A deep dive in deep learning ocean with Pytorch & TensorFlow

A comprehensive course about deep learning models with theory, intuition and implementation with Pytorch and TensorFlow

Created by: Zeeshan Ahmad
Machine Learning and Statistical Signal Processing

What Will I Learn?

• Theory, Maths and Implementation of all the deep learning models
• Pre-requisites for deep learning such as data preprocessing, Regression Analysis and Logistic Regression
• Build Artificial Neural Networks and use them for Regression and Classification Problems
• Using GPU with Deep Learning Models.
• Creating our own custom loss functions and custom layers for deep learning models
• Convolutional Neural Network ( CNN )
• One Dimensional CNN
• Setting early stopping criterion in deep learning models
• Transfer Learning
• Recurrent Neural Networks ( RNN )
• Time series prediction and classification
• Autoencoders and Variational Autoencoder ( VAE )
• CNN Autoencoder and LSTM Autoencoder
• Generative Adversarial Networks (GANs)
• LSTM and Bidirectional LSTM
• Transformer
• Vision Transformer
• Time Series Transformer
• Neural Style Transfer
• Implementation of deep learning models in Pytorch and Tensor Flow

Requirements

• Some Programming Knowledge is preferable but not necessary
• Gmail account ( For Google Colab )
• Basic Maths

Target audience

• Beginners who want to learn Deep Learning from Scratch.
• Students enrolled in Deep Learning course in universities and colleges.
• Researchers in Deep Learning and Generative AI
• Students and Researchers who want to develop Python Programming skills and want to implement deep learning models both in Pytorch and TensorFlow
• For those who know how to implement deep learning in Matlab but want to switch to Pytorch and TensorFlow