Online TensorFlow Training in Nigeria

This instructor-led live training in Nigeria (online or onsite) is designed for advanced professionals seeking to expand their computer vision knowledge and explore TensorFlow’s capabilities for developing advanced 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.
• Leverage Google Colab for scalable, 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 Nigeria (online or onsite) is tailored for 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 Nigeria (online or onsite) is tailored for data scientists who want to utilise TensorFlow for analysing potential fraud data.

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

• Create a fraud detection model using 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 Nigeria (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 solid conceptual foundation in neural networks, as well as an overview of machine learning algorithms and deep learning (covering both algorithms and their practical applications).

The first part (40%) of this training concentrates on fundamentals, yet it will equip you to select the appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

The second part (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

The third part (40%) of the training is heavily focused on TensorFlow, the open-source software library for Deep Learning developed by Google. All examples and hands-on exercises will be conducted within the TensorFlow environment.

Audience

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

Upon completion of this course, delegates will:

• possess a strong understanding of Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN)
• understand TensorFlow’s structure and deployment mechanisms
• be capable of performing installation, setting up production environments, designing architectures, and configuring systems
• be able to assess code quality, perform debugging, and implement monitoring
• be able to implement advanced production tasks such as training models, building computational graphs, and handling logging