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.
About this Course
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Course 3 of 6 in the
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Intermediate Level
Approx. 9 hours to complete
English
Subtitles: English
Skills you will gain
Deep LearningArtificial Neural NetworkArtificial Intelligence (AI)Machine Learningkeras
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 9 hours to complete
English
Subtitles: English
Offered by
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
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
4 videos
Welcome2m
Introduction to Deep Learning4m
Neurons and Neural Networks3m
Artificial Neural Networks5m
1 reading
Syllabus10m
1 practice exercise
Introduction to Neural Networks and Deep Learning30m
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)
1 practice exercise
Artificial Neural Networks30m
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)
1 practice exercise
Keras and Deep Learning Libraries30m
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)
4 videos
Convolutional Neural Networks8m
Recurrent Neural Networks2m
Autoencoders2m
1 practice exercise
Deep Learning Models30m
Reviews
TOP REVIEWS FROM INTRODUCTION TO DEEP LEARNING & NEURAL NETWORKS WITH KERAS
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.
by RSFeb 20, 2020
I took this course for understanding the TensorFlow properly. Now I am in the situation to understand all the frameworks. Thanks a lot for providing me this free course
by ASMay 10, 2020
Good course for absolute beginners. Would have liked an extra week or two to 'manually build' some of the key neural network concepts from scratch as in the first week.
About the IBM AI Engineering Professional Certificate
Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.
You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.
Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering.
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