In today’s video, NeuralNine going to build an intelligent AI chatbot using neural networks and natural language processing in Python.
YouTuber BharatiDWConsultancy provides a quick overview of quantum computing and its fundamental building block: the qubit!
deeplizard demonstrates how to use data augmentation on images using TensorFlow’s Keras API.
VIDEO SECTIONS
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
- 00:17 Introduction to Data Augmentation
- 01:32 Image Augmentation with Keras
- 08:16 Collective Intelligence and the DEEPLIZARD HIVEMIND
Watch Sascha Dittmann and friends build out a real time object detection system on the Jetson Nano.
There has been a large increase in interest in generative AI models of late.
Here’s a great introductory article (complete with code) on GAN’s in TensorFlow.
hese are models that can learn to create data that is similar to data that we give them. The intuition behind this is that if we can get a model to write high-quality news articles for example, then it must have also learned a lot about news articles in general. Or in other words, the model should also have a good internal representation of news articles. We can then hopefully use this representation to help us with other related tasks, such as classifying news articles by topic. Actually training models to create data like this is not easy, but in recent years a number of methods have started to work quite well. One such promising approach is using Generative Adversarial Networks (GANs). The prominent deep learning researcher and director of AI research at Facebook, Yann LeCun, recently cited GANs as being one of the most important new developments in deep learning:
In this episode, Mandy from deeplizard will be building on what we’ve learned about MobileNet combined with the techniques we’ve used for fine-tuning to fine-tune MobileNet for a custom image data set using TensorFlow’s Keras API.
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.
VIDEO SECTIONS
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com 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
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.
VIDEO SECTIONS
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com 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
Here are the most frequently asked ten important Tensorflow interview questions along with the solutions courtesy of fossbytes.com
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.
Index:
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com 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