Original Source Here Review — SoildNet: Soiling Degradation Detection in Autonomous Driving SoildNet for Soiling Detection , Formed by Dynamic Group Convolution From ResNeXt , and Channel Reordering From ShuffleNet V1 In this story, SoildNet: Soiling Degradation Detection in Autonomous Driving , (SoildNet), by Valeo India, is reviewed. Camera sensors are extremely prone to soiling such as rain drops, snow, dust, sand, mud and so on. In this paper: SoildNet ( S and, sn O w, ra I n/d I rt, oi L , D ust/mu D ) is proposed with the use of dynamic group convolution and channel reordering , make it suitable for low power embedded systems. Soiling is detected at tile level of size 64×64 on 1280×768 input image. Clean , opaque soiling and transparent soiling are classified. This is a paper in 2019 NeurIPS Workshop . ( Sik-Ho Tsang @ Medium) AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot vi

https://miro.medium.com/max/1200/0*w6WFKWNStVmorvX3 Original Source Here Prompt programming — Human communication Low-code and no-code initiatives appeared a few decades ago as a reaction to the increasingly large skill gap in the coding world. The technical ability to create good code and know how to handle tasks at different points in the design-production pipeline was expensive. As software products got more complex, so did the programming languages. No-code aims at solving this gap for non-technical business people. It’s an approach that bypasses coding to make the results accessible to anyone. Knowing how to code is arguably as important as speaking English was a few years ago. You either knew or you were missing a lot. Job opportunities, books and articles, papers, and other technical work… In the future, the percentage of smart houses — domotics — will increase. Technical software skills may be as important then as now is it knowing how to fix a pipe or a broken light.

Original Source Here D eep Learning Approach to Detect Banana Plant Diseases A system to identify Banana plant diseases at the early stages and to be aware of the diseases. Types of diseases Introduction Hello folks This is my final year research project based on deep learning. Let me give an introduction about my project first. When we talk about banana it’s a famous fruit that commonly available across the world, because it instantly boosts your energy. Bananas are one most consumed fruit in the world. According to modern calculations, Bananas are grown in around 107 countries since it makes a difference to lower blood pressure and to reduce the chance of cancer and asthma. One of the problem s that occur with banana cultivation is the number of diseases affecting the entire crop. To prevent such diseases most of the farmers use integrated chemicals and cultural methods. Targeted Diseases to Identify Banana plant diseases have become a severe problem in all over

Original Source Here In simple words, Copilot r eally understands what you want to code in the next line. In my case, it even understands bad comments perfectly. Sometimes, it makes a few silly mistakes like declaring the same variable repeatedly; these kinds of bugs were already expected, which is why Github initially gave developers access to give their feedback. I was quite surprised by its ability to understand. You just have to feed it with a few examples and the rest will generate itself perfectly. So far, there are 3 main features I would like to mention: Convert comments to code. Github Copilot can understands bad comments and function names. 2. Tests without the toil. This is the only feature I would recommend you all to use in Github Copilot. 3. Autofill for repetitive code. Github Copilot is the more advanced form of already available autosuggestion plugins. Which is obviously good for experienced developers, but not for newbies. But Github Copilot doe

https://venturebeat.com/wp-content/uploads/2020/12/texting.GettyImages-1205077112.jpg?w=1200&strip=all Original Source Here All the sessions from Transform 2021 are available on-demand now. Watch now. Can the tech world put food on the table? Not just for the programmers, but for the users too? That’s the question that a startup called Invisibly is asking. They’re developing a way for users — that is, all of us on the internet — to earn a paycheck for sharing data. Instead of trading their personal information for services like Facebook or Google, Invisibly is proposing a more explicit contract that rewards the people with cash for parting with the information about what they like to read on the internet. The trade-offs of the traditional data-driven business model are always cloudy. The users get free access to, say, discussion boards for talking with their friends, search engines, or maybe some basic office apps. In return, the services track everything we do and sel

Original Source Here Around 2017 Attention and beyond! Attention in Language models(LM) by Vaswani et al. (2017), introduced a way of capturing this context in language that outperformed some previous SOTA benchmarks in various downstream NLP tasks. Since then the language models have grown significantly in size. For context (all are transformer-based by the way): BERT-base (Transformer Encoder) has ~110M parameters GPT-1 (Transformer Decoder) has ~117M parameters BERT-large has ~340M parameters GPT-2 has ~1.5B parameters GPT-3 has ~175B parameters The pre-training objective of some of these large pre-trained language models is to predict the next word or next sentence. This turns out to be a good pre-training objective to understand complex word interactions and is useful for different downstream tasks like question answering. The datasets these models are trained on are non-trivial; for example BERT was trained on Wikipedia (2.5B words) + BookCorpus (800M words),

Original Source Here Abstract The Industrial Internet of Things is crucial for enterprise and country to drive the strategic upgrade and raise the level of national intelligent manufacturing. When pondering the IIoT industry evaluation, the corresponding dominating issues involve numerous indeterminacies. Spherical fuzzy set, portrayed by memberships of positive, neutral and negative, is a more efficient methods of seizing indeterminacy. In this article, firstly, the fire-new spherical fuzzy score function is explored for solving some suspensive comparison issues. Moreover, the objective weight and combined weight are determined by Renyi entropy method and non-linear weighted comprehensive method, respectively. Later, the multi-criteria decision making method based on combined compromise solutionis developed under spherical fuzzy environment. Finally, the corresponding method is effectively validated by the issue of IIoT industry evaluation. The main characteristics of the presented

Original Source Here Darknet — A Neural Network Framework written in C and CUDA Source: https://pjreddie.com/darknet/ In this article, I’m going to introduce to you what is Darknet. Outcomes What is Darknet? What Darknet can do? How to build Darknet in Windows, Mac, and Ubuntu. About Darknet AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/07/31/darknet%e2%80%8a-%e2%80%8aa-neural-network-framework-written-in-c-and-cuda/

https://venturebeat.com/wp-content/uploads/2021/07/GettyImages-617375080.jpg?w=1200&strip=all Original Source Here All the sessions from Transform 2021 are available on-demand now. Watch now. Most AI vendors develop solutions that target broad use cases with large markets. This is because investors have shown they are only interested in a target market if it is worth several billion dollars. Therefore smaller markets have been excluded, and AI solution ideas designed for niche markets often die out and the companies behind them come to a standstill before they have the chance to see the light of day. Another side effect of the limited capital to build niche models is that AI vendors tend to build one model and market it to a large set of disparate users. For example, a company selling a vehicle detection system would normally build a single model to detect all types of vehicles across multiple use cases and geographies. An animal detection model typically would detect m

Original Source Here The Dangerous Dismissal of AI Edge Cases FACT: Edge cases are limitless and model performance cannot be predicted by traditional accuracy metrics. We’ve all done it. Deployed a model only to have it fail on some obscure situation and dismissing it as merely an edge case that was unlikely to occur in the first place and claim success as it is quickly trained out of the model. Problem solved? Not so fast. AI is reorganizing our world at a dizzying pace. The many innovations are absolutely remarkable but we are doing ourselves a disservice when we dismiss AI limitations as merely edge cases. This dismissal and failure to acknowledge the true nature and magnitude of edge cases puts our pace of AI adoption at risk, and thus the entire industry. Instead of dismissing such limitations as edge cases, we need to acknowledge their true nature so we can work towards actually addressing the problem. An edge case is something that will rarely occur in practice and w

Original Source Here A Better Way for Data Preprocessing: Pandas Pipe Efficient, organized, and elegant. Photo by Sigmund on Unsplash Real-life data is usually messy. It requires a lot of preprocessing to be ready for use. Pandas being one of the most-widely used data analysis and manipulation libraries offers several functions to preprocess the raw data. In this article, we will focus on one particular function that organizes multiple preprocessing… AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/07/31/a-better-way-for-data-preprocessing-pandas-pipe/

https://miro.medium.com/max/1200/0*qD_X46iwDGG526qf Original Source Here Why should you get certified? There are two reasons why you should attempt the exam. First, getting this certificate is a great incentive to learn TensorFlow. Secondly, it’s also an excellent opportunity to certify and showcase your skills. If you do not have an y previous experience with Machine Learning, then it might be better to learn about it first (use these example resources as a starter: 1 , 2 , 3 , 4 ), and then come back to tackle the exam. Resources I first read about the exam a year ago but only actively started pursuing it last Christmas. My initial plans were to do preparatory courses in the winter holidays, but in the end, I was too busy with university work that I had to delay it. In March, I began the preparations, and I used the following resources for it. 1. DeepLearning.AI TensorFlow Developer Professional Certificate Costs: ~50$ per month after seven free days. Or audit it free of

Original Source Here Supervised vs Unsupervised vs Reinforcement A rtificial intelligence, Machine learning and Neural Network are few buzzwords in today’s world. Every body knows about it or want to know about it. This will be the trend of things going to be in next decade which will rule the technology. We can understood from this only that machine learning products capable of predicting like human. They are not absolutely correct but probabilistically best in the given condition. In other words, we can say they can imitate like an experts. You can understand where are we going to be in the upcoming years… AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/07/31/types-of-learning-in-ai/

Original Source Here The image format and the library one uses for reading image may differ. img = imread(‘/content/tiger.jfif’) #'bulbul.jpg' img = resize(img, (224,224), preserve_range = True) img = np.expand_dims(img.transpose((2,0,1)),0) img /= 255.0 mean = np.array([0.485, 0.456, 0.406]).reshape((1,3,1,1)) std = np.array([0.229, 0.224, 0.225]).reshape((1,3,1,1)) img = (img — mean)/std inpimg = torch.from_numpy(img).to(‘cuda:0’, torch.float32) Compute Gradient Class Activation Maps out, acts = gcmodel(inpimg) acts = acts.detach().cpu() loss = nn.CrossEntropyLoss()(out,torch.from_numpy(np.array([600])).to(‘cuda:0’)) loss.backward() grads = gcmodel.get_act_grads().detach().cpu() pooled_grads = torch.mean(grads, dim=[0,2,3]).detach().cpu() for i in range(acts.shape[1]): acts[:,i,:,:] += pooled_grads[i] heatmap_j = torch.mean(acts, dim = 1).squeeze() heatmap_j_max = heatmap_j.max(axis = 0)[0] heatmap_j /= heatmap_j_max Now, the heatmap needs to be resized and colou

Original Source Here #@CB ; Vir jou my Woefwuif, altyd en verewig AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/07/31/cb-vir-jou-my-woefwuif-altyd-en-verewig/

Original Source Here Though I clapped my 50’s to support, I’d argue with some statements concerning a decentralized finance crypto world. First, if you’re not one of that chosen community insider, you’ll never be aware of the scheme meant to use crypto. As market traders say, “no one knows 100% price action, unless you’re a market owner yourself”, we can only predict the fact of forthcoming events of the Bitcoin era where the crypto is admittedly unstoppable. Second, none of piece of g l obal value exists without the ultimate control over this by state authorities through levies, duties, taxes or even predatory traits, such as, say, raider’s takeover in developing countries. As for crypto, most likely, here we’ve got an implementation of the old times wisdom — “If you can’t beat them, join them.” The government authorities have power to prohibit or to profoundly take over the best of the technology, b/c the classical financial or banking system’s been existing since the times

Original Source Here Reading books is important because they books you endless knowledge. If you plan to become a Data Scientist or Machine Learning Engineer, reading machine learning books will help you gain the knowledge and skills required in this field. In a simple definition, machine learning is known as the subset of AI that has the ability to automatically learn from the data without explicitly being programmed or assisted by domain expertise. Email spam and malware filtering, product recommendations, image recognition, automatic language translation — these are all examples of real-life machine learning applications. The Futu r e of Jobs Report 2020 predicts that artificial intelligence (of which machine learning is a subset) will create 12 million new jobs across 26 countries by 2025. There are many books available in the market to learn machine learning. In this article, I have compiled the 5 best machine learning books that will help you start building your care

Original Source Here Why TBC Is Poised To Be Bullish Amid Crypto Market Dip The recent cryptocurrency crash saw most cryptos drop by almost 50%, including Bitcoin. However, even with the drop in crypto prices, we at TeraBlock are pushing forward by integrating new technologies into the TeraBlock ecosystem and partnering with other platforms to improve usability and facilitate the TeraBlock token (TBC) adoption. This has included partnerships with UniFarm, Sheesha Finance and Binance Cloud. TBC Outlook The recent crypto crash has put many investors on edge and highlighted the volatility of the crypto markets. However, this is not t he first time that the crypto markets have crashed. Bitcoin, for instance, has lost 80% of its value in numerous cases in the past and consistently recovered. In 2018, Ethereum lost nearly 95% of its value in an unprecedented crypto crash but has since recovered. Therefore, the recent drop presents an opportunity for traders to go long on crypto

Original Source Here Abstract The main task of local rough set model is to avoid the interference of complicated calculation and invalid information in the formation of approximation space. In this paper, we first present a local rough set model based on dominance relation to make the local rough set theory applicable to the ordered information system, then two kinds of local multigranulation rough set models in the ordered information system are constructed by extending the single granulation environment to a multigranulation case. Moreover, the updating processes of dynamic objects based on global (classical) and local multigranulation rough sets in the ordered information system are analyzed and compared carefully. It is addressed about how the rough approximation spaces of global multigranulation rough set and local multigranulation rough set change when the object set increase or decrease in an ordered information system. The relevant algorithms for updating approximations with

Original Source Here Scraping tweets using snscrape and building Sentiment classifier Extracting tweets using snscrape and sentiment analysis using Hugging Face Pipeline Recently, I worked with use cases where one of our clients needs to analyze their brand sentiment on Twitter. And, the timeline for the project is very tight as they are only looking for proof of concept. One of the… AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/07/31/scraping-tweets-using-snscrape-and-building-sentiment-classifier/

Original Source Here Photo by Benny on Dribbble Can you predict the success of a song, just by listening to it? I know! at least we try to do it many times, and a lot of times our predictions do turn out to be true. While we do consider many things and most importantly the emotions involved. Can we expect a deep learning model to predict that!?. Of course! In this article, we will figure out how though. IMPORTING LIBRARIES AND DATASET We will start with importing t h e necessary libraries and the dataset. For my model, I am using the dataset containing 90’s songs, though I’ll link datasets for different periods. All these songs in these datasets are taken from Spotify. After importing all the necessary stuff we will first check if our dataset is balanced or not. A balanced dataset is one that contains equal or near to equal value count for different classes of the prediction feature. Our dataset is already balanced!! PREPROCESSING Now, our dataset does contain som

Original Source Here First, we will extract faces from all our celebrity images using MTCNN and save the extracted face pixel arrays in uncompressed .npz format. Next, make sure to split celebrity images into train and validation folders. That’s it. The bollywood-data.npz contains a list of all faces that MTCNN detected in our celebrity dataset. Next, we will transform the extracted faces into face embedding using FaceNet. Finally, we will save all our face embeddings for into bollywood-embeddings.npz. After extracting face embeddings, we will build a classifier using Support Vector Machine to classify face embeddings to respective labels. We can choose any multi-class classifier, including building our Neutral Network. I choose Support Vector Machine due to its simplicity and popularity in other machine learning blogs for the face recognition step. We now evaluate our model using our test images. Finally, we will plot our results to have visual confirmation of SVM model per

Original Source Here I have embarked on a journey to learn deep learning. “For the things we have to learn before we can do them, we learn by doing them.” — Aristotle. In the last article, we learned about face detection using MTCNN. Now, w e will try to recognize the faces extracted by MTCNN. I have compiled a dataset of 30 Bollywood celebrities for our face recognition task. Every star has around ten training and ten validation images. FaceNet is a face recognition system developed in 2015 by Google researchers in a paper titled “ FaceNet: A Unified Embedding for Face Recognition and Clustering.” Given a picture of a face with input dimensions (160, 160, 3), FaceNet will extract high-quality features from the face and predict a 128 element vector representation called face embeddings. Then, we will use Support-Vector Machine to classify these face embeddings into respective Bollywood celebrities. Block diagram of our face recognition system First, we will extract faces fr

Original Source Here Review — EBGAN: Energy-Based Generative Adversarial Network (GAN) Using Autoencoder at Discriminator, Using Repelling Regularizer at Generator EBGAN: low energies to the regions near the data manifold and higher energies to other regions. (Figure from https://www.slideshare.net/MingukKang/ebgan ) In this story, Energy-based Generative Adversarial Network , (EBGAN), by New York University, and Facebook Artificial Intelligence Research, is briefly reviewed. In this paper: EBGAN views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Similar to the probabilistic GAN s, the generator is seen as being trained to produce contrastive samples with minimal energies , while the discriminator is trained to assign high energies to these generated samples. This is a paper in 2017 ICLR with over 1000 citations . ( Sik-Ho Tsang @ Medium) AI/ML Trending AI/M