Deep Learning With Python
Instructor-led training in Deep Learning With Python. Delivered live online by senior practitioners, with hands-on labs and a final assessment. Includes an Skilvi course-completion certificate.
Overview
Deep Learning With Python is an intermediate course designed to equip you with the skills to build and deploy deep learning models using Python libraries. Understanding deep learning is essential in today's AI-driven landscape, as it powers advancements in image recognition, natural language processing, and more.
What you'll learn
- You will be able to implement neural networks using TensorFlow and Keras.
- You will be able to preprocess and augment data for deep learning applications.
- You will be able to evaluate and optimize model performance.
- You will be able to apply convolutional neural networks for image classification tasks.
- You will be able to utilize recurrent neural networks for sequence prediction.
- You will be able to integrate deep learning models into real-world applications.
Curriculum
7 modules · outline is indicative and can be tailored to your team.
1Introduction to Deep Learning
- Overview of deep learning concepts
- Difference between machine learning and deep learning
- Applications of deep learning in various fields
2Setting Up Your Environment
- Installing Python and necessary libraries
- Overview of Jupyter Notebooks
- Setting up TensorFlow and Keras
3Building Neural Networks
- Understanding the architecture of neural networks
- Creating a simple feedforward neural network
- Training and validating your model
4Convolutional Neural Networks (CNNs)
- Introduction to CNNs and their applications
- Building a CNN for image classification
- Techniques for improving CNN performance
5Recurrent Neural Networks (RNNs)
- Understanding RNNs and LSTMs
- Building a model for sequence prediction
- Applications of RNNs in natural language processing
6Model Evaluation and Optimization
- Metrics for evaluating model performance
- Hyperparameter tuning techniques
- Strategies for preventing overfitting
7Deploying Deep Learning Models
- Overview of model deployment strategies
- Using Flask for deploying models as web services
- Best practices for production-ready models
Prerequisites
Basic knowledge of Python programming is recommended.
Who should attend
This course is ideal for data scientists, machine learning engineers, and developers looking to deepen their understanding of deep learning.
Certification
Frequently asked questions
What is the delivery format of the course?
The course is instructor-led and delivered live online.
How long is the course?
The course typically spans over 5 weeks with weekly sessions.
Will I receive a certificate upon completion?
Yes, participants will receive an Skilvi course-completion certificate.
Are exam vouchers included?
Exam vouchers are available through authorized channels on request.
Do I need prior experience in deep learning?
No prior experience required, but familiarity with Python is beneficial.
Pricing on request
- Live online (VILT)
- 24–32 hours
- Hands-on labs & assignments
- Skilvi completion certificate
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