Deep Learning With TensorFlow Training - Canada

Deep Learning with TensorFlow Course Outline

Module 1: Introduction to TensorFlow and Deep Learning

• Introduction to Deep Learning and TensorFlow
• Understanding Tensors
• Installation and Setup of TensorFlow
• Computation Phases in TensorFlow
• Variables and Operations
• Computational Graphs with TensorBoard
• Implementing Linear Regression in TensorFlow

Module 2: Artificial Neural Networks and Perceptrons

• Introduction to Artificial Neural Networks
• Characteristics of Artificial Neural Networks
• Perceptron Model
• Single-Layer Perceptron
• Multi-Layer Perceptron
• Role of Weights and Biases in Neural Networks

Module 3: Activation Functions and Gradient Computation

• Introduction to Activation Functions
• Types of Activation Functions
• Unit Step Function
• Sigmoid Function
• ReLU Function
• Piecewise Linear Function
• Gaussian Function
• Linear Function
• Gradient Computation in Neural Networks
• Understanding Backpropagation
• Steps for Computing Gradients

Module 4: Deep Learning Architectures and Techniques

• Introduction to Deep Learning Architectures
• Convolutional Neural Networks
• Filters and Feature Maps in CNN
• Pooling Layers
• Implementing CNN in TensorFlow
• Recurrent Neural Networks
• Long Short-Term Memory Networks
• Implementing RNN in TensorFlow

Module 5: Model Optimisation and Training Techniques

• Optimisers in Deep Learning
• Understanding Gradient Descent
• Adam Optimiser
• Learning Rate Scheduling
• Overfitting and Regularisation in Neural Networks
• Dropout and Batch Normalisation
• L1 and L2 Regularisation
• Hyperparameter Tuning
• Performance Metrics and Model Evaluation

Module 6: Applications and Case Studies in Deep Learning

• Applications of Deep Learning in Computer Vision
• Natural Language Processing
• Speech Recognition
• Recommendation Systems
• Real-World Case Studies
• Hands-on Project in TensorFlow
• Future Trends in Deep Learning

Who should attend this Deep Learning with TensorFlow Training Course?

The Deep Learning with TensorFlow Course is a specialised course focused on advanced Machine Learning techniques using TensorFlow, one of the most widely used open-source libraries for numerical computation and Machine Learning. The following professionals will benefit greatly from this course:

• Machine Learning Engineers
• Data Scientists
• Artificial Intelligence (AI) Researchers
• Software Developers
• Natural Language Processing Engineers
• Automotive Engineers
• Robotics Engineers

Prerequisites of the Deep Learning with TensorFlow Training Course

Delegates should have a basic understanding of Python Programming and Machine Learning.

Deep Learning with TensorFlow Course Overview

Deep Learning with TensorFlow is a comprehensive course designed to teach professionals how to build and deploy deep learning models using the TensorFlow framework. This course is essential for those looking to harness the power of neural networks to solve complex problems and drive innovation in fields like healthcare, finance, and technology.

Proficiency in TensorFlow and deep learning is crucial for Data Scientists, AI Engineers, Software Developers, and Researchers. Mastering this field empowers professionals to develop cutting-edge solutions in image and speech recognition, natural language processing, autonomous systems, and predictive analytics. It is vital for anyone aiming to stay competitive and lead technological advancements in their industry.

This intensive 1-day course equips delegates with a thorough understanding of deep learning concepts and practical skills using TensorFlow. Through hands-on workshops and expert-led sessions, delegates gain insights into neural network architectures, including convolutional and recurrent neural networks, and their applications. Delegates learn to implement deep learning models, interpret their results, and optimise them for various real-world scenarios.

Course Objectives

• To understand the foundational principles of neural networks
• To explore TensorFlow and its capabilities in building deep learning models
• To apply deep learning algorithms in image and speech recognition tasks
• To analyse and interpret deep learning model results effectively
• To optimise and fine-tune neural networks for improved performance
• To comprehend ethical considerations in deploying deep learning solutions

Upon completing this course, delegates will have acquired the knowledge and skills necessary to implement and optimise deep learning models using TensorFlow, making them invaluable assets in their professional fields.

What’s included in this Deep Learning with TensorFlow Training Course?

• World-Class Training Sessions from Experienced Instructors
• Deep Learning with TensorFlow Certificate
• Digital Delegate Pack

You’ll also get access to the MyTKA Training Portal, which will be your go to hub for all your training.
Hands-On Labs: Included as part of our online instructor-led delivery, these labs provide real-world exercises in a simulated environment guided by expert instructors to enhance your practical skills.