Deep Learning

Deep Learning (DL) is an advanced subset of ML that leverages neural networks to learn from vast amounts of data. Neural nets function like the human brain, enabling systems to learn hidden patterns from data by themselves and build more efficient decision rules. 

Dense to Sparse - AI World

• What we do in CNN - Convert Dense to Sparse with convolution and activations 

• What we do in NLP - Text Preprocessing: Stemming / Lemmatization / Stop-word removal - Vectorization 

• Topic Modelling - Words - Documents - Non-Negative Matrix Factorization 

• ML Feature Engineering / Recommendations - PCA / SVD 

Zero to one in Deep Learning

• Introduction to Backpropagation

• Introduction to CNN

• CNN with Keras / Transfer Learning

• Action Recognition / CNN based use cases

• Object detection Architecture - SSD, Yolo, RCNN Models

• RNN / Text based Use cases

• RNN - Sentiment Analysis

• Transformers / BERT

• Embedding creation / Topic Modelling

• Keyword search / Semantic Search

• Multimodal use cases with VectorDB

Applications

• Forecasting

• Recommendations

• Vision detection / Classification

• Beauty / Try on 

• ADAS vision models