Understand how deep learning actually works then build it yourself.
Deep Learning Fundamentals Training
Develop the neural network architecture, backpropagation, and model training skills that serious deep learning work genuinely requires.
- UK Licensed
- Global Standards
- Expert Instructor
- 5 Day Training
- Overview
Course Overview
Deep learning powers the systems that recognize faces, translate languages, detect fraud, and generate content. Most professionals who use or oversee these systems have a working understanding of what they do. Far fewer understand how they actually work how neural networks learn from data, what backpropagation is doing mathematically, or why a model that performs brilliantly on training data fails completely on real-world inputs.
This deep learning course covers the foundational mechanics of how deep neural networks function architecture design, backpropagation training, gradient descent optimization, and transfer learning alongside practical model implementation using PyTorch and TensorFlow. This course prepares participants for NVIDIA’s Fundamentals of Deep Learning certification exam, building the conceptual depth and implementation skills that the examination tests across real neural network training scenarios.
It sits within the Global Industries Intelligence training portfolio alongside data science certificate and other tech certifications bringing the same practically focused, GPU-powered approach to deep learning with python that we apply across our data science and AI programs.
- Goals
Course Objectives
By the end of this program, attendees will be able to:
- Explain how deep neural networks learn from data the forward pass, loss calculation, backpropagation, and gradient descent training cycle that underlies every deep learning model
- Design and implement deep learning models using deep learning in tensorflow and pytorch training loop workflows across realistic supervised learning scenarios
- Apply transfer learning to adapt pre-trained deep learning models to new tasks without training from scratch
- Diagnose and resolve common training problems including overfitting, vanishing gradients, and slow convergence using practical optimization techniques
- Implement deep learning with python using GPU-accelerated training environments for performance that CPU-based training cannot match at scale
- Prepare confidently for NVIDIA's Fundamentals of Deep Learning certification examination
- Key Points
Who Should Attend?
- Data Scientists Adding Deep Learning to Their Skill Set
- Machine Learning Engineers Building Neural Network Models
- Software Developers Moving Into AI and Deep Learning Roles
- Researchers Applying Deep Learning Across Scientific Domains
- Technical Managers Evaluating Deep Learning Capability Investment
- Key Benefits
Key Benefits of the Course
- A deep learning course grounded in real neural network training and optimization scenarios
- Practical pytorch training loop and deep learning in tensorflow implementation skills applicable from the next model building assignment
- Stronger backpropagation training and gradient descent understanding that explains why models behave the way they do
- Clear transfer learning methodology for adapting pre-trained models without the data and compute requirements of training from scratch
- A recognised credential from our training portfolio supporting progression toward senior deep learning and AI engineering roles
- Outline
Course Outline
Day 1: Neural Network Architecture and How Deep Learning Works
- Covers the fundamentals of deep learning in plain terms first what a neural network is, how it's organized into layers, why depth matters, and what makes deep learning different from traditional machine learning before introducing the mathematical mechanics that underpin it all
- Introduces forward pass computation, activation functions, loss functions, and the training cycle that connects a network's predictions to the optimization process that improves them building the conceptual foundation that deep learning techniques make no sense without
- Real deep learning model examples show where practitioners who skipped foundational understanding consistently struggled to diagnose training problems that were immediately obvious once the underlying mechanics were properly understood
Day 2: Backpropagation and Gradient Descent Training
- Covers backpropagation training in depth how gradients flow backward through a network, what the chain rule has to do with it, and how gradient descent training uses those gradients to update weights in the direction that reduces prediction error
- Introduces gradient descent variants including stochastic gradient descent, mini-batch gradient descent, and adaptive optimizers what each one does differently and when each one produces better training outcomes across different deep learning network architectures and dataset sizes
- Attendees implement a backpropagation training exercise from scratch before moving to framework-level implementation, developing the mechanical understanding that makes debugging neural network training problems significantly faster than treating the training process as a black box
Day 3: Deep Learning With Python PyTorch and TensorFlow Implementation
- Covers pytorch training loop implementation in depth dataset loading, model definition, loss function selection, optimizer configuration, and the training loop structure that forms the backbone of every PyTorch-based deep learning projec
- Introduces deep learning in tensorflow the Keras API, model compilation, training callbacks, and the implementation workflow that gets a deep learning model from defined architecture to trained weights in a way that's clean, reproducible, and production-ready
- Hands-on exercises develop complete model implementation skills across both frameworks, building the practical deep learning with python competency that real deep learning engineering requires regardless of which framework a production environment uses
Day 4: Transfer Learning and Model Optimization
- Covers transfer learning methodology how pre-trained deep learning models that learned representations from large datasets can be fine-tuned for new tasks with dramatically less data and compute than training from scratch requires
- Introduces model optimization techniques for addressing the training problems that deep learning practitioners encounter most frequently dropout regularization for overfitting, batch normalization for training stability, and learning rate scheduling for convergence control
- Attendees apply transfer learning to a realistic scenario using a pre-trained model, developing the fine-tuning and optimization judgment that separates practitioners who get good results from pre-trained models from those who fine-tune without understanding what the process is actually doing
Day 5: Advanced Training Techniques and NVIDIA Exam Preparation
- Covers pytorch distributed training and pytorch lightning trainer for scaling deep learning model training across multiple GPUs the training infrastructure techniques that matter when single-GPU training becomes the performance bottleneck
- Introduces deep learning model evaluation, deployment considerations, and the systematic debugging methodology that diagnoses training failures, poor generalization, and inference performance problems in real deep learning systems
- Attendees complete a full deep learning project architecture design, training, optimization, and evaluation alongside a timed NVIDIA Fundamentals of Deep Learning practice examination, with direct instructor feedback on model design decisions and exam readiness
- Terms
Training Methodology
This program combines:
- Deep learning course workshops using GPU-powered cloud environments and PyTorch and TensorFlow across realistic neural network training scenarios
- Backpropagation and gradient descent exercises with structured peer review and instructor feedback on implementation quality
- Transfer learning and model optimization sessions built around real deep learning training challenges
- NVIDIA Fundamentals of Deep Learning certification examination preparation aligned to current exam domain objectives
- Achievements
Certification
Attendees receive a certificate of completion issued by GII Corporate & Industrial Training Center upon successful completion of the program. This course prepares participants for NVIDIA’s Fundamentals of Deep Learning certification exam NVIDIA administers and awards that certification separately upon passing their examination. In-house training is available for data science and AI engineering teams building deep learning capability. Contact us now for upcoming dates across our training portfolio.
- Case Study
Case Study Example
A software development firm had several engineers who had integrated pre-trained deep learning models into production applications without formal deep learning training behind them. When model performance degraded on new data distributions, nobody on the team could diagnose whether the problem was architectural, a training data issue, or an optimization failure because nobody understood the training process well enough to distinguish between them. After key engineers completed this Deep Learning Fundamentals Training program, the team diagnosed the degradation as a distribution shift problem that transfer learning fine-tuning addressed directly. The same engineers subsequently built two new deep learning models internally that would previously have required external AI consultancy engagement, reducing development cost and timeline significantly across both projects.
- Why Choose
Why Choose This Program?
Deep learning training that covers implementation without developing mechanical understanding produces engineers who can copy model architectures without knowing why they work or how to fix them when they don’t. This program is built around both the foundational understanding of how deep neural networks learn and the practical PyTorch and TensorFlow implementation skills that production deep learning engineering requires. Explore our data science certificate program and other in-house training options across our training portfolio for a complete view of what Global Industries Intelligence delivers.
- Connect
Contact Us
Ready to build deep learning capability across your data science or engineering team? Contact us now for upcoming dates and in-house training solutions tailored to your organization.
- FAQs
Fequently Asked Questions
What does the Deep Learning Fundamentals Training program cover?
Five days across neural network architecture and deep learning mechanics, backpropagation and <b>gradient descent training</b>, PyTorch and TensorFlow implementation, <b>transfer learning</b> and model optimization, and advanced training techniques with NVIDIA exam preparation with a full deep learning project and practice examination on Day 5.
Does GII issue the NVIDIA Fundamentals of Deep Learning certification?
No. GII issues a certificate of completion from GII Corporate & Industrial Training Center. The NVIDIA Fundamentals of Deep Learning certification is awarded by NVIDIA upon passing their own examination this program prepares participants fully for that examination.
Do attendees need prior machine learning or programming experience?
Python programming proficiency and familiarity with basic machine learning concepts are strongly recommended. The program introduces deep learning fundamentals from conceptual foundations, but participants without Python experience will find the implementation content significantly more challenging.
Does the course use real GPU hardware for training?
Yes. All training exercises use GPU-powered cloud environments participants access GPU instances during lab sessions, reflecting how deep learning models are trained in production environments and ensuring the performance benefits of GPU acceleration are experienced directly during the course.
Which roles benefit most from deep learning fundamentals training?
Data scientists adding neural network capability, software engineers integrating deep learning into applications, and machine learning practitioners who have used pre-trained models without understanding the training process see the most direct value though any technical professional whose work involves building, evaluating, or maintaining deep learning systems has a strong use case for this foundational deep learning course.
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Course Schedule
Select your preferred date & venue
20 Jan -- 24 Jan
South Africa
GBP 4595/Person
14 Apr -- 18 Apr
KSA
GBP 4595/Person
7 Jul -- 11 Jul
UK
GBP 4595/Person
13 Oct -- 17 Oct
Morocco
GBP 4595/Person
20 Jan -- 24 Jan
South Africa
GBP 3595/Person
14 Apr -- 18 Apr
KSA
GBP 3595/Person
7 Jul -- 11 Jul
UK
GBP 3595/Person
13 Oct -- 17 Oct
Morocco
GBP 3595/Person
Duration
5 Days