Upon completion of this course, you will be able to:

- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
- Understand the intuition behind Recurrent Neural Networks
- Apply Recurrent Neural Networks in practice
- Understand the intuition behind Self-Organizing Maps
- Apply Self-Organizing Maps in practice
- Understand the intuition behind Boltzmann Machines
- Apply Boltzmann Machines in practice
- Understand the intuition behind AutoEncoders
- Apply AutoEncoders in practice

**Intakes :**

Monthly

**Entry Requirements :**

- You should have passed basic high school mathematics.

##### modules 1

__Day 1__

*W**hat is Deep Learning and what are Neural Networks? (30 min)*

- 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 (60 min)

- 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 (60 min)*

- Getting the python libraries
- Constructing ANN
- Using the bank customer churn dataset
- Predicting if customer will leave or not

#### Evaluating Performance of an ANN (60 min)

- Evaluating the ANN
- Improving the ANN
- Tuning the ANN

#### Hands-On Exercise (60 min)

- 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 (60 min)

- What are CNN’s?
- Convolution operation
- ReLU Layer
- Pooling
- Flattening
- Full Connection
- Softmax and Cross-entropy

*Building a CNN (60 min)*

- Getting the python libraries
- Constructing a CNN
- Using the Image classification dataset
- Predicting the class of an image

##### modules 2

__Day 2__

*Evaluating Performance of a CNN (60 min)*

- Evaluating the CNN
- Improving the CNN
- Tuning the CNN

#### Hands-On Exercise (60 min)

- 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 (60 min)

- What are RNN’s?
- Vanishing Gradient problem
- LSTMs
- Practical intuition
- LSTM variations

*Building a RNN (60 min)*

- Getting the python libraries
- Constructing RNN
- Using the stock prediction dataset
- Predicting stock price

#### Evaluating Performance of a RNN (60 min)

- Evaluating the RNN
- Improving the RNN
- Tuning the RNN

#### Hands-On Exercise (60 min)

- Participants will be asked to build the RNN from the previous exercise
- Participants will be asked to improve the accuracy of their RNN

##### modules 3

__Day 3__** **

*Self-Organizing Maps (SOM) Intuition (60 min)*

- What are SOMs?
- K-means clustering
- How do SOMs learn?
- Reading Advanced SOMs

#### Building a SOM (60 min)

- Getting the python libraries
- Constructing SOM
- Using the fraud detection dataset
- Predicting fraud

#### Boltzmann Machine (BM) Intuition (60 min)

- What is a BM?
- Energy-Based Models (EBM)
- Restricted Boltzmann Machine (RBM)
- Contrastive Divergence
- Deep Belief Networks (DBN)
- Deep Boltzmann Machines (DBM)

*Building a BM (60 min)*

- Getting the python libraries
- Constructing BM
- Using the recommendation dataset
- Predict recommendation for a movie (Like or Not)

#### Auto Encoders Intuition (60 min)

- What are Auto Encoders?
- Biases
- Training an Auto Encoder
- Overcomplete hidden layers
- Sparse Autoencoders
- Denoising Autoencoders
- Contractive Autoencoders
- Stacked Autoencoders
- Deep Autoencoders

*Building an Auto Encoder (60 min)*

- Getting the python libraries
- Constructing Auto Encoder
- Using the recommendation dataset
- Predict rating for a movie (1 to 5)

*Hands-on Exercise (60 min)*

- Participants will be asked to construct a BM and Auto Encoder from the previous exercise

*A Certificate of Completion shall be given upon completion.

###### Duration

###### Tuition Fees Structure