Overview
Learning Path
Career Outlook
Course Overview:
The Deep Learning with Keras and TensorFlow course helps you master the concepts of artificial neural networks, Keras and Tensorflow frameworks, autoencoders, and deep learning techniques. It enables you to build deep learning models, implement deep learning algorithms and interpret the results.
Course Highlights
- 34 hours of applied learning
- Real-life industry-based projects
- Flexibility to choose classes
- Dedicated mentoring session from our industry experts
Course Delivery Method:
Online Bootcamp– Online self-paced video-based learning and live virtual classroom conducted by a leading coach in the industry. This course includes Simpliearn’s Integrated lab platform.
Prerequisites:
To take this Deep Learning with Keras and Tensorflow course, you should have a basic understanding of programming fundamentals, statistics, mathematics, and machine learning concepts.
Skills Covered:
- Keras framework
- TensorFlow framework
- PyTorch
- Image classification
- Convolutional networks
- Recurrent neural networks
Learning Path
Who Will Benefit:
The Deep Learning with Keras and Tensorflow course is best suited for data analysts, data scientists, statisticians with an interest in deep learning, and software engineers.
Key Learning Outcomes:
- Understand the concepts of Keras and TensorFlow, its main functions, operations, and the
- execution pipeline
- Implement deep learning algorithms, understand neural networks, and traverse the layers of data abstraction
- Master and comprehend advanced topics such as convolutional neural networks, recurrent
- neural networks, training deep networks, and high-level interfaces
- Build deep learning models using the Keras and TensorFlow frameworks and interpret the results
- Understand the language and fundamental concepts of artificial neural networks, application of autoencoders, and PyTorch and its elements
- Troubleshoot and improve deep learning models
- Build your own deep learning project
- Differentiate between machine learning, deep learning, and artificial intelligence
Certification Criteria:
- At least 85 percent attendance of one live virtual classroo
- A score of at least 75 percent in course-end assessment
- Successful evaluation in the course-end project
Tools covered
