Advanced Deep Learning Professional (ADLP)


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  • Duration: 3 Day / 24 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Aspiring Artificial Intelligence professionals, Data Analyst, Computer Scientist, Programmers, and Anyone interested in pursuing a career in the areas of Artificial Intelligence and Deep Learning

Course Objective

Acquire the essential knowledge and technical skills on how manage and deploy Artificial Intelligence and Deep Learning in an organization.

Learn the Python Programming and its extensive libraries to develop the core components of Deep Learning, Computer Vision, Neural Network and Natural Language Processing (NLP) technologies


It is recommended that participants have some prior experience in computer science or have successfully completed Certified Machine Learning Expert.


Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Deep Learning and Python Programming based on the syllabus covered

Participants are expected to score a minimum of 70% to pass the examination

Module 1
Introduction to Deep Learning

Topics Covered

  • What is Artificial Intelligence and Machine Learning?
  • Understanding Learning Representation of Data
  • Probabilistic Modeling
  • Kernel Methods
  • Decision trees, Random Forests, and Gradient Boosting Machines
  • Fundamentals of Deep Learning
  • Understanding Neural Networks
  • What Makes Deep Learning Different
  • Hardware, Data, and Algorithms of Deep Learning
  • How Deep Learning Works
  • Deep Learning and Its Application
  • Future of Deep Learning
Module 2
Data Representation in Neural Networks

Topics Covered

  • Scalars, Vectors, Matrices
  • 3D Tensors and higher-dimensional tensors
  • Key Attributes
  • Manipulating Tensors in Numpy
  • The Notion of Data Batch
  • Examples of Data Tensors
  • Vector Data
  • Timeseries Data or Sequence Data
  • Image and Video Data
Module 3
Neural Networks: Tensor Operations and Gradient-Based Operations

Topics Covered

  • Element-wise Operations
  • Broadcasting
  • Tensor Dot
  • Tensor Reshaping
  • Geometric Interpretation of Tensor Operations
  • Geometric Interpretation of Deep Learning
  • Introduction to Derivative
  • Derivative of a Tensor Operation: Gradient
  • Stochastic Gradient Descent
  • The Backpropagation Algorithm
Module 4
Getting Ready for Deep Learning and Structure of Neural Network

Topics Covered

  • Key Considerations
  • Setting up Jupyter Notebooks
  • Setting up of Keras
  • Deep Learning in a Cloud
  • Identifying the Best GPU for Deep Learning
  • What are the Layers for Deep Learning?
  • Neural Network Models
  • Core Elements to Configuring a Learning Process
  • Introduction to Keras, TensorFlow, Theano, and CNTK
  • Brief Overview of Keras
Module 5
Classification and Regression Models in Deep Learning

Topics Covered

  • Data preparation for Binary Classification
  • Building a Binary Classification Network
  • Validating Approach
  • Using a Trained Network to Generate Predictions on New Data
  • Data Preparation for Multi-Class Classification
  • Building Multi-Class Classification Network
  • Validating Approach
  • Generate Predictions on New Data for Regression Model
  • Alternative Ways to Handle Labels and Loss
  • Importance of Having Sufficiently Large Intermediate Layers
  • Preparing the Data
  • Building Network
  • Validating Approach using K-Fold Validation
Module 6
Deep Dive into Computer Vision (CV)

Topics Covered

  • Introduction to Convnets
  • Understanding Convolution Operation and Max Pooling Operation
  • Training a Convnet on a Small Dataset
  • Understanding the Relevance of Deep Learning for Small-Data Problems
  • Downloading the Data and Building the Network
  • Data Pre-processing and Data Augmentation
  • Visualizing Intermediate Activations
  • Visualizing Convnet Filters
  • Visualizing Heatmaps of Class Activation
Module 7
Deep Learning for Text and Sequences

Topics Covered

  • Encoding of Words or Characters
  • How is Word Embedding Being Used?
  • From Raw Text to Word Embeddings
  • Understanding Recurrent Neural Networks
  • What is LSTM and GRU Layers?
  • What is a First Recurrent Baseline
  • Using Recurrent Dropout to Fight Overfitting
  • Stacking Recurrent Layers
  • Using Bidirectional RNNs
  • Understanding 1D Convolution for Sequence Data
  • 1D Pooling for Sequence Data
  • Implementing a 1D Convnet
  • Combing CNNs and RNNs to Process Long Sequences

Advanced Deep Learning Professional (ADLP) involves rigorous usage of real-time case studies, hands-on exercises and group discussion

Some Reasons Why Learners Choose CASUGOL

  • International Certification Body

  • Presence in 38 Countries

  • Developed by Industry Experts

  • More than 42,000 professionals passed through our education system

  • Flexible program design for all individuals

  • Learn from internationally renowned leading industry experts, academics, and researchers

  • Support for participants during and after training

  • Enhance competency of workforce and improve individual career prospect

  • Customization of programs for specific industry, organization, government agencies, statutory boards

  • Learn in a highly interactive, supportive and encouraging environment

  • Regular invitation to attend courses / workshops / seminars / events at complimentary rate

Certificate Verification

All certificates issued by CASUGOL are to individuals who have successfully completed CASUGOL Certification Programs / Executive Workshops and have fulfilled all requirements by demonstrating proficiency in applying the knowledge and skills acquired.

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