Deep Learning With Tensorflow
Instructor-led training in Deep Learning With Tensorflow. Delivered live online by senior practitioners, with hands-on labs and a final assessment. Includes an Skilvi course-completion certificate.
Overview
Deep Learning With Tensorflow is an intermediate course designed to equip participants with essential skills in building and deploying deep learning models using Tensorflow. This course emphasizes practical applications and hands-on experience, making it vital for those looking to advance their careers in AI and machine learning.
What you'll learn
- You will be able to design and implement neural networks using Tensorflow.
- You will be able to apply deep learning techniques to solve real-world problems.
- You will be able to optimize model performance through hyperparameter tuning.
- You will be able to work with different types of neural networks, including CNNs and RNNs.
- You will be able to preprocess and augment data for training deep learning models.
- You will be able to deploy Tensorflow models in production environments.
Curriculum
7 modules · outline is indicative and can be tailored to your team.
1Introduction to Deep Learning
- Understanding the basics of deep learning
- Key concepts and terminology
- Overview of neural networks architecture
2Tensorflow Fundamentals
- Installation and setup of Tensorflow
- Understanding tensors and operations
- Building simple models with Keras API
3Building Neural Networks
- Creating feedforward neural networks
- Implementing activation functions
- Loss functions and optimization techniques
4Convolutional Neural Networks
- Understanding convolutional layers
- Building CNNs for image classification
- Data augmentation techniques
5Recurrent Neural Networks
- Exploring RNN architecture
- Implementing LSTM and GRU layers
- Applications of RNNs in sequence prediction
6Model Evaluation and Tuning
- Techniques for model evaluation
- Hyperparameter tuning strategies
- Using Tensorboard for visualization
7Deployment of Deep Learning Models
- Exporting models with Tensorflow
- Serving models using Tensorflow Serving
- Best practices for model deployment
Prerequisites
Basic understanding of machine learning concepts is recommended.
Who should attend
This course is ideal for data scientists, machine learning engineers, and software developers looking to enhance their deep learning skills.
Certification
Frequently asked questions
What is the format of the course?
The course is delivered as live-online instructor-led sessions.
How long is the course?
The course spans over 8 weeks with weekly sessions.
Will I receive a certificate upon completion?
Yes, participants will receive an Skilvi course-completion certificate.
Are exam vouchers provided?
Exam vouchers are available through authorized channels on request.
What are the prerequisites for this course?
Basic understanding of machine learning concepts is recommended.
Pricing on request
- Live online (VILT)
- 24–32 hours
- Hands-on labs & assignments
- Skilvi completion certificate
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