Syllabus

1 to 1.5 months

5+ Case Studies / Projects

Prerequisites - Full Data Science Course

Introduction to A.I. & Neural Networks
- Deep Learning vs Machine Learning | Tech Advancement
- Intro to Artificial Neural Networks (ANN)
- Types of Artificial Neural Networks
Deep Learning in Python
- Deep Learning packages in python
- TensorFlow v2 | Keras
- Model Building with default TFLearn API
Keras API
- Keras vs TFLearn vs Pycharm APIs
- Model Building with Keras API Wrapper
- Losses | Metrics | Optimizers | Activations
- Callbacks - Early Stopping, TensorBoard
- Keras Backend
Deep Neural Networks
- Understand How Deep Network Works?
- Different variants of Gradient Descent
- Stochastic Gradient Descent vs Adam vs Others
- Hyper parameter Tuning
CNN - Convolutional Neural Networks
- Architecture of a CNN
- Kernel | Depth | Pooling
- Understanding and Visualizing a CNN
RNN - Recurrent Neural Networks
- Recurrent Neural Network Model
- Training RNNs with Back-propagation
- Long Short Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
Projects & Case Studies
- MNIST Dataset
- CIFAR-10 Dataset
- Facial Recognition
- Speech Recognition
- Natural Language Processing
- Sentiment Classification | Word Clouds
- Chatbot