- Data science & its importance
- Key Elements of Data Science
- Artificial Intelligence & Machine Learning Introduction
- Who uses AI?
- AI for Banking & Finance, Manufacturing, Healthcare, Retail and Supply Chain
- What makes a Machine Learning Expert?
- What to learn to become a Machine Learning Developer?
- Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
- Deep Learning: A revolution in Artificial Intelligence
- What is Deep Learning?
- Advantage of Deep Learning over Machine learning
- 3 Reasons to go for Deep Learning
- Real-Life use cases of Deep Learning
Neural Networks Basics
- How Neural Networks Work?
- Various activation functions – Sigmoid, Relu, Tanh
- Perceptron and Multi-layer Perceptron
- What is TensorFlow?
- TensorFlow code-basics
- Graph Visualization
- Constants, Placeholders, Variables
- Creating a Model
- Step by Step - Use-Case Implementation
- Introduction to Keras
- Understand Neural Networks in Detail
- Illustrate Multi-Layer Perceptron
- Backpropagation – Learning Algorithm
- Understand Backpropagation – Using Neural Network Example
- MLP Digit-Classifier using TensorFlow
Deep Neural Networks
- Why Deep Networks
- Why Deep Networks give better accuracy?
- Understand How Deep Network Works?
- How Backpropagation Works?
- Illustrate Forward pass, Backward pass
- Different variants of Gradient Descent
- Types of Deep Networks
- Batch Normalization
- Activation and Loss functions
- Hyper parameter tuning
- Training challenges and techniques
- Optimizers, learning rate, momentum, etc.
Convolutional Neural Networks
- Introduction to CNNs
- CNNs Application
- Architecture of a CNN
- Forward propagation & Backpropagation for CNNs
- Convolution, Pooling, Padding & its mechanisms
- Understanding and Visualizing a CNN
- An overview of pre-trained models (AlexNet, VGGNet, InceptionNet & ResNet) and Transfer Learning
- Image classification using CNN
Advanced Computer Vision
- Auto encoders
- Semantic segmentation
- Siamese Networks
- Object & face recognition using techniques above
Natural Language Processing
- Sentiment Analysis
- Topic Summarization
- Topic Modelling
- Nltk, Gensim, vader, etc.
- Bag of Words and Tf-IDF
- Cosine Similarity of terms, documents concepts
- Text Cleaning and Pre-processing using Regex
- Tokenization, Stemming and Lemmatization
RNN And LSTM
- Introduction to Sequential data
- Word embeddings and lang translation
- RNNs and its mechanisms
- Vanishing & Exploding gradients in RNNs
- Time series analysis
- LSTMs with attention mechanism
Visualization Using Tensorboard
- What is Tensor board?
- Test vs Train set accuracy
- Occlusion Experiment
- CAM, Saliency and Activation maps
- Visualizing Kernels
- Style transfer
Reinforcement Learning And Gans
- How GANs work?
- Applications of GANs (Generative adversarial networks)
- Summary and Closing Remarks