Neural Networks and Deep Learning

Course Dates :

28/07/25

5

Course ID:

250728001001313ESH

Course Duration :

5 Studying Day/s

Course Location:

Course Category:

Professional and CPD Training Programs

Subcategories: Construction Safety, Health and Wellbeing, Environmental Sustainability, Risk Management, Technical Skills Development, Leadership and Communication, Quality Assurance

Course Certified By:

* ESHub CPD
&
* LondonUni - Executive Management Training

* Professional Training and CPD Programs

Leading to:
Executive Diploma Certificate
Leading to:
Executive Mini Masters Certificate
Leading to
Executive Masters Certificate

Certification Will Be Issued From : From London, United Kingdom

Course Information

Introduction

Artificial intelligence (AI) has emerged as a transformative force across industries, reshaping how organizations approach problem-solving, innovation, and efficiency. At the heart of this revolution lies neural networks and deep learning—technologies that mimic the human brain’s ability to process information and learn from data. These methodologies have enabled breakthroughs in image recognition, natural language processing, autonomous systems, and predictive analytics, among others. As industries increasingly rely on AI-driven solutions, understanding the intricacies of neural networks and deep learning becomes indispensable for professionals seeking to remain competitive.

Despite their potential, neural networks and deep learning present significant challenges. Many practitioners struggle with the mathematical foundations required to design effective models or lack the practical experience to deploy them in real-world scenarios. Additionally, ethical concerns surrounding bias, transparency, and accountability in AI systems demand a nuanced understanding of both theory and application. This course addresses these gaps by providing a comprehensive framework for mastering neural networks and deep learning, equipping participants with the tools needed to navigate this complex yet rewarding field.

The relevance of this subject extends beyond technical expertise; it offers strategic advantages for organizations aiming to innovate and optimize operations. For instance, healthcare providers leveraging deep learning algorithms have achieved remarkable accuracy in diagnosing diseases from medical images. Similarly, financial institutions use neural networks to detect fraudulent transactions in real time, saving millions annually. Such examples underscore the importance of integrating these technologies into business processes, making them not just optional but essential for sustained growth.

From an individual perspective, mastering neural networks and deep learning opens doors to high-demand roles such as machine learning engineer, data scientist, and AI researcher. According to industry reports, the global AI market is projected to grow exponentially over the next decade, creating unprecedented opportunities for skilled professionals. By gaining proficiency in this domain, participants can position themselves at the forefront of technological advancement while contributing meaningfully to their organizations’ success.

This course draws upon established theories and frameworks, including supervised and unsupervised learning paradigms, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Participants will explore cutting-edge research and best practices, ensuring they stay abreast of evolving trends. Moreover, hands-on exercises and case studies will bridge the gap between theory and practice, enabling learners to apply their knowledge effectively.

Ultimately, this program seeks to empower participants to harness the full potential of neural networks and deep learning. Whether developing intelligent chatbots, automating repetitive tasks, or enhancing decision-making through predictive modeling, the skills acquired in this course will serve as a catalyst for personal and professional growth. By fostering a deeper understanding of AI’s capabilities and limitations, we aim to cultivate responsible innovators who can drive positive change in their respective fields.

Objectives

By attending this course, participants will be able to:

Analyze the foundational principles of artificial neural networks, including activation functions, backpropagation, and gradient descent, to build robust models.
Design and implement convolutional neural networks (CNNs) for image classification and object detection tasks using industry-standard tools like TensorFlow and PyTorch.
Evaluate the performance of deep learning models using metrics such as precision, recall, F1-score, and ROC curves to ensure optimal results.
Apply advanced techniques, including transfer learning and hyperparameter tuning, to improve model efficiency and generalization across diverse datasets.
Implement ethical guidelines and interpretability methods to address challenges related to bias, fairness, and transparency in AI systems.

Who Should Attend?

This course is ideal for:

Data scientists, machine learning engineers, and software developers seeking to expand their expertise in AI and deep learning.
Business analysts and consultants interested in leveraging neural networks to derive actionable insights from large datasets.
Researchers and academics exploring innovative applications of deep learning in fields such as medicine, finance, and robotics.
IT managers and CTOs responsible for implementing AI strategies within their organizations.


While prior exposure to programming languages like Python and basic statistics is beneficial, this course caters to intermediate learners looking to deepen their understanding of neural networks and deep learning. Beginners with a strong interest in AI may also enroll, provided they are willing to invest additional effort in grasping foundational concepts.

Training Method

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a 7” Tablet containing a copy of the presentation, slides and handouts
• Post-assessment

Program Support

This program is supported by:
* Interactive discussions
* Role-play
* Case studies and highlight the techniques available to the participants.

Daily Agenda

The course agenda will be as follows:
• Technical Session 08.30-10.00 am
• Coffee Break 10.00-10.15 am
• Technical Session 10.15-12.15 noon
• Coffee Break 12.15-12.45 pm
• Technical Session 12.45-02.30 pm
• Course Ends 02.30 pm