Advanced Deep Learning and Neural Network Architectures Course for AI Engineers

Module 1: Foundations of Deep Learning and Perceptrons

ü  Biological inspiration and the Artificial Neuron

ü  Activation functions: Sigmoid, Tanh, ReLU, and Leaky ReLU

ü  The architecture of a Multi-Layer Perceptron (MLP)

ü  Linear Algebra and Calculus essentials for Neural Networks

ü  Practical Session: Building a basic 3-layer neural network from scratch using NumPy.

Module 2: Backpropagation and Optimization Algorithms

ü  Cost functions and Loss functions: Cross-entropy vs. MSE

ü  The chain rule and gradient computation

ü  Stochastic Gradient Descent (SGD), Momentum, and RMSprop

ü  The Adam Optimizer: Why it’s the industry standard

ü  Practical Session: Implementing backpropagation and comparing optimizer convergence speeds.

Module 3: Convolutional Neural Networks (CNNs) for Computer Vision

ü  Convolutional layers, filters, and feature maps

ü  Pooling layers (Max vs. Average) and Stride

ü  Popular architectures: ResNet, VGG, and Inception

ü  Transfer Learning and Fine-tuning pre-trained models

ü  Practical Session: Building an image classifier to detect anomalies in industrial components.

Module 4: Sequence Modeling: RNNs, GRUs, and LSTMs

ü  The vanishing gradient problem in vanilla RNNs

ü  Long Short-Term Memory (LSTM) cells and Gated Recurrent Units (GRU)

ü  Many-to-one and Many-to-many architectures

ü  Bidirectional RNNs for context awareness

ü  Practical Session: Developing a time-series forecasting model for financial market data.

Module 5: Regularization and Improving Deep Neural Networks

ü  Overfitting vs. Underfitting: The Bias-Variance tradeoff

ü  L1/L2 Regularization and Weight Decay

ü  Dropout layers and Batch Normalization

ü  Data Augmentation strategies for small datasets

ü  Practical Session: Applying dropout and batch normalization to stabilize a deep network.

Module 6: Generative Adversarial Networks (GANs) and Autoencoders

ü  Generator vs. Discriminator dynamics

ü  Training stability challenges in GANs

ü  Variational Autoencoders (VAEs) for data compression

ü  Applications in image synthesis and style transfer

ü  Practical Session: Training a GAN to generate synthetic training data for rare edge cases.

Module 7: Attention Mechanisms and Transformer Architectures

ü  The "Attention is All You Need" paradigm

ü  Self-attention and Multi-head attention mechanisms

ü  Positional Encoding and the Encoder-Decoder structure

ü  Introduction to BERT, GPT, and Large Language Models (LLMs)

ü  Practical Session: Implementing a self-attention layer and exploring Transformer-based text classification.

Module 8: Deep Reinforcement Learning Fundamentals

ü  Markov Decision Processes (MDP)

ü  Q-Learning and Deep Q-Networks (DQN)

ü  Policy Gradient methods

ü  Reward shaping and exploitation vs. exploration

ü  Practical Session: Training a reinforcement learning agent to solve a simulated navigation task.

Module 9: Model Debugging, Tuning, and Performance Analysis

ü  Hyperparameter search: Grid, Random, and Bayesian optimization

ü  Using TensorBoard for visualization and bottleneck identification

ü  Sensitivity analysis and feature importance in DL

ü  Weight initialization techniques (Xavier/He initialization)

ü  Practical Session: Systematic hyperparameter tuning of a deep vision model to improve F1-score.

Module 10: Deep Learning at Scale and Production Deployment

ü  Distributed training across multiple GPUs

ü  Model quantization and pruning for mobile/edge deployment

ü  Serving models via Flask/FastAPI and Docker

ü  Monitoring for model drift in production

ü  Practical Session: Deploying a trained Deep Learning model as a scalable REST API using Docker.

About Our Trainers

Our trainers are elite practitioners in the field of Artificial Intelligence, holding PhDs or Master’s degrees in Computer Science and Computational Mathematics. They possess extensive industry experience, having led AI engineering teams at global tech hubs. Our lead instructors are active contributors to open-source DL libraries and have successfully deployed large-scale neural network solutions in sectors ranging from FinTech to Autonomous Systems.

Quality Statement

Phoenix Center for Policy, Research and Training is dedicated to providing high-fidelity technical education. We maintain a rigorous curriculum that is updated every six months to keep pace with the breathtaking speed of AI evolution. Our focus is on "Engineering Excellence," ensuring that every participant leaves not just with knowledge, but with the practical ability to build production-grade AI systems.

Tailor-Made Courses

We understand that every organization has unique challenges and opportunities as well as unique training needs. Phoenix Training Center offers tailor-made courses designed to address specific requirements and challenges faced by your team or organization. Whether you need a customized curriculum, a specific duration, or on-site delivery, we can adapt our expertise to provide a training solution that perfectly aligns with your objectives.We can customize this Course to focus on your industry, specific risk profile, or internal stakeholder dynamics. Contact us to discuss how we can create a bespoke training program that maximizes value and impact for your team. For further inquiries, please contact us on Tel: +254720272325 / +254737296202 or Email training@phoenixtrainingcenter.com

Admission Criteria

ü  Participants should be reasonably proficient in English. 

ü  Applicants must live up to Phoenix Center for Policy, Research and Training admission criteria.

Terms and Conditions

ü  Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

ü  What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

ü  Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

ü  The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

ü  Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@phoenixtrainingcenter.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training. Or call us on +254720272325 / +254737296202

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

ü  Groups of 5 People and Above – Cheque Payments to: Phoenix Center for Policy, Research and Training Limited should be paid in advance, 5 days to the training.

ü  Invoice: We can send a bill directly to you or your company.

ü  Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

ü  Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

ü  Participants may cancel attendance 14 days or more prior to the training commencement date.

ü  No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Accommodation and Airport Transfer

For physical training attendees, we can assist with recommendations for accommodation near the training venue. Airport pick-up services can also be arranged upon request to ensure a smooth arrival. Please inform us of your travel details in advance if you require these services. For reservations contact the Training Officer on Email: training@phoenixtrainingcenter.com or on Tel: +254720272325 / +254737296202