Artificial Intelligence

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Electronics and Mechanical Engineering Expert Company: Hawk-Research Part-time, Contract, Freelance Salary: 20 - 120 $ per Hour Hawk Research is looking for Electronics and Mechanical/Civil Engineering expert to provide assistance on our projects in academic research sphere. We are building a knowledge sharing platform to help people during their studies, so they can improve their level in mentioned disciplines. We strive to help our clients facilitate learning and improve their performance through modern technology and knowledge-sharing services. We are looking for self-organized experts with specialization in Electronics/Electrical and Mechanical/Civil Engineering who can help us and our customers with various projects. Job Responsibilities Fulfilling various small projects related to Electronics Engineering, Electronics Design, Electric Power System design; if your tend to do Mechanical and Civil Engineering - work AutoCAD and Modelling Requirements / Qualifications (one of the mentioned or few): Electronics Engineering Electronics Design Electrical CAD Drafting/Designing Electric Power System design Civil or Mechanical Engineering Benefits/What We offer Flexible schedule Fully remote job Ability to combine this job with your main job or other projects Contacts: turuk@hawk-research.com More Remote IT Jobs on @remotejobshg 👇

7 Steps of the Machine Learning Process Data Collection: The process of extracting raw datasets for the machine learning task. This data can come from a variety of places, ranging from open-source online resources to paid crowdsourcing. The first step of the machine learning process is arguably the most important. If the data you collect is poor quality or irrelevant, then the model you train will be poor quality as well. Data Processing and Preparation: Once you’ve gathered the relevant data, you need to process it and make sure that it is in a usable format for training a machine learning model. This includes handling missing data, dealing with outliers, etc. Feature Engineering: Once you’ve collected and processed your dataset, you will likely need to transform some of the features (and sometimes even drop some features) in order to optimize how well a model can be trained on the data. Model Selection: Based on the dataset, you will choose which model architecture to use. This is one of the main tasks of industry engineers. Rather than attempting to come up with a completely novel model architecture, most tasks can be thoroughly performed with an existing architecture (or combination of model architectures). Model Training and Data Pipeline: After selecting the model architecture, you will create a data pipeline for training the model. This means creating a continuous stream of batched data observations to efficiently train the model. Since training can take a long time, you want your data pipeline to be as efficient as possible. Model Validation: After training the model for a sufficient amount of time, you will need to validate the model’s performance on a held-out portion of the overall dataset. This data needs to come from the same underlying distribution as the training dataset, but needs to be different data that the model has not seen before. Model Persistence: Finally, after training and validating the model’s performance, you need to be able to properly save the model weights and possibly push the model to production. This means setting up a process with which new users can easily use your pre-trained model to make predictions.

Do you know who are the Best Ai/Ml/Dl Teacher 👨‍🏫 in the India and in the World?? 🌏 Well Here is curated list 🎓👨‍🏫 1) Dr. R. Balasubramanian from IIT Roorkee https://www.iitr.ac.in/~CSE/Balasubramanian_R_ 2) Dr. Jawahar C. V. from IIIT Hyderabad https://www.iiit.ac.in/people/faculty/jawahar/ 3) Dr. Mausam from Indian Institute of Technology, Delhi https://www.cse.iitd.ac.in/~mausam/ 4) Dr. Amit Sethi from Indian Institute of Technology, Bombay https://www.ee.iitb.ac.in/~asethi/ 5) Dr. Balaraman Ravindran from Indian Institute of Technology, Madras http://www.cse.iitm.ac.in/profile.php?arg=MjE= Bonus • Pulak Ghosh from Indian Institute of Management, Bangalore https://www.iimb.ac.in/user/73/pulak-ghosh • Debdoot Sheet from Indian Institute of Technology, Kharagpur http://iitkgp.ac.in/department/EE/faculty/ee-debdoot • Mitesh M. Khapra from Indian Institute of Technology, Madras http://www.cse.iitm.ac.in/~miteshk/ Top Ai Teacher's in the World 🌍 1) Dr. Andrew Ng from Stanford University, https://en.m.wikipedia.org/wiki/Andrew_Ng https://online.stanford.edu/instructors/andrew-ng 2) Dr. Fei fei Li from Stanford University https://profiles.stanford.edu/fei-fei-li https://en.m.wikipedia.org/wiki/Fei-Fei_Li 3) Dr. Geoffrey Hinton from University of Toronto, http://www.cs.toronto.edu/~hinton/ https://en.m.wikipedia.org/wiki/Geoffrey_Hinton 4) Dr. Yoshua Bengio from University of Montreal, Canada https://en.m.wikipedia.org/wiki/Yoshua_Bengio 5) Dr. Michael Jordan from University of California, Berkeley, https://people.eecs.berkeley.edu/~jordan/ Bonus: • Dr. Ruslan Salakhutdinov from Carnegie Mellon University https://www.cs.cmu.edu/~rsalakhu/ • Jürgen Schmidhuber https://en.m.wikipedia.org/wiki/J%C3%BCrgen_Schmidhuber • Dr. Yann LeCun from New York University https://engineering.nyu.edu/faculty/yann-lecun

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Roadmap for 3 days Live Masterclass Day 1: Master Concepts of Python Day 2: Introduction to Python Libraries Day 3: Introduction to Machine learning and a Bonus Timings: 06:00 pM to 07:00 PM Takeaways of this Live Masterclass: Project Source Code LinkedIn shareable Certificate Link : https://codekaro.in/python-masterclass

Quick read to brush up on the mathematics required for Deep Learning. Terence Parr and Jeremy Howard written it and this is one of many great resources put together by the faculty in the MS in Data Science program at the University of San Francisco. Source linked here https://explained.ai/matrix-calculus/

10 Free Resources To Learn PyTorch In 2022 At the NeurIPS conference in 2019, PyTorch appeared in 166 papers, whereas TensorFlow appeared in 74 papers. Last year, NVIDIA GTC 2021 hosted over +50 different sessions related to PyTorch and Cheery on cake, Facebook and OpenAI has announced last year to migrate all its AI systems to PyTorch. PyTorch is developed by Facebook AI in 2016 since then it's one of the most popular library. Today PyTorch is one of the most widely used open-source machine learning libraries for wide deep learning applications. List of curated PyTorch resources: 1) PyTorch Official Tutorials. 2) Intro to Deep Learning with PyTorch by Facebook AI. 3) PyTorch Fundamentals By Microsoft 4) PyTorch - Python Deep Learning Neural Network API by Deeplizard. 5) Deep Neural Networks with PyTorch by Joseph Santarcangelo 6) PyTorch Basics for Machine Learning by IBM 7) Deep Learning with Python and PyTorch 8) Pytorch - Deep learning with Python by Harrison Kinsley, Sentdex 9) Make Your First GAN Using PyTorch 10) PyTorch Tutorials By Morvan Zhou Bonus: Deep Learning with PyTorch book📚 Visualization of above resources are available on Twitter!

Roadmap for 3 days Live Masterclass Day 1: Master Concepts of Python Day 2: Introduction to Python Libraries Day 3: Introduction to Machine learning and a Bonus Timings: 06:00 pM to 07:00 PM Takeaways of this Live Masterclass: Project Source Code LinkedIn shareable Certificate Link : https://codekaro.in/python-masterclass

Roadmap for 3 days Live Masterclass Day 1: Master Concepts of Python Day 2: Introduction to Python Libraries Day 3: Introduction to Machine learning and a Bonus Timings: 06:00 pM to 07:00 PM Takeaways of this Live Masterclass: Project Source Code LinkedIn shareable Certificate Link : https://codekaro.in/python-masterclass

Neural Networks and Deep Learning, a free online book. The book will teach you about: * Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data * Deep learning, a powerful set of techniques for learning in neural networks http://neuralnetworksanddeeplearning.com/index.html

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