freeCodeCamp.org posted this DeepLizard video.

This course will teach you how to use Keras, a neural network API written in Python and integrated with TensorFlow. We will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more!

Course index:

  • (00:00:00) Welcome to this course
  • (00:00:16) Keras Course Introduction
  • (00:00:50) Course Prerequisites
  • (00:01:33) DEEPLIZARD Deep Learning Path
  • (00:01:45) Course Resources
  • (00:02:30) About Keras
  • (00:06:41) Keras with TensorFlow – Data Processing for Neural Network Training
  • (00:18:39) Create an Artificial Neural Network with TensorFlow’s Keras API
  • (00:24:36) Train an Artificial Neural Network with TensorFlow’s Keras API
  • (00:30:07) Build a Validation Set With TensorFlow’s Keras API
  • (00:39:28) Neural Network Predictions with TensorFlow’s Keras API
  • (00:47:48) Create a Confusion Matrix for Neural Network Predictions
  • (00:52:29) Save and Load a Model with TensorFlow’s Keras API
  • (01:01:25) Image Preparation for CNNs with TensorFlow’s Keras API
  • (01:19:22) Build and Train a CNN with TensorFlow’s Keras API
  • (01:28:42) CNN Predictions with TensorFlow’s Keras API
  • (01:37:05) Build a Fine-Tuned Neural Network with TensorFlow’s Keras API
  • (01:48:19) Train a Fine-Tuned Neural Network with TensorFlow’s Keras API
  • (01:52:39) Predict with a Fine-Tuned Neural Network with TensorFlow’s Keras API
  • (01:57:50) MobileNet Image Classification with TensorFlow’s Keras API
  • (02:11:18) Process Images for Fine-Tuned MobileNet with TensorFlow’s Keras API
  • (02:24:24) Fine-Tuning MobileNet on Custom Data Set with TensorFlow’s Keras API
  • (02:38:59) Data Augmentation with TensorFlow’ Keras API
  • (02:47:24) Collective Intelligence and the DEEPLIZARD HIVEMIND

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

TensorFlow gets a new release.

Here’s a great round up of the new features and improvements.

Google just release TensorFlow 2.2.0 with many news features and improvements, the new Profiler for TensorFlow 2 for CPU/GPU/TPU. TensorFlow 2.2.0 is dropping the support for Python 2, which is already passed end of life in January 2020. There are many other features and improvements with this version of […]

TensorFlow.js is a library for developing and training machine learning models in JavaScript and deploying them in a browser or on Node.js.

It is an open source, hardware-accelerated JavaScript library for training and deploying machine learning models.

Amazing to see the innovation of TensorFlow combined with the reach of more developers.

In recent times, a lot of attention is being paid to artificial intelligence (AI) and machine learning (ML). And in this context, the two most popular technologies are the Python and R environments or even C++ libraries. One of the most popular frameworks among developers is TensorFlow, which was developed by Google in 2011. Most of TensorFlow was designed in C++ and has bindings to Python or Java or R, but the most crucial language is missing, which is JavaScript.

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