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.
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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
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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
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.
52 Courses
A Distinguished Data Scientist Advancing AI and Open Source Technologies
Romeo Kienzler serves as Senior Scientific Software Engineer and STSM at IBM Research Europe, bringing extensive expertise in data science, AI, and cloud computing. After earning his M.Sc. in Information Systems, Bioinformatics & Applied Statistics from ETH Zurich, he has built an impressive career spanning roles including CTO and Chief Data Scientist at IBM's Center for Open Source Data and AI Technologies (CODAIT) and Global Chief Data Scientist for IBM Watson IoT. His academic contributions include serving as Associate Professor for Artificial Intelligence at the Swiss University of Applied Sciences Berne, while his research focuses on cloud-scale machine learning and deep learning using open source technologies. As lead instructor for IBM's Advanced Data Science specialization on Coursera, he teaches courses on Scalable Data Science, Advanced Machine Learning, Signal Processing, and Applied AI with Deep Learning. His expertise spans massive parallel data processing architectures, machine learning, and blockchain technologies, with significant contributions to open source projects and international publications. A member of both the IBM Technical Expert Council and IBM Academy of Technology, he continues to advance the field through his work on TensorFlow, Keras, DeepLearning4J, Apache SystemML, and the Apache Spark stack, while advocating for ethical machine learning, transparency, and privacy in AI development.
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