TensorFlow 2 for Deep Learning Specialization
The specialization caters to machine learning professionals seeking TensorFlow skills through a structured progression from basics to advanced topics, emphasizing practical application through capstone projects.
Description for TensorFlow 2 for Deep Learning Specialization
Features of Course
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Imperial College London
Duration: 3 months at 10 hours a week
Schedule: Flexible
Pricing for TensorFlow 2 for Deep Learning Specialization
Use Cases for TensorFlow 2 for Deep Learning Specialization
FAQs for TensorFlow 2 for Deep Learning Specialization
Reviews for TensorFlow 2 for Deep Learning Specialization
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for TensorFlow 2 for Deep Learning Specialization
Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world
Become an adept in the field of machine learning. Break into the field of artificial intelligence by mastering the fundamentals of deep learning. Recently upgraded with state-of-the-art methodologies!
Gain the skills needed for a machine learning engineering role and prepare for the Google Cloud Professional Machine Learning Engineer certification exam by learning to design, build, and productize ML models using Google Cloud technologies.
Begin your professional journey as an AI engineer. Master the art of generating business insights from large datasets by employing deep learning and machine learning models.
Gain practical experience in optimizing, deploying, and scaling machine learning models using Google Cloud Platform through a structured five-course specialization with hands-on labs and a focus on advanced topics and recommendation systems.
Construct and train neural networks and tree ensemble methods using TensorFlow, while applying effective machine learning practices for real-world data generalization.
Explore the differences between ML in Finance and Technology, evaluation methods for regression and classification models, Reinforcement Learning for stock trading, and modeling techniques for market frictions and feedback in option trading.
Learn to develop, train, and assess neural networks using TensorFlow to resolve classification issues by understanding the fundamental principles of neural networks.
Learn to effectively use TensorFlow for constructing and optimizing neural networks, including applications in computer vision with convolutional techniques.
Gain skills in computer vision, convolutional neural networks, and AI applications through the Deep Learning Specialization to advance your career in AI technology.
Featured Tools
Learn to write efficient queries, perform correlations, create visualizations, and leverage sub-searches and lookups to become a Search Expert.
The course provides comprehensive coverage of AI and ML's increasing integration, structured into three sections focusing on business strategy, fundamental technologies, and hands-on projects, to aid in strategy development and technical planning.
Develop and evaluate a neural network that can identify handwritten numerals, implement One Hot Encoding for classification, and evaluate the efficacy of the model through practical exercises.
Effectively employ Azure ML Studio for predictive model development, experiment establishment, and operationalizing machine learning workflows through drag-and-drop modules.
Gain practical skills in relational and NoSQL databases, Big Data tools, and data pipelines for comprehensive data engineering tasks.
Compare & Review AI Tool Directory Best AI Course Marketplace
Aimed Labs