Deep Learning with TensorFlow in Google Colab

• Basic knowledge of machine learning
• Experience with Python programming

Audience

• Data scientists
• Software developers

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.

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