Deep Learning with TensorFlow 2 Training Course

This instructor-led, live training delivered Kenya (online or onsite) is designed for advanced-level professionals aiming to deepen their grasp of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the conclusion 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 Kenya (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.

Audience

This course is tailored for deep learning researchers and engineers who aim to leverage accessible tools, predominantly open-source solutions, for computer image analysis

Participants will gain exposure to practical, working examples.

This instructor-led, live training in Kenya (online or onsite) is tailored for data scientists who aim to use TensorFlow to analyze 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 course provides foundational knowledge on neural networks, machine learning algorithms, and deep learning (including algorithms and their applications).

Part-1 (40%) of this training focuses heavily on fundamentals while helping you select the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

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 heavily centered on Tensorflow - the API for Google's open-source software library for Deep Learning. The examples and hands-on exercises will all be conducted in TensorFlow.

Audience

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

Upon completing this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNN, and RNN
• comprehend TensorFlow's structure and deployment mechanisms
• be capable of executing installation / production environment / architecture tasks and configuration
• be able to evaluate code quality, perform debugging, and monitoring
• be able to implement advanced production-like tasks such as training models, building graphs, and logging