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