Deep Learning Specialty Course Overview

Deep Learning Specialty Course Overview

The Deep Learning Specialty course is an extensive program designed to equip learners with the cutting-edge skills required to excel in the field of artificial intelligence. The course offers in-depth training in various aspects of deep learning, from the fundamental principles to advanced applications. Beginning with an Introduction to Deep Learning, learners will explore the latest trends and real-world applications, setting a solid foundation for understanding neural networks.

As participants progress through Neural Network Basics and Shallow Neural Network modules, they will gain hands-on experience in building and optimizing neural networks, learning key concepts like vectorization, forward propagation, and backpropagation. The Deep Neural Network module takes learners deeper into computation and the construction of networks for tasks such as computer vision.

Practical Aspects of Deep Learning and Optimization Algorithms focus on critical techniques like initialization, regularization, and advanced optimization strategies, respectively, to improve model performance. With Hyperparameter Tuning, Batch Normalization, Frameworks, students will delve into TensorFlow and dataset training.

The curriculum also covers strategic insights in ML Strategy, and advances through Convolutional Neural Networks (CNNs), exploring pooling, convolutional layers, and deep CNN case studies for image classification. Object Detection, Face Recognition & Neural Style Transfer modules demonstrate the application of CNNs in specialized tasks, while Recurrent Neural Networks (RNNs), Natural Language Processing & Word Embeddings, and Sequence Models & Attention Mechanism modules address the challenges in processing sequential data like text and audio.

Finally, the Transformer Network module introduces the revolutionary architecture reshaping modern AI. By participating in these deep learning classes and completing the deep learning training, learners will be well-prepared to tackle complex problems and innovate in the field of AI.

Purchase This Course


  • Live Online Training (Duration : 40 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
  • date-img
  • date-img

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

  • Live Online Training (Duration : 40 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

Request More Information

Email:  WhatsApp:

Koenig's Unique Offerings


1-on-1 Training

Schedule personalized sessions based upon your availability.


Customized Training

Tailor your learning experience. Dive deeper in topics of greater interest to you.


4-Hour Sessions

Optimize learning with Koenig's 4-hour sessions, balancing knowledge retention and time constraints.


Free Demo Class

Join our training with confidence. Attend a free demo class to experience our expert trainers and get all your queries answered.

Course Prerequisites

Certainly! Here are the minimum required prerequisites for successfully undertaking the Deep Learning Specialty course:

  • Basic understanding of machine learning concepts and algorithms
  • Fundamental knowledge of statistics and probability
  • Proficiency in at least one programming language (preferably Python)
  • Familiarity with calculus and linear algebra (derivatives, gradients, matrix multiplication)
  • Experience with handling data in appropriate formats (CSV, JSON, etc.)
  • Basic knowledge of computer science principles and data structures
  • An understanding of the software development environment (IDE usage, debugging, version control)
  • Willingness to learn and adapt to new mathematical concepts and programming paradigms

These prerequisites are designed to ensure that students have a solid foundation upon which to build their deep learning knowledge. With these skills, students will be better prepared to grasp the complex concepts presented in the course and apply them effectively in practical scenarios.

Target Audience for Deep Learning Specialty

The Deep Learning Specialty course is designed for professionals seeking advanced AI and machine learning skills.

  • Data Scientists looking to specialize in deep learning
  • Machine Learning Engineers pursuing in-depth knowledge of neural networks
  • AI Researchers focusing on cutting-edge deep learning techniques
  • Software Developers aiming to implement deep learning in their solutions
  • Computer Vision Engineers interested in CNN and object detection
  • NLP Engineers and Researchers working with text and speech recognition
  • Robotics Engineers integrating deep learning for autonomous systems
  • Bioinformatics Researchers using deep learning for genomic data
  • Academics and Students in computer science or AI-related fields
  • IT Professionals transitioning to roles focused on AI and machine learning
  • Technical Leads managing AI projects or teams
  • CTOs and Technology Strategists planning AI implementation in business solutions
  • Product Managers overseeing AI-based product development
  • R&D Professionals in tech companies exploring deep learning applications

Learning Objectives - What you will Learn in this Deep Learning Specialty?

Introduction to the Course's Learning Outcomes

This Deep Learning Specialty course is designed to equip students with advanced knowledge and practical skills in deep learning, from the basics of neural networks to the complexities of modern architectures like CNNs and RNNs.

Learning Objectives and Outcomes

  • Grasp the current trends and real-world applications of deep learning, and understand where and how it's applied.
  • Comprehend the fundamentals of neural networks, including how to set up machine learning problems and implement vectorization.
  • Develop and train a shallow neural network, and understand concepts such as forward and backpropagation.
  • Dive into deep neural networks, learning how to construct and train them for complex tasks like computer vision.
  • Apply practical aspects of deep learning, including network initialization and regularization techniques to combat overfitting.
  • Master advanced optimization algorithms, minibatch processing, and learning rate scheduling for improved network training.
  • Gain proficiency in TensorFlow, and train neural networks using TensorFlow datasets.
  • Formulate effective machine learning strategies, perform error analysis, and manage ML production workflows.
  • Explore the foundations of convolutional neural networks (CNNs) and learn to build deep CNNs for image classification.
  • Understand and apply object detection, face recognition, and neural style transfer techniques using CNNs.
  • Delve into recurrent neural networks (RNNs) and their variants for sequential data modeling.
  • Learn about natural language processing (NLP) and leverage word embeddings for advanced text analysis.
  • Enhance sequence models with attention mechanisms for applications like speech recognition.
  • Comprehend the architecture and functioning of transformer networks for state-of-the-art results in various machine learning tasks.