Welcome to The ALT Code!

Kaggle image dataset used in the video: https://www.kaggle.com/c/carva....na-image-masking-cha

Aladdin Persson viedeo about UNET: https://youtu.be/oLvmLJkmXuc

In this video I build the UNET neural network architecture and use it for image segmentation in the Carvana Kaggle competition. Unfortunately, I couldn't train the model due to some tensorflow bugs, but I'll try to solve them and show you the result in an upcoming video. If you have any recommendation or advice leave it in the comments and I will gladly read it.

Video sections:
00:00 Intro
02:03 Start coding
04:25 Problems and bugs
05:24 Building nn architecture
09:24 More problems and bugs (same)

I hope you've liked the content. If so, remember to leave a like, comment and subscribe to the channel so you don't miss any video.
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In this video I show you how to calculate the mean and std across multiple channels of the data you're working with which you will normally then use for normalization to obtain a 0 mean and standard deviation (std) of 1.


People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx
DL Specialization (affiliate): https://bit.ly/30npNrw
ML Course (no affiliate): https://bit.ly/3t8JqA9
DL Specialization (no affiliate): https://bit.ly/3t8JqA9

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This video explains Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning & Computer Vision for beginners. This is the first video of our tutorial series about Artificial intelligence for beginners that gives a basic introduction to the field of AI and Computer Vision.

The first use of the phrase “Artificial Intelligence”: https://www.aaai.org/ojs/index.....php/aimagazine/arti

✅Artificial Intelligence, or AI, is the science and engineering of making machines learn, think, and act like humans.
✅Machine Learning is a sub-field of AI where machines learn directly from data instead of hand-coded rules.
✅Deep Learning is a sub-field of Machine Learning where machines learn using Deep Neural Networks.
✅And finally, Computer Vision is the science and engineering of interpreting visual data. Many Computer Vision problems are solved using AI, but many others are not.

❓FAQ
What are artificial intelligence, machine learning, and deep learning?
What are artificial intelligence and machine vision?
What is artificial intelligence in machine learning?
Is computer vision artificial intelligence or machine learning?

⭐️Time Stamps⭐️
0:00-0:34 : Introduction
0:34-0:46 : What is Artificial Intelligence
0:46-1:11 : Early AI techniques
1:11-1:28 : What is Machine Learning
1:28-2:31 : What is Neural Network
2:31-2:38 : What is Deep Learning
2:38-3:49 : What is Computer Vision
3:49-4:46 : Summary

🖥️ On our blog - https://learnopencv.com we also share tutorials and code on topics like Image Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow.

🤖 Learn from the experts on AI: Computer Vision and AI Courses
YOU have an opportunity to join the over 5300+ (and counting) researchers, engineers, and students that have benefited from these courses and take your knowledge of computer vision, AI, and deep learning to the next level.🤖
https://opencv.org/courses

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Deep Learning NVR DVA3219 is an AI-powered video analytics server perfect for those who are concerned about privacy and security.

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This video explains the Transformer architecture in a very detailed way, including most math formulas in the paper, and the neural network operations behind it. The Transformer is the foundation of many powerful language models like BERT, GPT3, RoBERTa, XLNET, ELECTRA, T5. Understanding how it works in detail might help you modify, optimize, or improve it in the way you want.


Connect
Linkedin https://www.linkedin.com/in/xue-yong-fu-955723a6/
Twitter https://twitter.com/home
Email edwindeeplearning@gmail.com

0:00 - Intro
1:10 - Architecture overview
1:56 - Encoder
3:58 - Residual connection & layer normalization
6:59 - Decoder
11:14 - Attention mechanism
14:30 - Scaled dot-product attention
20:22 - Learned projection layers
26:32 - Multi-head attention
28-39 - Encoder-decoder attention
31:18 - Encoder self-attention
31:34 - Decoder self-attention
33:58 - Position-wise feedforward network
36:41 - Word embedding
39:34 - Positional encoding
47:37 - Why self-attention

What Is GPT-3 Series
https://www.youtube.com/playli....st?list=PLoS8jSwcU-c

Paper: Attention Is All You Need
https://arxiv.org/abs/1706.03762

Abstract
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best
performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English- to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.