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.