Deep Learning Fundamentals with Keras

About

Please Note: Learners who successfully complete this IBM course can earn a skill badge "a detailed, verifiable and digital credential that profiles the knowledge and skills you've acquired in this course. Enroll to learn more, complete the course and claim your badge!

Looking to kickstart a career in deep learning? Look no further. This course will introduce you to the field of deep learning and teach you the fundamentals. You will learn about some of the exciting applications of deep learning, the basics fo neural networks, different deep learning models, and how to build your first deep learning model using the easy yet powerful library Keras.

This course will presentsimplified explanations to some oftoday's hottest topics in data science, including:

• What is deep learning?
• How do neural networks learn and what are activation functions?
• What are deep learning libraries and how do they compare to one another?
• What are supervised and unsupervised deep learning models?
• How to use Keras to build, train, and test deep learning models?

The demand fordeep learning skills-- and the job salaries of deep learning practitioners -- arecontinuing to grow, as AI becomes more pervasive in our societies. This course will help you build the knowledge you need to future-proofyour career.

What you will learn

• You will learn about exciting applications of deep learning and why it is really rewarding to learn how to leverage deep learning skills.
• You will learn about neural networks and how theylearn and update their weights and biases.
• You will learn about thevanishing gradient problem.
• You will learn about building a regression model using the Keras library.
• You will learn about building a classification model using the Keras library.
• You will learn about supervised deep learning models, such as convolutional neural networks and recurrent neural networks, and how to build a convolutional neural network using the Keras library.
• You will learn about unsupervised learning models such as autoencoders.

Skills you learn

• Artificial Intelligence
• Data Science
• Keras (Neural Network Library)
• Artificial Neural Networks
• Deep Learning

Syllabus

Module1 - Introduction to Deep Learning
- Introduction to Deep Learning
- Biological Neural Networks
- Artificial Neural Networks - Forward Propagation

Module2 -Artificial Neural Networks
- Gradient Descent
- Backpropagation
- Vanishing Gradient
- Activation Functions

Module3 - Deep Learning Libraries
- Introduction to Deep Learning Libraries
- Regression Models with Keras
- Classification Models with Keras

Module4 -Deep Learning Models
- Shallow and Deep Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Autoencoders

Auto Summary

Embark on your deep learning journey with "Deep Learning Fundamentals with Keras," a comprehensive course designed to introduce you to the essential concepts and applications in the field of data science and AI. Guided by expert instructors from IBM, this course will equip you with a strong foundation in deep learning, making it an ideal starting point for aspiring AI professionals. Throughout the 30-hour course, you'll delve into the basics of neural networks, explore various deep learning models, and learn to build, train, and test your first deep learning model using the powerful Keras library. The course offers simplified explanations of complex topics, including: - Understanding deep learning and its significance - The mechanics of neural networks and activation functions - Comparing different deep learning libraries - Distinguishing between supervised and unsupervised deep learning models Upon successful completion, you'll earn a verifiable digital badge from IBM, showcasing your newly acquired skills and knowledge. Whether you're looking to enhance your current skill set or pivot into a new career, this course will help you stay ahead in the rapidly evolving field of AI. Available through edX, this course is part of their Starter subscription, tailored for learners at the awareness level. Join now to future-proof your career and tap into the growing demand for deep learning expertise.