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L13.4 Convolutional Filters and Weight-Sharing

L13.3 Convolutional Neural Network Basics

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Day 2- MLOPS End To End Implementation From Basics- Machine Learning

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PyTorch Time Sequence Prediction With LSTM - Forecasting Tutorial

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Machine Learning Frameworks - The Landscape

L13.0 Introduction to Convolutional Networks -- Lecture Overview

L12.6 Additional Topics and Research on Optimization Algorithms

L12.5 Choosing Different Optimizers in PyTorch

L12.4 Adam: Combining Adaptive Learning Rates and Momentum

L12.3 SGD with Momentum

L12.2 Learning Rate Schedulers in PyTorch

L12.1 Learning Rate Decay

L12.0: Improving Gradient Descent-based Optimization -- Lecture Overview

Day 1- MLOPS End To End Implementation- Machine Learning

TensorFlow DCGAN Tutorial

MLOps Live Community Session Announcement

L11.7 Weight Initialization in PyTorch -- Code Example

L11.6 Xavier Glorot and Kaiming He Initialization

L11.5 Weight Initialization -- Why Do We Care?

L11.4 Why BatchNorm Works

L11.3 BatchNorm in PyTorch -- Code Example

L11.2 How BatchNorm Works

L11.1 Input Normalization

L11.0 Input Normalization and Weight Initialization -- Lecture Overview

Waste Classification Machine Learning Classification Project-Waste Recycling

CycleGAN Paper Walkthrough

L10.5.4 Dropout in PyTorch

L10.4 L2 Regularization for Neural Nets

L10.2 Data Augmentation in PyTorch

L10.0 Regularization Methods for Neural Networks -- Lecture Overview

Pix2Pix implementation from scratch

How (and why) you should undervolt your GPU - A step by step guide (Deep Learning/Gaming/Mining)