In this deeplizard episode, learn how to prepare and process our own custom data set of sign language digits, which will be used to train our fine-tuned MobileNet model in a future episode.


  • 00:00 Welcome to DEEPLIZARD – Go to for learning resources
  • 00:40 Obtain the Data
  • 01:30 Organize the Data
  • 09:42 Process the Data
  • 13:11 Collective Intelligence and the DEEPLIZARD HIVEMIND

LAWs, short for Lethal Autonomous Weapons may change the nature of warfare forever.

With the development of AI systems, drones that can autonomously find and eliminate a targeted individual are only years away not decades.


At this point, the issue is not with hardware but with software, meaning how fast new AI algorithms can be developed and be implemented for certain military purposes.

Murtaza’s Workshop – Robotics and AI posted this video to explains how to perform Facial recognition with high accuracy.

We will first briefly go through the theory and learn the basic implementation. Then we will create an Attendance project that will use webcam to detect faces and record the attendance live in an excel sheet.

Link to the Article:

Jeremy Howard provides this introductory lesson on Deep Learning for Coders.

In this first lesson, we learn about what deep learning is, and how it’s connected to machine learning, and regular computer programming. We get our GPU-powered deep learning server set up, and use it to train models across vision, NLP, tabular data, and collaborative filtering. We do this all in Jupyter Notebooks, using transfer learning from pretrained models for the vision and NLP training.

We discuss the important topics of test and validation sets, and how to create and use them to avoid over-fitting. We learn about some key jargon used in deep learning.

We also discuss how AI projects can fail, and techniques for avoiding failure.


  • 00:00 – Introduction
  • 06:44 – What you don’t need to do deep learning
  • 08:38 – What is the point of learning deep learning
  • 09:52 – Neural Nets: a brief history
  • 16:00 – Top to bottom learning approach
  • 23:06 – The software stack
  • 39:06 – Git Repositories
  • 42:20 – First practical exercise in Jupyter Notebook
  • 48:00 – Interpretation and explanation of the exercise
  • 55:35 – Stochastic Gradient Descent (SGD)
  • 1:01:30 – Consider how a model interacts with its environment
  • 1:07:42 – “doc” function and fastai framework documentation
  • 1:16:20 – Image Segmentation
  • 1:17:34 – Classifying a review’s sentiment based on IMDB text reviews
  • 1:18:30 – Predicting salary based on tabular data from CSV
  • 1:20:15 – Lesson Summary

deeplizard  introduces MobileNets, a class of light weight deep convolutional neural networks that are vastly smaller in size and faster in performance than many other popular models.


  • 00:00 Welcome to DEEPLIZARD – Go to for learning resources
  • 00:17 Intro to MobileNets
  • 02:56 Accessing MobileNet with Keras
  • 07:25 Getting Predictions from MobileNet
  • 13:32 Collective Intelligence and the DEEPLIZARD HIVEMIND

vcubingx provides a visual introduction to the structure of an artificial neural network.

The Neural Network, A Visual Introduction | Visualizing Deep Learning, Chapter 1

  • 0:00 Intro
  • 1:55 One input Perceptron
  • 3:30 Two input Perceptron
  • 4:40 Three input Perceptron
  • 5:17 Activation Functions
  • 6:58 Neural Network
  • 9:45 Visualizing 2-2-2 Network
  • 10:59 Visualizing 2-3-2 Network
  • 12:33 Classification
  • 13:05 Outro

In this video, Mandy from deeplizard  demonstrates how to use the fine-tuned VGG16 Keras model that we trained in the last episode to predict on images of cats and dogs in our test set.


  • 00:00 Welcome to DEEPLIZARD – Go to for learning resources
  • 00:17 Predict with a Fine-tuned Model
  • 05:40 Plot Predictions With A Confusion Matrix
  • 05:16 Collective Intelligence and the DEEPLIZARD HIVEMIND