Deep Learning with TensorFlow in Google Colab Training Course
Google Colab is a cloud-based Jupyter notebook environment that allows you to run Python code for free and is particularly well-suited for machine learning and deep learning tasks using libraries like TensorFlow.
This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.
By the end of this training, participants will be able to:
- Set up and navigate Google Colab for deep learning projects.
- Understand the fundamentals of neural networks.
- Implement deep learning models using TensorFlow.
- Train and evaluate deep learning models.
- Utilize advanced features of TensorFlow for deep learning.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Google Colab for Deep Learning
- Overview of Google Colab
- Setting up Google Colab
- Navigating the Google Colab interface
Introduction to Deep Learning
- Overview of deep learning
- Importance of deep learning
- Applications of deep learning
Understanding Neural Networks
- Introduction to neural networks
- Architecture of neural networks
- Activation functions and layers
Getting Started with TensorFlow
- Overview of TensorFlow
- Setting up TensorFlow in Google Colab
- Basic TensorFlow operations
Building Deep Learning Models with TensorFlow
- Creating neural network models
- Training neural networks
- Evaluating model performance
Advanced TensorFlow Techniques
- Implementing convolutional neural networks (CNNs)
- Implementing recurrent neural networks (RNNs)
- Transfer learning with TensorFlow
Data Preprocessing for Deep Learning
- Preparing datasets for training
- Data augmentation techniques
- Handling large datasets in Google Colab
Optimizing Deep Learning Models
- Hyperparameter tuning
- Regularization techniques
- Model optimization strategies
Collaborative Deep Learning Projects
- Sharing and collaborating on notebooks
- Real-time collaboration features
- Best practices for collaborative projects
Tips and Best Practices
- Effective deep learning techniques
- Avoiding common pitfalls
- Enhancing model performance
Summary and Next Steps
Requirements
- Basic knowledge of machine learning
- Experience with Python programming
Audience
- Data scientists
- Software developers
Open Training Courses require 5+ participants.
Deep Learning with TensorFlow in Google Colab Training Course - Booking
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Testimonials (5)
very friendly and helpful
Aktar Hossain - Unit4
Course - Building Microservices with Microsoft Azure Service Fabric (ASF)
Amount of Information, Exercises
Lukasz Kowalski - Sii Sp. z o.o.
Course - AWS IoT Core
Machine Translated
The manual serverless setup. Also, I had no Idea sls web console exits, which is nice.
Rafal Kucharski - The Software House sp. z o.o.
Course - Serverless Framework for Developers
IOT applications
Palaniswamy Suresh Kumar - Makers' Academy
Course - Industrial Training IoT (Internet of Things) with Raspberry PI and AWS IoT Core 「4 Hours Remote」
Later the balance between theory and practice was much better. But the beginnings were terrible. The way of expressing (language) is very calm, understandable, in a human way.
Lukasz Derkowski - NetworkedAssets Sp. z o.o.
Course - AWS CloudFormation
Machine Translated
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