Showing posts from February, 2021

✍How Autonomous vehicles company uses neural network ?

Original Source Here ✍How Autonomous vehicles company uses neural network ? Artificial Neural Network ✍What are Neural Networks? Neural networks are a set of algorithms, they are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data. For Example Human Brain made is up of a network of neurons and also the brain is a very complex structure. Neural network It’s capable of quickly assessing and understanding the context of numerous different situations. Computers struggle to react to situations in a similar way. Artificial Neural Networks are a way of overcoming this limitation. First dev e loped in the 1940s Artificial Neural Networks attempt to simulate the way the brain operates. ✍Why we use Neural Networks? Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns

End to End Object Detection theory and implementation.*ogMR0Nd_Mep9eIZ2 Original Source Here End to End Object Detection theory and implementation. Introduction Computer vision has gained quite a prominence in the industry with the advent of GPUs. In particular object recognition, detection, segmentation plays a pivotal role in a self-driving car 🚘 , automated identification 👮‍♀️ , information retrieval. Sometimes for image classification one needs to first detect individual objects and pass it to some classifier. Over a period of time, different algorithms were proposed for object detection such as R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and many more. In this blog, we will primarily focus on these region-based algorithms, for YOLO one can see this blog. R-CNN The algorithm as proposed by Ross Girshick can be broadly divided int

Deep Learning Interview Questions

Original Source Here What are the steps of deep learning? 1. create a function (neural network) 2. evaluate the goodness of the function 3. pick the best function as the final question answer machine What is a neural network? Neural netw o rk simulates the ways human learn but is much simpler. It can be imagined as a function. When you feed it input, you are supposed to get an output. It commonly consists of an input layer, hidden layer(s), and an output layer. Reference: Hung-Yi Lee’s Lecture Slides Why is it necessary to introduce non-linearities in the neural network? If there are all linear functions, they actually compose a new linear function, which gives a linear model. A linear model has a much smaller number of parameters and is therefore limited in its complexity. What is the difference between single-layer perceptron and multi-layer perceptron? The main difference between them is the existence of hidden layers. Multi-layer perceptron can classify nonlinear

Working of Neural Network

Original Source Here What is Neural Network? Neural Network is set of algorithm which follows the working of human brain. It has dense cells called neurons which are helpful for system to learn things. For neural network we need at least 3 layers, Input layer, hidden layer and output layer. Hidden layer can be multiple on the basis of requirement or it’s accuracy. Working of Forward Neural Network Input Layer: In the above diagram there is 3 features x1, x2, and x3 which will help our model to train. For each feature th e re is weight associated with them w1, w2 and w3. A good neural network always have distinct weights. So question comes up Why we need weight? Answer: Weights decide how much input values will affect our output. It means that weight decide how fast activation function will work. For Deep understanding lets take a real life example, Suppose we pull 2 kg weight with left hand and 5 kg weight with right hand. In this process there are multiple neurons will wo


Original Source Here Google怎麼知道我不是機器人?機器人又要怎麼破解? 來源: 大家應該都看過這個框框,只要點選了就可以證明自己不是機器人。但大家應該也有類似的疑問:為什麼我點了就不是機器人?難道機器人就不會勾選嗎?今天我就被這個框框煩了好幾次,突然決定來認真查一下這是甚麼東西。 寫著寫著發現其實可以牽扯到蠻多深度學習的相關題目,因此會在各個章節把相關的議題都稍微列一下給大家玩玩看。 CAPTCHA 這個框框其實是Google開發的一種CAPTCHA系統。CAPTCHA的全名是Completely Automated Public Turing test to tell Computers and Humans Apart,就是用完全自動化的公開圖靈測試來區分人類跟電腦。 最早的CAPTCHA大家都有看過,就是「驗證碼」。驗證碼的英文字會設計得歪七扭八是為了讓機器人難以識別,而人類仍然能勉強看出來答案,用以區分人類與機器。之後更有reCAPTCHA方法,使用真實書籍上的文字來增加機器人的區分難度。 CAPTCHA OCR破解CAPTCHA OCR(Optical character recognition),光學文字識別。近年來受到深度學習的蓬勃發展已經十分強大,OCR破解CAPTCHA是理所當然的事情。深度學習大神LeCun Yann在深度學習領域中突破的MNIST資料集就是數字識別,同時MNIST也是深度學習第一課會拿來當範例的標準題目,破解CAPTCHA一點都不意外。 OCR最通用的實作方法是Detection+Classification: 先用Detection方法檢測可能是文字/字詞的框(ex: EAST ) CNN 預測文字:透過CNN網路,單獨預測每個文字框的文字內容 CRNN 預測字詞:透過CNN提取深度特徵,加上LSTM等RNN網路預測字詞。(ex: SEE ) 至於使用CNN還是CRNN,要看你的應用場景而定。以純數字驗證碼來說,前後數字的上下文關係可能不是很明顯,所以可以分開預測文字。但若是在預測英文單詞或文章的時候,使用CRNN類型方法可以有更好的效果。舉個例子, example

SMOTE: Synthetic Data Augmentation for Tabular Data

Original Source Here As can be seen in the previous image, the samples considered to generate synthetic samples are those that are in a low-density area. An alternative to ADASYN is K-Means-SMOTE which generates synthetic samples based on the density of each cluster found in the minority class. SMOTE in practice In this section, we will see the SMOTE [ 2 ] implementation and its variants ( Borderline-SMOTE [ 3 ] and ADASYN [ 4 ]) using the python library imbalanced-learn [1] . In order to make a comparison of each of these techniques, an unbalanced dataset will be generated using the module make_classification of the scikit-learn framework. Later, visualizations corresponding to each algorithm will be shown as well as the evaluation of each model under the accuracy , precision , recall and f1-score metrics. Therefore, let’s start with the generation of the dataset. Code Snippet 1. Data generation Code snippet 1 generates a 2000 sample dataset with only 2 features

NFNets Explained — DeepMind’s New State-Of-The-Art Image Classifier*BWz48FL_sMBnumFa Original Source Here NFNets Explained — DeepMind’s New State-Of-The-Art Image Classifier Is this the beginning of the end for Batch Normalization? Introduction DeepMind has recently released a new family of image classifiers that achieved a new state-of-the-art accuracy on the ImageNet dataset. This new family of image classifiers, named NFNets (short for Normalizer-Free Networks), achieves comparable accuracy to EfficientNet-B7, while having a whopping 8.7x faster train time. NFNet-F1 trains 8.7x faster than EfficientNet-B7, while achieving comparable accuracy. NFNet-F5 achieves state-of-the-art accuracy, surpassing previous accuracies of the EfficientNet family. This improvement in training speed was partly achieved by replacing batch normalization with other techniques. This represents an important paradigm shift in the world of image classifiers, which has relied heavily on batch normalization as a key comp

Axonius raises $100 million to protect IoT devices from cyberattack Original Source Here Join Transform 2021 for the most important themes in enterprise AI & Data. Learn more . Axonius , a cybersecurity startup developing an end-to-end device management platform, today announced that it raised $100 million in series D funding led by Stripes, valuing the company at over $1 billion post-money. Axonius says it’ll use the proceeds to scale growth globally and to expand its platform to meet market demand. By 2020,  Gartner predicts that there will be more than 20 billion connected devices globally — a number that has some executives worried. In a recent survey  conducted by Spiceworks, 90% of IT professionals expressed concern that the influx would create security and privacy issues in the workplace. And in a separate study commissioned by eSecurity Planet, 31% of internet of things (IoT) developers said they considered the software

Deep Dive into Neural Networks — Deep Learning for Practitioners; Part 2

Original Source Here Training and Evaluating the Network Now comes the interesting part — the learning of the network, we have defined the architecture of the neural network, compiled it with a loss function, an optimizer for the updating weight, and a metric you need to keep track of. For the learning of the network, we need to define when the training should end in this case; the number of epochs, we also define a batch_size to tell the machine after how many samples weight should be updated. We also input the training data samples and the training labels. >>>, train_labels, epochs=10, batch_size=256) Epoch 1/10 235/235 [==============================] - 1s 3ms/step - loss: 0.5015 - accuracy: 0.8563 Epoch 2/10 235/235 [==============================] - 1s 3ms/step - loss: 0.1406 - accuracy: 0.9576 Epoch 3/10 235/235 [==============================] - 1s 3ms/step - loss: 0.0895 - accuracy: 0.9737 Epoch 4/10 235/235 [============================

VSB Power Line Fault Detection

Original Source Here The signals a, b, and c in Figure 35 are smooth signals which are phase-shifted by a certain degree, while the signal d is a rough signal. The various type of fractals like Petrosian, Katz, and Detrended Fluctuation Analysis (DFA) calculated on the above signals indicate that: The difference between the fractal values is less between the signals a, b, and c. The difference between the fractal values is more between the signals a, b, c, and signal d. A similar kind of analysis is performed on the current medium voltage power line signals to measure the ‘roughness’ of the signal with and without the presence of the PD pattern. As mentioned above, the following fractal dimension values are obtained for each signal: Petrosian Fractal Dimension Katz Fractal Dimension Detrended Fluctuation Analysis (DFA) These fractal values are calculated on the DWT denoised signal. Petrosian Fractal Dimension Petrosian Fractal Dimension (FD) is used to provide a fas

Deep Learning for Practitioners — Glossary

Original Source Here This is a glossary of the topics covered in the Deep Learning for Practitioner Series. Continue reading on Medium » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress

Sensing robot healthcare helpers

Original Source Here Robots that could take on basic healthcare tasks to support the work of doctors and nurses may be the way of the future. Who knows, maybe a medical robot can prescribe your medicine someday? That’s the idea behind 3D structural-sensing robots being developed and tested at Simon Fraser University by Woo Soo Kim, associate professor in the School of Mechatronic Systems Engineering. “The recent pandemic demonstrates the need to minimize human-to-human interaction between healthcare workers and patients,” says Kim, who authored two recent papers on the subject — a perspective on the technology and a demonstration of a robots’ usefulness in healthcare. “There’s an opportunity for sensing robots to measure essential healthcare information on behalf of care providers in the future.” Kim’s research team programmed two robots, a humanoid figure and a robotic arm, to measure human physiological signals, working from Kim’s Additive Manufacturing Lab located in SFU Surre

The NLP Cypher | 02.28.21

Original Source Here Ok, if you own a KIA automobile please read this…👇 KIA was apparently hacked with ran s omware earlier this month, and the actors want to be paid in full. They are asking for a cool $21 million in BTC. KIA has denied the allegations it was ever hacked although they recently suffered network outages. Read more here . Consequences of the Hack 👀 “Kia’s key connected services remain offline, meaning customers are unable to pay their car loans, remotely start their vehicles, or other functions using Kia’s infrastructure.” — the drive blog A *PSEUDO* DALL-E Appears From the Ashes OpenAI opened the week with a pre-emptive strike w/r/t its DALL-E project by releasing *part* of the model. They released the image reconstruction *part* d-VAE. The actual encoder language model remains out of pocket, and without this, we can’t actually achieve what they demo’d in their paper. OpenAI’s CLIP Implementations: If you’re still interested in OpenAI’s CLIP, we found

M003: Kiyomizu-Dera Nostalgia

Original Source Here M003: Kiyomizu-Dera Nostalgia Exploring spatial imagination of AI Title: Kiyomizu-Dera Nostalgia Nr: 003 AI models in use: 3D Ken Burns / JukeBox Music (Premium) Image backstory: I took this photo in Kyoto Status: Unsold Owner: N.N. Platform: Exploring AI dreams with 3D Ken Burns. 3D spatial animation from a single photo (I took this photo in Kyoto, Kiyomizu-Dera Temple). Being there I had an intense nostalgic feeling. If there would be something like reincarnation, I was probably a resident of Kyoto in my last life. PREMIUM CONTENT: AI-created soundtrack (using JukeBox by OpenAI with Jazz as imagination target) AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress

CryptoMERZ: #NFT, #AI, and #ART

Original Source Here It’s time, my friends, to discover new frontiers. I opened a new blog, where I will present my NFT-Art. As you know, I am exploring new possibilities of new technologies in creative terrains. How does a machine imagine our world? What is human? Does AI can inspire us (SPOILER: yes it can). With various existing AI methods, we (AI and me) are trying to reconstruct reality in a new way. Feel free to subscribe to my blog: P.S. I want to thank Shortcut Art for great support, tips and first purchase. AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress

M002: Through the (w)all

Original Source Here Vladimir Alexeev. Futurist. AI-driven Dadaist. Living in Germany, loving Japan, AI, mysteries, books, and stuff. AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress

M001: Toriification

Original Source Here Vladimir Alexeev. Futurist. AI-driven Dadaist. Living in Germany, loving Japan, AI, mysteries, books, and stuff. AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress