Umang R. -Data Scientist- Python, Machine learning, Artificial Intelligence, Java

ABOUT ME

ARTIFICIAL INTELLIGENCE  Neural Networks and Deep Learning; Activation and Loss functions; Gradient Descent; Batch Normalization; TensorFlow & Keras for Neural Networks; Hyper Parameter Tuning.  Computer vision; Convolutional Neural Networks; Convolution, Pooling, Padding & its mechanisms; Forward Propagation & Backpropagation for CNNs; CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet; Transfer Learning.  NLP Basics(Natural Language Processing); Stop Words; Tokenization; Stemming and lemmatization; Bag of Words Model; Word Vectorizer; TF-IDF; POS Tagging; Named Entity Recognition.  Sequential Models and NLP; Sequential data; RNNs and its mechanisms; Vanishing & Exploding gradients in RNNs; LSTMs - Long short-term memory; GRUs - Gated recurrent unit; LSTMs Applications; Time series analysis; LSTMs with attention mechanism; Neural Machine Translation; Advanced Language Models: Transformers, BERT, XLNet.  Advanced Computer Vision; Object Detection; YOLO, R-CNN, SSD; Semantic Segmentation; U-Net; Face Recognition using Siamese Networks; Instance Segmentation.  GANs (Generative adversarial networks); Generative Networks; Adversarial Networks; DCGANs -Deep Convolution GANs; Applications of GANs.  Reinforcement Learning (RL). MACHINE LEARNING Supervised learning; Unsupervised learning; Ensemble Techniques; Recommendation Systems Excellent skills of Python and Applied Statistics.