A deep dive in deep learning ocean with Pytorch & TensorFlow

What you’ll 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

Description:
Course ContentsDeep Learning and revolutionized Artificial Intelligence and data science.  Deep Learning teaches computers to process data in a way that is inspired by the human brain.This is complete and comprehensive course on deep learning. This course covers the theory and intuition behind deep learning models and then implementing all the deep learning models both in Pytorch and Tensor flow.Practical Oriented explanations Deep Learning Models with implementation both in Pytorch and Tensor Flow.No need of any prerequisites. I will teach you everything from scratch.Job Oriented StructureSections of the Course· Introduction of the Course· Introduction to Google Colab· Python Crash Course· Data Preprocessing· Regression Analysis· Logistic Regression· Introduction to Neural Networks and Deep Learning· Activation Functions· Loss Functions· Back Propagation· Neural Networks for Regression Analysis· Neural Networks for Classification· Dropout Regularization and Batch Normalization· Optimizers· Adding Custom Loss Function and Custom Layers to Neural Networks· Convolutional Neural Network (CNN)· One Dimensional CNN· Setting Early Stopping Criterion in CNN· Recurrent Neural Network (RNN)· Long Short-Term Memory (LSTM) Network· Bidirectional LSTM· Generative Adversarial Network (GAN)· DCGANs· Autoencoders· LSTM Autoencoders· Variational Autoencoders· Neural Style Transfer· Transformers· Vision Transformer· Time Series Transformers. K-means Clustering. Principle Component Analysis.  Deep Learning Models with implementation both in Pytorch and Tensor Flow.

Who this course is for:
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