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