Advanced Deep Learning Professional (ADLP)

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Online ‘Live’ Session: 9:30am to 5:30pm (SGT / UTC +8) 5 Lessons Click on preferred date to register

Course Information

  • Duration: 5 Day / 40 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Data Scientist, Data Analyst, Analyst, System Analyst, Technologist, System Engineer, IT Professionals and Anyone seeking to acquire advanced knowledge on Deep Learning

Course Objective

Advanced Deep Learning Professional (ADLP) is designed for anyone interested in acquiring the advanced knowledge and skills required to implement / manage Deep Learning an organization


It is preferred that participants have some prior experience in Python Programming, Data Analytics or successfully completed and received a Certificate of Competency in Python Programming Essentials (PPE).


Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Deep Learning based on the syllabus covered
Module 1 Introduction to Deep Learning
Topics Covered
  • What is Artificial Intelligence and Machine Learning?
  • Understanding Learning Representation of Data
  • Fundamentals of Deep Learning
  • How Deep Learning Works
  • Deep Learning and Its Application
  • Future of Deep Learning

Module 2 Understanding Machine Learning Essentials
Topics Covered
  • Probabilistic Modeling
  • Kernel Methods
  • Decision trees, Random Forests, and Gradient Boosting Machines
  • Understanding Neural Networks
  • What Makes Deep Learning Different
  • Hardware, Data, and Algorithms of Deep Learning

Module 3 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 4 Neural Networks: Tensor Operations
Topics Covered
  • Element-wise Operations
  • Broadcasting
  • Tensor Dot
  • Tensor Reshaping
  • Geometric Interpretation of Tensor Operations
  • Geometric Interpretation of Deep Learning

Module 5 Neural Networks: Gradient-Based Operations
Topics Covered
  • Introduction to Derivative
  • Derivative of a Tensor Operation: Gradient
  • Stochastic Gradient Descent
  • The Backpropagation Algorithm

Module 6 Structure of a Neural Network
Topics Covered
  • 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 7 Getting Ready for Deep Learning
Topics Covered
  • Key Considerations
  • Setting up Jupyter Notebooks
  • Setting up of Keras
  • Deep Learning in a Cloud
  • Identifying the Best GPU for Deep Learning

Module 8 Binary Classification
Topics Covered
  • Preparing the Data
  • Building Network
  • Validating Approach
  • Using a Trained Network to Generate Predictions on New Data

Module 9 Multi-Class Classification
Topics Covered
  • Preparing the Data
  • Building Network
  • Validating Approach
  • Generate Predictions on New Data
  • Alternative Ways to Handle Labels and Loss
  • Importance of Having Sufficiently Large Intermediate Layers

Module 10 Regression Model
Topics Covered
  • Preparing the Data
  • Building Network
  • Validating Approach using K-Fold Validation

Module 11 Deep Learning for Computer Vision
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 12 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

Module 13 Advanced Deep Learning Techniques
Topics Covered
  • Introduction to Functional APIs
  • Multi-Input and Multi-Output Modes
  • Directed Acyclic Graphs of Layers
  • Layer Weight Sharing
  • Using Keras Callbacks and TensorBoard
  • Understanding TensorFlow Visualization Framework
  • Advanced Architecture Patterns
  • Hyperparameter Optimization
  • Model Ensembling

Module 14 Generative Deep Learning
Topics Covered
  • Text Generation with LSTM
  • Implementing Deep Dream in Keras
  • Neural Style Transfer in Keras
  • Generating Images with Variational Autoencoders
  • What is a Generative Adversarial Networks
  • Training DCGAN

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