Deep Learning with TensorFlow
Master deep learning for unstructured data analysis using TensorFlow, covering neural networks, CNN, RNN, and advanced architectures.
Master deep learning for unstructured data analysis using TensorFlow, covering neural networks, CNN, RNN, and advanced architectures.
This intermediate-level course teaches deep learning implementation using TensorFlow, focusing on handling unstructured data like images, sound, and text. Students learn TensorFlow's core concepts, from basic operations to advanced neural architectures. The curriculum covers curve fitting, regression, classification, and error function minimization. Special emphasis is placed on deep architectures including Convolutional Networks, Recurrent Networks, and Autoencoders. Students gain practical experience in applying TensorFlow for backpropagation and neural network training.
4.4
(14 ratings)
53,104 already enrolled
Instructors:
English
English
Powered by
What you'll learn
Master TensorFlow fundamentals and execution pipelines
Implement curve fitting, regression, and classification models
Develop various deep learning architectures including CNN and RNN
Apply backpropagation techniques for neural network training
Work with unstructured data using deep learning methods
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
Closed caption
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 7 modules in this course
This comprehensive course focuses on implementing deep learning solutions using TensorFlow, one of the leading libraries for machine learning. The curriculum covers fundamental TensorFlow concepts and operations, progressing through various neural network architectures. Students learn to handle unstructured data using deep learning techniques, from basic regression and classification to advanced architectures like CNNs, RNNs, and Autoencoders. The course emphasizes practical application, teaching students how to implement backpropagation, tune neural networks, and develop solutions for real-world data analysis problems.
Advanced Keras Functionalities
Module 1
Advanced CNNs in Keras
Module 2
Transformers in Keras
Module 3
Unsupervised Learning and Generative Models in Keras
Module 4
Advanced Keras Techniques
Module 5
Introduction to Reinforcement Learning with Keras
Module 6
Final Project and Assignment
Module 7
Fee Structure
Payment options
Financial Aid
Instructors
Chief Data Scientist at IBM Specializing in Data Science and Parallel Processing Architectures
Romeo Kienzler is the Chief Data Scientist and Course Lead at IBM, where he leverages nearly two decades of experience in software engineering, database administration, and information integration. He holds a Master of Science from the Swiss Federal Institute of Technology (ETH) in Information Systems, Bioinformatics, and Applied Statistics. Since joining IBM in 2012, Romeo has focused his research on massive parallel data processing architectures and has published numerous works in the field through international publishers and conferences. In addition to his professional contributions, he is actively involved in various open-source projects. On Coursera, he teaches several courses, including Deep Learning with Keras and TensorFlow, Introduction to Big Data with Spark and Hadoop, Scalable Machine Learning on Big Data using Apache Spark, and Tools for Data Science, all designed to equip learners with essential skills in data science and machine learning
38 Courses
Pioneering Data Scientist Leading Enterprise Analytics Innovation
Saeed Aghabozorgi, PhD, serves as a Senior Data Scientist at IBM, where he specializes in developing enterprise-level applications that transform complex data into actionable business knowledge. His expertise spans data mining, machine learning, and statistical modeling, with particular emphasis on large-scale datasets. As an accomplished educator, his courses have reached over 100,000 learners worldwide, maintaining an impressive 4.7 instructor rating. His most notable contribution includes the Machine Learning with Python course, which has enrolled more than 482,000 students and covers comprehensive topics from supervised learning to advanced clustering techniques. Through his work at IBM, he continues to advance the field of data science by developing cutting-edge analytical methods and sharing his expertise through educational initiatives that bridge the gap between theoretical knowledge and practical application.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.