TensorFlow Training in Johannesburg

This instructor-led, live training in Johannesburg (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:

• Construct and train convolutional neural networks (CNNs) using TensorFlow.
• Utilise 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.
• Apply transfer learning to enhance the performance of CNN models.
• Visualise and interpret the results of image classification models.

This instructor-led, live training in Johannesburg (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.

This instructor-led live training in Johannesburg (available online or onsite) is targeted at data scientists who wish to employ 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 analyzing fraud data.

This instructor-led, live training in Johannesburg (online or on-site) is aimed at developers and data scientists who wish to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and other solutions.

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 provides a conceptual foundation in neural networks, machine learning algorithms, and deep learning (including algorithms and applications).

Part 1 (40%) of this training focuses primarily on fundamentals but will assist you in selecting the appropriate technology, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part 2 (20%) of this training introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part 3 (40%) of the training is heavily centred on TensorFlow, the open-source software library for Deep Learning developed by Google. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers looking to utilise TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow’s structure and deployment mechanisms
• be capable of performing installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production-level tasks such as model training, graph construction, and logging