TensorFlow Training in Bhutan

This instructor-led, live training in Bhutan (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end of this training, participants will be able to:

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led live training, conducted in Bhutan (either online or onsite), targets intermediate-level data scientists and developers who aim 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.

This instructor-led live training in Bhutan (online or onsite) is designed for data scientists who wish to use TensorFlow to analyse potential fraud data.

By the end of this training, participants will be able to:

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analysing fraud data.

This instructor-led, live training in Bhutan (online or on-site) is designed for developers and data scientists who intend to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and related applications.

By the end of this training, participants will be able to:

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This course starts by providing conceptual knowledge of neural networks, machine learning algorithms, and deep learning (including algorithms and applications).

Part 1 (40%) of this training focuses more on fundamentals, but will help you choose the right technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part 2 (20%) of this training introduces Theano, a Python library that makes writing deep learning models easy.

Part 3 (40%) of the training would be extensively based on TensorFlow - the API of Google's open-source software library for Deep Learning. The examples and hands-on sessions will all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• have a good understanding of deep neural networks (DNN), CNN and RNN
• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production-like training models, building graphs and logging