• University of Global Village(UGV)
  • |
  • Iversity part of Springer Nature

Deep Learning with Python

Deep Learning with Python

Deep Learning with Python

Learning Outcomes

After attending the course, the participants:

  • Will be able to demonstrate understanding of Deep Learning an its different variants.
  • Identify the deep learning algorithms which are more appropriate for various types of learning tasks in various domains.
  • Implement deep learning algorithms and solve real-world problems.

Details Plan:

Deep learning basics:

• Intro, History, capabilities, the perceptron • Activation Functions • Loss Functions • Gradient Descent, Back Propagation • Optimizers • Hyper Parameters • Different Types of Architectures .

Regression and Classification :

• Deep Learning Case Study with TensorFlow

Convolutional neural networks:

• Intro to CNNs, Convolution, Correlation, FIltering. • CNN architectures • Detection and Segmentation • Visualizing and Understanding • Advanced CNNs for computer vision

Representation Learning:

• Word Embedding • Auto Encoder

Recurrent Units :

• Recurrent Neural networks (RNNs) • Advanced RNN: LSTM, GRU,

Advanced Topics :

• Deep reinforcement learning • GAN


● Basic Python Programming Knowledge

● Some exposure to Numpy, Pandas etc.


● 20 Hours