Deep Learning in Python (using Keras and TensorFlow)

UNIT I: Introduction to Deep Learning & Neural Networks
  • Setting Up Environment for Deep Learning: Keras, Tensorflow, Jupyter etc
  • TensorFlow basics
  • Theoretical Foundations of Deep Learning, Deep Learning vs Machine Learning
  • Deep Learning history, biological inspirations and demo with MNIST dataset to start with.
  • Understanding Neural Network, How neural networks learn, Architecture of Neural Networks
  • Activations Functions: Sigmoid, Tanh, Softmax, Softmaxcrossentropy,SigmoidCrossentropy
  • Basic ANN Types: Dense Neural Networks, Convolution Neural Networks, Recurrent Neural Networks
UNIT II: CNN Theory and Project
  • CNN: Deep-dive, Overfitting, Decaying Leaning, Dropout
  • CNN Project -  finding presence of a certain class of object in images.
  • Object Detection Systems/Computer Vision: YOLO (You Look Only Once)
UNIT III: RNN Theory & Projects
  • Recurrent Neural Networks: LSTM, GRU CELL
  • Modern RNN Architectures/ Frameworks: Embed-Encode-Attend-Predict Framework - Developed by Google
  • RNN Project: Project - Toxic comment detector
  • Other Deep Learning Projects


For registrations contact: 8169 543 099 or 9860 246 128.