Courses
Deep Learning Foundation Certificate (DLFC)
Deep Learning is the fastest-growing field in Machine Learning and highly crucial for Artificial Intelligence, using many-layered Deep Neural Networks (DNNs) to make sense of data and enable many practical machine assists.
Pass the 2-hour exam consisting of 50 Online Multiple Choice Questions with the score of 70% to earn this certification
One of the most industry validated digital skills certification in Asia. Course and exams is taken by the industries / academia / governments from 26 countries in Asia via 30+ Authorised Training Partners (ATP) and 50+ Authorised Academy Partners (AAP).
“Vendor-neutral” certifications refer to any certifications that are not directly associated with specific IT vendors. These certifications tend to develop a knowledge and skill base that is universally applicable and individual with skills that are more conceptual, setting you up to work with a greater range of products / tools.
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Deep Learning is the fastest-growing field in Machine Learning and highly crucial for Artificial Intelligence, using many-layered Deep Neural Networks (DNNs) to make sense of data and enable many practical machine assists.
Our Deep Learning Foundation Certifification course will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
AI is transforming many industries. This 3-day, instructor-led, Deep Learning Foundation course provides a pathway for you to take the defifinitive step in the world of AI by helping you gain the knowledge and skills to level up your career.
- Understand the intuition behind Artificial Neural Networks
- Understand the intuition behind Convolutional Neural Networks
- Apply Artificial and Convolutional Neural Networks in practice
- Understand the intuition behind Recurrent Neural Networks
- Apply Recurrent Neural Networks in practice
- Introduction to Deep Learning
- Getting Started with Deep Learning Approaches to Object Detection using DIGITS
- Deep Learning for Image Segmentation
- Deep Learning Network Deployment
- Medical Image Segmentation using DIGITS
- Introduction to Deep Learning with MXNET
- Introduction to RNNs Signal Processing using DIGITS
- Deep Learning with Electronic Health Record
Training Module
What is Deep Learning and what are Neural Networks?
- Deep Learning as a branch of AI
- Neural networks and their history and relationship to neurons
- Creating a neural network in Python
Artificial Neural Networks (ANN) Intuition
- Understanding the neuron and neuroscience
- The activation function (utility function or loss function)
- How do NN’s work?
- How do NN’s learn?
- Gradient descent
- Stochastic Gradient descent
- Backpropagation
Building an ANN
- Getting the python libraries
- Constructing ANN
- Using the bank customer churn dataset
- Predicting if customer will leave or not
Evaluating Performance of an ANN
- Evaluating the ANN
- Improving the ANN
- Tuning the ANN
Hands-On Exercise
- Participants will be asked to build the ANN from the previous exercise
- Participants will be asked to improve the accuracy of their ANN
Convolutional Neural Networks (CNN) Intuition
- What are CNN’s?
- Convolution operation
- ReLU Layer
- Pooling
- Flattening
- Full Connection
- Softmax and Cross-entropy
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Building a CNN
- Getting the python libraries
- Constructing a CNN
- Using the Image classification dataset
- Predicting the class of an image
Evaluating Performance of a CNN
- Evaluating the CNN
- Improving the CNN
- Tuning the CNN
Hands-On Exercise
- Participants will be asked to build the CNN from the previous exercise
- Participants will be asked to improve the accuracy of their CNN
Recurrent Neural Networks (RNN) Intuition
- What are RNN’s?
- Vanishing Gradient problem
- LSTMs
- Practical intuition
- LSTM variations
Building a RNN
- Getting the python libraries
- Constructing RNN
- Using the stock prediction dataset
- Predicting stock price
Evaluating Performance of a RNN
- Evaluating the RNN
- Improving the RNN
- Tuning the RNN
Hands-On Exercise
- Participants will be asked to build the RNNfrom the previous exercise
- Participants will be asked to improve the accuracy of their RNN
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Natural Language Processing and Word Embeddings
- Word representation
- Word embeddings
- Word2Vec
- Sentiment Classification
Sequence Models and Attention Mechanism
- Picking the next word or sentence
- Beam Search
- What is an Attention Model?
- Speech Recognition
- Trigger Word Detection
- Working with Advanced NLP Models – GPT – 3
Hands-On Exercise
- Participants will be asked to use attention-based sequence models and evaluate their effectiveness
- Participants will be asked to improve the accuracy of their attention-based models
Building a Deep Learning Neural Network (DQN)
- Getting the Python libraries
- Constructing the DQN
- Working with OpenAI Gym
- Optimising a DQN
Reinforcement Learning
- What is reinforcement learning?
- K-Armed Bandit Problem – exploration / exploitation trade-off
- Markov Processes
- Policies and value functions
- Dynamic programming
- Q learning and Deep Q learning
Hands-On Exercise
- Participants will be asked to build the DQN from the previous exercise
- Participants will be asked to improve the accuracy of their DQN
Evaluating Performance of a DQN
- Evaluating the DQN
- Improving the DQN
- Tuning the DQN
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