In this video, Mandy from deeplizard demonstrate how to fine-tune a pre-trained model called VGG16 to classify images as cats and dogs.
In this episode, deeplizard demonstrates the various ways of saving and loading a Sequential model using TensorFlow’s Keras API.
deeplizard demonstrates how to create a confusion matrix, which will aid us in being able to visually observe how well a neural network is predicting during inference.
VIDEO SECTIONS
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
- 00:34 Plotting a Confusion Matrix
- 02:48 Reading a Confusion Matrix
- 04:56 Collective Intelligence and the DEEPLIZARD HIVEMIND
Visual scenes are often comprised of sets of independent objects. Yet, current vision models make no assumptions about the nature of the pictures they look at.
Yannic Kilcher explore a paper on object-centric learning.
By imposing an objectness prior, this paper a module that is able to recognize permutation-invariant sets of objects from pixels in both supervised and unsupervised settings. It does so by introducing a slot attention module that combines an attention mechanism with dynamic routing.
Content index:
- 0:00 – Intro & Overview
- 1:40 – Problem Formulation
- 4:30 – Slot Attention Architecture
- 13:30 – Slot Attention Algorithm
- 21:30 – Iterative Routing Visualization
- 29:15 – Experiments
- 36:20 – Inference Time Flexibility
- 38:35 – Broader Impact Statement
- 42:05 – Conclusion & Comments
In this video, Mandy from deeplizard demonstrate how to train an artificial neural network using the Keras API integrated within TensorFlow.
In this episode, deeplizard demonstrates how to create a simple artificial neural network using a Sequential model from the Keras API integrated within TensorFlow.
deeplizard shows us how to add batch normalization to a convolutional neural network.
Content index:
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
- 00:30 What is Batch Norm?
- 04:04 Creating Two CNNs Using nn.Sequential
- 09:42 Preparing the Training Set
- 10:45 Injecting Networks Into Our Testing Framework
- 14:55 Running the Tests – BatchNorm vs. NoBatchNorm
- 16:30 Dealing with Error Caused by TensorBoard
- 19:49 Collective Intelligence and the DEEPLIZARD HIVEMIND
deeplizard teaches us how to set up debugging for PyTorch source code in Visual Studio Code.
Content index:
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
- 00:27 Visual Studio Code
- 00:55 Python Debugging Extension
- 01:30 Debugging a Python Program
- 03:46 Manual Navigation and Control of a Program
- 06:34 Configuring VS Code to Debug PyTorch
- 08:44 Stepping into PyTorch Source Code
- 10:36 Choosing the Python Environment00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
- 00:27 Visual Studio Code
- 00:55 Python Debugging Extension
- 01:30 Debugging a Python Program
- 03:46 Manual Navigation and Control of a Program
- 06:34 Configuring VS Code to Debug PyTorch
- 08:44 Stepping into PyTorch Source Code
- 10:36 Choosing the Python Environment
- 12:30 Collective Intelligence and the DEEPLIZARD HIVEMIND
deeplizard debugs the PyTorch DataLoader to see how data is pulled from a PyTorch data set and is normalized.
We see the impact of several of the constructor parameters and see how the batch is built.
Content index:
- 0:00 Welcome to DEEPLIZARD – Go to deeplizard.com
- 0:45 Overview of Program Code
- 3:12 How to Use Zen Mode
- 3:56 Start the Debugging Process
- 4:38 Initializing the Sampler Based on the Shuffle Parameter
- 5:35 Debugging next(iter(dataloader))
- 7:57 Building the Batch Using the Batch Size
- 10:37 Get the Elements from Dataset
- 18:43 Tensor to PIL Image
- 20:41 Thanks for Contributing to Collective Intelligence
deeplizard teaches us how to normalize a dataset. We’ll see how dataset normalization is carried out in code, and we’ll see how normalization affects the neural network training process.
Content index:
- 0:00 Video Intro
- 0:52 Feature Scaling
- 2:19 Normalization Example
- 5:26 What Is Standardization
- 8:13 Normalizing Color Channels
- 9:25 Code: Normalize a Dataset
- 19:40 Training With Normalized Data