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MLDawn

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139 videos

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0:56

The Future of AI Why Self Reflection Matters

0:48

Unraveling Inductive Bias The Key to Understanding Smoothness

0:21

The Future of AI Insights from a Machine Learning Pioneer

0:49

Unlocking Nature's Secrets Machine Learning Meets Biology

0:48

The Evolution of Machine Learning 25 Years of Change

0:50

Understanding the Interpolation Point in Regression Models

0:59

Revolutionizing Machine Learning The Power of Continuous Learning

0:20

Understanding Model Complexity Smoother Solutions Explained

0:47

Neural Networks vs The Human Brain Key Differences

0:52

Understanding Interpolation Thresholds in Machine Learning Models

0:58

Navigating Paper Rejections Insights for Researchers

0:47

Understanding Inductive Bias Control Your Model’s Risk

0:58

How I Discovered My Passion for Machine Learning

0:57

Understanding Classical vs Modern Regimes in Data Science

0:45

How Does the Brain Understand Action in Sentences

0:53

The Future of AI Quantum Computing and Deep Models

0:22

How Biological Neural Nets Inspire AI Innovation

1:02:47

Perception & Neuro-Mimetic Design under the Free Energy Principle

0:24

Revealing Surprising Insights on U Shape Behavior

0:38

LLM Predicts Brain Activity

0:49

Sense of Crisis and Double Descent Explained

20:37

What is Surprise in Active Inference?

1:00

Exploring Brain Activity Using BERT Language Models

12:35

How to Validate a Multi Label Classification Model?

0:54

What is the Difference Between Convolutional Neural Network (CNN) and MLP?

26:54

How to Train a Multi Label classification Model?

0:51

What is Frozen vs. Plastic Machine Learning?

23:36

Multi Label Classification in Machine Learning: How to Build a ResNet18 Model

18:25

Multi Label Classification in Machine Learning - Split into Training, Validation, Test Sets

43:05

What is the Difference Between PhD and Postdoc?

1:01

Occam's Razor: Start simple! #machinelearning #deeplearning

1:01

Which Machine Learning Algorithms to Use for Anomaly Detection? #machinelearning

14:53

Hello from Melbourne Australia :-)

11:01

A Short Pause

0:54

Uncovering the Insane Power of Curvature in Machine Learning

10:37

Multi Label Classification: Dataloaders in Pytorch

0:56

Active Inference Explained: Unveiling the Surprising Origins of Neural Network Theory

29:08

Multi Label Classification: Customized Pytorch Dataset

0:56

Active Inference Explained: Complexity in Belief Updating

20:27

Active Inference Explained: Bayes' Rule Numeric Example

0:48

Active Inference Explained: Insane Logic Behind Planning

1:09:43

Active inference and Dynamical Causal Modeling Explained with Dr. Adeel Razi

0:43

Active Inference Explained: Self-hood and Action Selection

20:54

Exploring a Multi Label Classification Dataset using Python

0:44

Occams Razor and the Free Energy Principle

5:19

A Dataset for Multi Label Classification

0:51

Active Inference and Minimizing Surprise

11:28

19- How to Detect OVERFITTING in Machine Learning?

0:48

How to choose the best policy using the Free Energy Principle

8:40

18- Validation Set Approach To Solving Over-fitting Problem

0:46

Bayesian Model Selection in Neural Networks #shorts

0:56

Bayesian Belief Updating #shorts

0:50

The Surprising Connection Between Our Brains And Environment #shorts

43:09

Active Inference Explained: Perception as Inference

26:59

17- Visualizing the Feature Maps in Convolutional Neural Networks (CNN)

27:30

Predictive Coding Beats Backpropagation?

1:14:32

Deep Learning & MLOps - Episode 8: Variational Autoencoder Loss Function Derivation (PART1)

1:12:33

Deep Learning & MLOps - Episode 7: What are Autoencoders?

1:13:22

Deep Learning & MLOps - Episode 6: Exploratory Data Analysis

1:17:22

Deep Learning & MLOps - Episode 5: Create an Environment with GPU Support

1:30:28

Deep Learning & MLOps - Episode4: Bootsrap your Machine Learning Project

1:20:49

Deep Learning & MLOps - Episode3: Dev Environment Setup

34:53

Deep Learning & MLOps - Episode2: Project Roadmap

50:18

Deep Learning & MLOps - Episode1: mlops and machine learning (Learning Objectives)

17:31

Multi Label Classification and Backpropagation of Gradients

28:46

16- Train a New-Born Convolutional Neural Network in Pytorch

1:49:48

How to Design Machine Learning Experiments (Step-by-Step)

9:04

Multi Label Classification and Forward Pass

6:48

Multi Label Classification and Loss Function

8:07

Multi Label Classification and Ground Truth Vector

10:02

What Output Function to use in Multi Label Classification?

59:08

AI Application in Space : Design and Deployment

5:23

What is Multi Label Classification in Machine Learning?

22:37

15- Test a New-Born Convolutional Neural Network in Pytorch

0:16

Machine learning and differential equations | ANNs can solve them!

16:09

14- Pytorch: What is model.eval?

7:29

What you should do if your research paper got rejected!

22:26

Mathematics of Backpropagation in neural networks : Regression

0:16

Simple is Better: Occam's Razor!

0:16

A Transfer Learning Secret!

0:16

Imbalanced Dataset and Cross-Entropy: NO!!!

1:35:51

Active inference explained with Prof. Karl Friston

21:43

13- Inside convolutional layers and fully connected layers in Pytorch!

23:36

12- Convolutional layers and linear layers CRUSH input dimensionality: Step-by-step!

27:56

11- How to code a CNN using Pytorch!

9:37

10- Iterate through the MRI Dataset (Part-2): Using DataLoader

17:10

9- Iterate through the MRI Dataset (Part-1): Using For Loop and Next(Iter) commands

29:42

8-Create CUSTOMIZED Pytorch Class for brain MRI images

1:06:21

A talk on Novelty Detection (an more) with Prof. Thomas Dietterich

23:01

7-What is Pytorch's Abstract Dataset Class: Overriding len and getitem!

29:36

A Gentle 101 Talk on Artificial Neural Networks at the Insight Centre (UCD, Ireland)

20:36

6-How to visualize our Brain MRI images?

23:22

5-Reading the Brain MRI images in Python

24:45

ANN-12-Backpropagation Algorithm-PART(1): Multilayer Perceptrons

24:06

4-Finding a Kaggle Brain MRI Dataset and Importing Packages!

20:41

3-Replicate my Environment on Your Machine!

15:49

2-Step by step of Anaconda environment setup!

26:36

5- A gentle introduction to Reinforcement Learning (RL)

40:17

bias-variance trade-off explained with Dr. Mikhail Belkin

9:26

1-Introduction