.
2 hours to complete
4 videos (Total 17 min)
Reviews
TOP REVIEWS FROM INTRODUCTION TO DEEP LEARNING & NEURAL NETWORKS WITH KERAS
by ABMar 15, 2020
Interesting course. Forward propagation, gradient descent, backward propagation, the vanishing gradient problem, (+ Regression, Classification, and CNN with Keras) explained clearly.
by AMJun 24, 2020
Good course. It is a very direct approach. It is a basic introduction to keras. Doing the labs is recommended, and also previous knowledge about machine learning is encouraged.
by ASMar 19, 2020
A good course. Could be better if it was explained how to select the optimal number of layers and nodes. This was not covered and explained anywhere. Overall it was good.
by MCFeb 19, 2020
Best suited for beginner in Deep learning with Keras. Good content, hands on experience with Jupiter notebook with IBM Developer tool. Worked Exercise code for CNN & RNN.
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