GPT-3 has 175 billion parameters/synapses and produces astonishing results.
Human brain has 100 trillion synapses. How much will it cost to train a language model the size of the human brain?
Lex Fridman works through the math.
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
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.
In this video Sabine Hossenfelder explains what the main differences are between the current approaches to artificial intelligence and human intelligence.
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
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