DLFC : DEEP LEARNING FOUNDATION CERTIFICATE

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

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

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