Deep Learning Training in Ontario

Deep Learning Training in Ontario, Canada

Dive into neural networks to build models for image, speech, and text processing.

Enroll Now

Deep Learning Training is a professional training program delivered by ProgNXT in Ontario, Canada. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Deep Learning Training course with hands-on, instructor-led sessions. Deep Learning training provides participants with an in-depth understanding of neural networks and their advanced architectures, such as...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: PRO04
14 Hrs
Last Updated: April 2026

Course Rating5/5

Deep Learning training provides participants with an in-depth understanding of neural networks and their advanced architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. The course covers essential topics like activation functions, backpropagation, optimization techniques, and deep learning frameworks like TensorFlow and PyTorch. Participants learn to build, train, and fine-tune models for tasks such as image recognition, natural language processing, and time series forecasting. Hands-on exercises focus on preparing datasets, designing network architectures, and deploying deep learning models in real-world scenarios. By the end of the training, attendees will be equipped to harness the power of deep learning for cutting-edge applications across industries.

Basic understanding of Python programming
Familiarity with basic mathematical concepts such as linear algebra and probability
Basic knowledge of machine learning algorithms

Upon completing the Deep Learning for Beginners course, participants will:

Understand the fundamentals of deep learning and neural networks.
Gain practical experience in implementing deep learning models.
Learn how to use popular deep learning frameworks like TensorFlow and Keras.
Develop and train simple neural networks for image and text classification tasks.

Deep Learning Fundamentals: Understanding key concepts such as neural networks, activation functions, and backpropagation.
Neural Network Architecture: Ability to design, train, and evaluate basic neural networks.
Framework Proficiency: Hands-on experience with TensorFlow and Keras for deep learning model development.
Model Optimization: Skills to improve model performance using techniques like regularization, dropout, and fine-tuning.

Neural Network Architecture: Knowledge of the basic components of a neural network.
Deep Learning Techniques: Ability to train and evaluate a neural network for specific tasks.
Framework Usage: Proficiency in using TensorFlow/Keras for deep learning projects.
Model Optimization: Skills in optimizing models to improve accuracy and performance.

Certificate of Completion: Participants who attend all sessions and successfully complete the course assessments will receive a ProgNXT Certificate of Completion for the Deep Learning for Beginners Training course.

Introduction to Deep Learning and Neural Networks

Introduction to Deep Learning

Overview of deep learning and its applications
How deep learning differs from traditional machine learning
Real-world applications: image classification, NLP, etc.

Neural Networks and Perceptrons

Structure of a neural network
Perceptron model and its limitations
Activation functions: ReLU, Sigmoid, and Tanh

Training Neural Networks

Forward propagation and backpropagation
Loss functions and optimization techniques (Gradient Descent)
Practical exercise

Using TensorFlow and Keras

Introduction to TensorFlow and Keras libraries
Setting up the environment for deep learning projects
Practical exercise

Advanced Topics in Deep Learning

Improving Neural Network Performance

Overfitting and underfitting in neural networks
Techniques to improve model accuracy: Regularization, dropout, and data augmentation
Practical exercise

Convolutional Neural Networks (CNNs)

Introduction to CNNs and their architecture
Applications in image classification and computer vision
Practical exercise

Deep Learning for Text

Introduction to Recurrent Neural Networks (RNNs)
Applications in natural language processing
Practical exercise

Hands-On Exercises and Evaluation

Hands-on Exercises: Build and train a neural network model on a real dataset
Evaluating model performance: metrics like accuracy, precision, and recall

Detailed course slides and lecture notes
Python code examples and datasets for hands-on exercises
Access to TensorFlow and Keras documentation and resources

Participants will be evaluated through practical exercises and a final project.
ProgNXT Certificates of completion will be awarded to participants who successfully complete the course assessments.

Deep Learning Training Events in ontario

Online Events

Global Region	Location	Start Date	End Date	Action
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌

More Upcoming Session Dates

November 2026

📍 Ontario, Canada

22nd Nov 2026→23rd Nov 2026

🌐 Online Instructor-Led

24th Nov 2026→25th Nov 2026

December 2026

📍 Ontario, Canada

1st Dec 2026→2nd Dec 2026

🌐 Online Instructor-Led

10th Dec 2026→11th Dec 2026

🌐 Online Instructor-Led

18th Dec 2026→19th Dec 2026

📍 Ontario, Canada

27th Dec 2026→28th Dec 2026

January 2027

🌐 Online Instructor-Led

5th Jan 2027→6th Jan 2027

📍 Ontario, Canada

8th Jan 2027→9th Jan 2027

📍 Ontario, Canada

19th Jan 2027→20th Jan 2027

🌐 Online Instructor-Led

21st Jan 2027→22nd Jan 2027

Events for other courses in Ontario, Canada

Global Region	Course Name	Start Date	End Date	Action
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌
‌	‌	‌	‌	‌

Deep Learning Training in Ontario