Online TensorFlow Training in Botswana

This instructor-led, live training in Botswana (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 in Botswana (online or onsite) targets intermediate data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

Upon completing this training, participants will be able to:

• Configure and navigate Google Colab for deep learning projects.
• Grasp the fundamental concepts of neural networks.
• Develop deep learning models using TensorFlow.
• Train and assess deep learning models.
• Leverage advanced TensorFlow features for deep learning.

Audience

This course is ideal for Deep Learning researchers and engineers who wish to utilise available tools (predominantly open-source) for the analysis of computer images.

The course provides practical working examples.

This instructor-led, live training in Botswana (online or on-site) is aimed at 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 analyzing fraud data.

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

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 you with a conceptual understanding of neural networks and machine learning algorithms in general, with a specific focus on deep learning (algorithms and applications).

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

Part-2 (20%) of this training introduces Theano, a Python library that simplifies the creation of deep learning models.

Part-3 (40%) of the training is extensively based on TensorFlow, the API of Google's open-source software library for Deep Learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers who wish to apply TensorFlow to their Deep Learning projects.

Upon completing this course, delegates will:

• possess a strong understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow's structure and deployment mechanisms
• be capable of carrying out installation, production environment, and architecture tasks as well as configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production tasks such as training models, building graphs, and logging