Deep Learning with TensorFlow and Keras
Master advanced deep learning techniques using Keras and TensorFlow 2.x for computer vision, NLP, and reinforcement learning.
Master advanced deep learning techniques using Keras and TensorFlow 2.x for computer vision, NLP, and reinforcement learning.
This comprehensive course equips professionals with advanced Keras and TensorFlow 2.x techniques for building and optimizing sophisticated machine learning models. Students begin by mastering Keras's functional API for designing complex architectures and creating custom layers and models tailored to unique challenges. The curriculum progresses through specialized deep learning domains, starting with advanced convolutional neural networks (CNNs) for computer vision, including data augmentation techniques, transfer learning with pre-trained models, and transpose convolution. Participants then explore Transformer architecture for sequential data processing, focusing on time series prediction and text generation applications. The course also covers unsupervised learning, teaching students to implement autoencoders, cutting-edge diffusion models, and generative adversarial networks (GANs). Advanced optimization techniques are addressed through custom training loops and hyperparameter tuning using Keras Tuner. The final modules introduce reinforcement learning concepts, including Q-Learning algorithms and deep Q-networks (DQNs). Throughout the course, students apply their knowledge in practical lab exercises, culminating in a peer-graded final project on transfer learning for waste product classification. With machine learning engineer salaries currently ranging from $100,809 to over $254,000, this course provides the practical skills needed to tackle complex real-world challenges across various deep learning domains.
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What you'll learn
Create custom layers and models in Keras and integrate them with TensorFlow 2.x Develop advanced convolutional neural networks using sophisticated data augmentation techniques Implement transfer learning with pre-trained models for efficient computer vision applications Master transpose convolution for advanced image processing tasks Build and train Transformer models for sequential data processing and time series prediction Develop generative models including autoencoders, diffusion models, and GANs Create custom training loops and optimize model performance through hyperparameter tuning Implement reinforcement learning algorithms including Q-Learning and Deep Q-Networks
Skills you'll gain
This course includes:
PreRecorded video
Quizzes, Labs, Peer-graded project
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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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 8 modules in this course
This advanced course on deep learning with TensorFlow and Keras is structured to provide comprehensive training in cutting-edge neural network techniques across multiple domains. The curriculum starts with advanced Keras functionalities, teaching students to leverage the functional API and subclassing API for complex model architectures and to create custom layers for specialized tasks. The course then delves into advanced convolutional neural networks, covering sophisticated data augmentation techniques, transfer learning with pre-trained models, and transpose convolution for upsampling tasks. Students explore Transformer architectures for sequential data processing, with applications in natural language processing and time series prediction. The program also covers unsupervised learning and generative models, including autoencoders, diffusion models, and generative adversarial networks (GANs). Advanced optimization techniques are addressed through custom training loops and hyperparameter tuning. The final section introduces reinforcement learning concepts with implementations of Q-Learning algorithms and deep Q-networks. Throughout the course, theoretical concepts are reinforced through hands-on labs, practice quizzes, and discussion prompts. The program culminates in a final project on waste product classification using transfer learning, allowing students to demonstrate their ability to apply advanced techniques to real-world 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
Introduction to Reinforcement Learning with Keras
Module 6
Final Project and Assignment
Module 7
Course Wrap Up
Module 8
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.
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