ML Fundamentals

March 29- Live Virtual Mock Interview For Fresher For Data Science Role-iNeuron Student

Multiclass Classification : Data Science Concepts

Follow These Playlist Before Interviews- Guide to Crack Data Science Interviews

Learn NumPy In 30 Minutes

AI Face Body and Hand Pose Detection with Python and Mediapipe

Join with me to attend FREE AI Conference NVIDIA GTC: April 12 to 16

Impact Measurement: Where are we and what are we learning?

AI+X: AI Innovation in Healthcare

Leadership in Extraordinary Times S3E5: The Great Decoupling

A Chat with Andrew on MLOps: From Model-centric to Data-centric AI

Word embedding using keras embedding layer | Deep Learning Tutorial 40 (Tensorflow, Keras & Python)

L13.9.3 AlexNet in PyTorch

L13.9.2 Saving and Loading Models in PyTorch

Project InnerEye: Augmenting cancer radiotherapy workflows with deep learning and open source

ProGAN implementation from scratch

Build by Small Pieces // Igor Lushchyk // MLOps Meetup #55 short clip

Autoencoder In PyTorch - Theory & Implementation

Operationalizing Machine Learning at a Large Financial Institution // Daniel Stahl //MLOps Meetup#56

MARCH MADNESS - Will My Machine Learning Model Beat Your Bracket?

ProGAN Paper Walkthrough

Should You Scale Your Data ??? : Data Science Concepts

Rainfall Prediction- Converting A Kaggle Project to End To End Machine Learning Project

Friday Live Q&A Ask Anything RelatedData Science

Colab Pro Now Available In India, Brazil, France, Thailand,Japan,UK- BOON FOR Data Science Aspirants

Successful Career Transition Story From Fresher College Student To Data Science-Sameer Singh

Amazing Initiative For School Kids By iNeuron

Leadership in Extraordinary Times S3E6: Saints or Sinners?

Texthero-Text Preprocessing, Representation And Visualization From Zero to Hero.

Oxford High Performance Leadership Programme | Ethos and Virtual Design

L13.9.1 LeNet-5 in PyTorch

L13.8 What a CNN Can See

L13.7 CNN Architectures & AlexNet

L13.6 CNNs & Backpropagation

DeepLearningAI Live Stream

Lux - Python Library for Intelligent Visual Discovery

Devops Vs MLOPS- Understand The Differences And Why IT is Important

Day 4- MLOPS Continuous Integration And Model Tracking Using MLFlow- Machine Learning

L13.4 Convolutional Filters and Weight-Sharing

L13.3 Convolutional Neural Network Basics

L13.1 Common Applications of CNNs

L13.0 Introduction to Convolutional Networks -- Lecture Overview

Day 3- MLOPS End To End Implementation With Deployment- Machine Learning

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

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