
Curriculum for AI and Deep Learning
- What are the Limitations of Machine Learning?
- What is Deep Learning?
- Advantage of Deep Learning over Machine learning
- Reasons to go for Deep Learning
- Real-Life use cases of Deep Learning
- History of AI
- Modern era of AI
- How is this era of AI different?
- Transformative Changes
- Role of Machine learning & Deep Learning in AI
- Hardware for AI (CPU vs. GPU vs. TPU)
- Software Frameworks for AI
- Deep Learning Frameworks for AI
- Key Industry applications of AI
- What is Tensor Flow?
- Tensor Flow code-basics
- Graph Visualization
- Constants, Placeholders, Variables
- Tensorflow Basic Operations
- Linear Regression with Tensor Flow
- Logistic Regression with Tensor Flow
- K Nearest Neighbor algorithm with Tensor Flow
- K-Means classifier with Tensor Flow
- Random Forest classifier with Tensor Flow
- Overview of important python packages for Deep Learning
- Quick recap of Neural Networks
- Activation Functions, hidden layers, hidden units
- Illustrate & Training a Perceptron
- Important Parameters of Perceptron
- Understand limitations of A Single Layer Perceptron
- Illustrate Multi-Layer Perceptron
- Back-propagation – Learning Algorithm
- Understand Back-propagation – Using Neural Network Example
- TensorBoard
- What is Deep Learning Networks?
- Why Deep Learning Networks?
- How Deep Learning Works?
- Feature Extraction
- Working of Deep Network
- Training using Backpropagation
- Variants of Gradient Descent
- Types of Deep Networks
- Feed forward neural networks (FNN)
- Convolutional neural networks (CNN)
- Recurrent Neural networks (RNN)
- Generative Adversal Neural Networks (GAN)
- Restrict Boltzman Machine (RBM
- Introduction to Convolutional Neural Networks
- CNN Applications
- Architecture of a Convolutional Neural Network
- Convolution and Pooling layers in a CNN
- Understanding and Visualizing a CNN
- Transfer Learning and Fine-tuning Convolutional Neural Networks
- Intro to RNN Model
- Application use cases of RNN
- Modelling sequences
- Training RNNs with Backpropagation
- Long Short-Term Memory (LSTM)
- Recursive Neural Tensor Network Theory
- Recurrent Neural Network Model
- What is Restricted Boltzmann Machine?
- Applications of RBM
- Collaborative Filtering with RBM
- Introduction to Autoencoders & Applications
- Understanding Autoencoders
- Define Keras
- How to compose Models in Keras
- Sequential Composition
- Functional Composition
- Predefined Neural Network Layers
- What is Batch Normalization
- Saving and Loading a model with Keras
- Customizing the Training Process
- Using TensorBoard with Keras
- Use-Case Implementation with Keras
- Intuitively building networks with Keras
- Computer Vision
- Text Data Processing
- Image processing
- Audio & video Analytics
- Internet of things (IOT
- Computer Vision
- Text Data Processing
- Image processing – PNG, PDF,JPEG, JPG etc.
- Speech analytics – Speech to text / Voice tonality
- Internet of Things – IOT