News

News

Social Feeds

Facebook

This message is only visible to admins.
Problem displaying Facebook posts.
Click to show error
Error: Server configuration issue

Youtube

This test demonstrates electronic steering working. Beam is configured to be electronically steering from -30, -15, 0, 0, 15, 30 degrees. As plane moves past these directions it is detected in respective bins. Center two bins are both actually referring to 0 degrees in this test.
Phased Array Demo
ScanX: Document Scanner with OCR<br />Key features: Auto-detection of documents, Scanning, Save and Share as Image or PDF, OCR in six major languages, Smart Editing showing original while you correct errors, Listen to extracted text
ScanX: Document Scanner with OCR
Renzym Real-Time Face Recognition Demonstration (Watch in HD for better results):<br /><br />Face Recognition code running on GPU to give real-time performance. Algorithm uses convolutional neural network (CNN) trained on Renzym Team Member's faces. There can be several applications such Office/Class Attendance, Automatic Door opening/closing for house members, Identifying Criminals from Surveillance setups (at traffic signals, airports) etc.
Face Recognition Demo
Software Save 70% time by using automatic form processing instead of processing manually. Our state of the art handwritten character recognition engine automates Data Entry process. There can be many use cases e.g. courier services, hajj application processing, census forms processing etc.<br /><br />www.renzym.com
Automatic form processing demo
https://renzym.com/renzym-products/sdr-communication-kit.html<br /><br />SDR communication kit (SCK) is a USB based plug and play device, designed for organizations and communication labs involved in the study, research and development of communication systems especially software defined radios. It can be used to readily build real-time communication systems by directly applying the concepts learnt from theory with a minimum of implementation effort in the hardware. One of its main advantages is that it provides direct interface, through USB port, with powerful simulation and analysis tools like Matlab/Simulink, Lab-View, GNU Radio etc. Computational and visualization capabilities offered by these tools can be used to design and analyze communication systems directly from simulations. This blending of theory and practice makes SCK an ideal candidate for analog/digital communication labs because this aspect is generally missing from most of the communication courses. Theoretical performance of various techniques can now be quickly compared with their performance in a real-world environment. Furthermore it provides a unique opportunity to the researchers working on receiver design to verify their algorithms in various practical scenarios.
SDR Communication Kit
Tired of irrelevant tweets/facebook posts, follow your favorite political parties under one Android App. Election fever is starting.<br /><br />https://play.google.com/store/apps/details?id=com.renzym.elections
Chatter.pk: Election fever is ON
Scan & Scribe can convert your Handwritten and Printed Documents, Scribbled Notes, Whiteboard Brain Storming Sessions into Editable text that you can Copy, Save, Listen or Share. Its standout feature is its separate offline text recognition engines for Handwritten and Printed texts.<br />App Link: https://itunes.apple.com/us/app/scan-scribe/id1003178723?mt=8
Scan & Scribe iOS App – Promo Video
Scan & Scribe can convert your Handwritten and Printed Documents, Scribbled Notes, Whiteboard Brain Storming Sessions into Editable text that you can Copy, Save, Listen or Share. Its standout feature is its separate offline text recognition engines for Handwritten and Printed texts.<br />App Link: https://play.google.com/store/apps/details?id=com.renzym.scanscribe
Scan & Scribe Android App – Promo Video
Subscribe
This error message is only visible to WordPress admins

Error 403: Requests from referer are blocked..

Domain code: global
Reason code: forbidden

Twitter

renzyminc Follow

@ ·
now

Reply on Twitter Retweet on Twitter Like on Twitter Twitter
Load More

Youtube

#processor #verilog #computerarchitecture #processordesign<br />A processor 101 intro. Gives a basic idea of assembly language, machine code and processor design from scratch.<br /><br />Processor code: https://github.com/yasir-javed/simple_processor<br />Urdu video: https://www.youtube.com/watch?v=Pkq9S6vCsm0<br />Verilog video: https://www.youtube.com/watch?v=nblGw37Fv8A<br />iverilog installation: https://www.youtube.com/watch?v=Y0bNVStZok4<br />BRAM video: https://www.youtube.com/watch?v=bJ0smcbfCY0<br />RISC-V Assembly book: https://github.com/johnwinans/rvalp<br /><br />SUBSCRIBE! Also Enable Notifications by clicking bell button on channel page<br />https://www.youtube.com/c/RenzymEducation?sub_confirmation=1<br /><br />CONTENT<br />(0:00) Intro<br />(0:55) Building blocks and Verilog<br />(8:44) How processor works<br />(10:59) Instruction set<br />(15:33) Processor hardware design<br />(23:14) Verilog code<br />(27:48) Processor code and demo<br />(35:05) Making processor fancy<br />(43:08) RISC-V instruction set
Processor design for dummies [English]
#python #pythontutorial #numpy #machinelearning #jupyternotebook <br /><br />Python quick tutorial, covers python basics in first 1.5 hrs. <br />Numpy, plotting and working with images and files in next 1 hour<br /><br />Machine learning in 5 Hrs: https://www.youtube.com/watch?v=HGYWEOiQgWM<br /><br />Stanford Python tutorial link (cs231n): https://cs231n.github.io/python-numpy-tutorial/<br />Google Colab intro: https://www.youtube.com/watch?v=inN8seMm7UI<br /><br />SUBSCRIBE! Also Enable Notifications by clicking bell button on channel page<br />https://www.youtube.com/c/RenzymEducation?sub_confirmation=1<br /><br />(0:00) Interpreted vs Compiled languages<br />(1:30) Ubuntu/WSL example<br />(8:10) Spyder/Anaconda example<br />(10:51) Jupyter Notebook overview<br />(19:53) Programming languages overview<br />(32:00) Google colab intro<br />(38:17) Python/Jupyter tutorial<br />(43:00) Data types and operations, int, bool, string<br />(53:54) Lists<br />(57:16) Slicing<br />(1:01:13) For Loops<br />(1:04:59) List comprehensions<br />(1:07:43) Dictionaries<br />(1:17:12) Sets<br />(1:22:42) Tuples<br />(1:29:28) Functions and if/else<br />(1:35:05) Classes<br />(1:39:13) Numpy<br />(1:49:22) Numpy Array indexing<br />(2:05:30) Numpy Array maths<br />(2:12:48) Broadcasting<br />(2:19:28) Plotting: Matplotlib<br />(2:24:13) Read/Display Images<br />(2:26:42) Reading/Writing text files<br />(2:29:13) Modules
Python/Numpy/Jupyter in 2 hrs
(0:00) Machine learning intro <br />(1:58) Intro: Supervised Learning<br />(5:02) Intro: Unsupervised learning<br />(6:33) Linear regression: Model representation<br />(16:24) Linear regression: Cost function<br />(23:47) Gradient descent<br />(32:57) Briefest overview of matrices<br />(35:07) Multiple features<br />(42:06) Feature scaling, Mean normalization<br />(46:53) Learning rate<br />(49:27) Polynomial regression<br />(53:23) Normal equation<br />(59:22) Logistic regression/Classification<br />(1:14:30) Multi-class classification<br />(1:17:20) Regularization: Problem of overfitting<br />(1:26:33) Non-linear hypothesis<br />(1:30:22) Neurons and Model representation<br />(1:44:19) Multi-classclassification<br />(1:45:43) Neural network Cost function<br />(1:47:36) Back propagation<br />(1:59:31) Gradient checking, random initialization<br />(2:04:48) Machine learning advice: Deciding what to do next<br />(2:06:51) Evaluating a hypothsis<br />(2:08:56) Model selection and training/validation/test sets<br />(2:11:00) Diagnosing bias vs variance, Regulrization<br />(2:14:51) Learning curves<br />(2:18:31) Spam classification example<br />(2:23:35) Error Analysis<br />(2:29:22) Error metric for skewed classes, precision, recall, F-score<br />(2:34:31) Data for machine learning<br />(2:36:15) Fashion MNIST Tensor Flow example<br />(2:40:14) SVM - Support vector machines<br />(2:44:20) SVM: Maths behind large margin classifier<br />(2:51:34) SVM: Kernels<br />(3:04:11) Clustering<br />(3:10:50) Dimensionality reduction: Principale Component Analysis PCA<br />(3:26:22) Anomaly Detection<br />(3:46:30) Recommender Systems<br />(3:58:49) Large datasets, Stochastic/Mini-batch gradient descent, Map-reduce<br />(4:07:20) OCR example, Sliding window, Ceiling analysis<br />(4:17:49) Decision trees, Tree ensembles, Random Forests<br />(4:36:09) Convolutional Neural Networks CNNs<br />(4:45:20) CNN case studeies: LeNet-5, AlexNet, VGG-16, Resnet, Inception<br />(4:52:40) Object Detection and Yolo quick overview<br />(4:57:24) Face recognition/verification, <br /><br />Complete Course (at slower pace) playlist: https://www.youtube.com/watch?v=qUILAy-I204&list=PLoVsjlwzzlUT74osi5McUu7H80L6X6nzB<br /><br />Course Material (Slides/Assignments): https://tinyurl.com/ml-renzym<br /><br />SUBSCRIBE! Also Enable Notifications by clicking bell button on channel page<br />https://www.youtube.com/c/RenzymEducation?sub_confirmation=1
Machine Learning in 5 hrs [Urdu/Hindi]
(0:00) SVM: Maths behind large margin classifier<br />(5:31) SVM: Kernels<br />(18:13) Clustering<br />(24:53) Dimensionality reduction: Principale Component Analysis PCA<br />(40:26) Anomaly Detection<br />(1:00:30) Recommender Systems<br />(1:13:35) Large datasets, Stochastic/Mini-batch gradient descent, Map-reduce<br />(1:22:07) OCR example, Sliding window, Ceiling analysis<br />(1:32:35) Decision trees, Tree ensembles, Random Forests<br />(1:50:55) Convolutional Neural Networks CNNs<br />(2:00:07) CNN case studeies: LeNet-5, AlexNet, VGG-16, Resnet, Inception<br />(2:07:15) Object Detection and Yolo quick overview<br />(2:12:11) Face recognition/verification, <br /><br /> <br />Complete playlist: https://www.youtube.com/watch?v=qUILAy-I204&list=PLoVsjlwzzlUT74osi5McUu7H80L6X6nzB<br /><br />Course Material (Slides/Assignments): https://tinyurl.com/ml-renzym<br /><br />SUBSCRIBE! Also Enable Notifications by clicking bell button on channel page<br />https://www.youtube.com/c/RenzymEducation?sub_confirmation=1
Course Review 3/3, SVM, Clustering,PCA, Anomaly, Recommender,CNNs, Machine Learning Lec 30/30 [Urdu]
(0:00) Non-linear hypothesis<br />(4:21) Neurons and Model representation<br />(18:17) Multi-classclassification<br />(19:44) Neural network Cost function<br />(21:54) Back propagation<br />(33:30) Gradient checking, random initialization<br />(38:48) Machine learning advice<br />(40:50) Evaluating a hypothsis<br />(42:53) Model selection and training/validation/test sets<br />(45:00) Diagnosing bias vs variance, Regulrization<br />(48:51) Learning curves<br />(53:29) Spam classification example<br />(57:38) Error Analysis<br />(1:03:22) Error metric for skewed classes, precision, recall, F-score<br />(1:08:31) Data for machine learning<br />(1:10:13) Fashion MNIST Tensor Flow example<br />(1:14:13) SVM - Support vector machines<br />(1:18:17) Math behind large margin classifier<br /> <br />Complete playlist: https://www.youtube.com/watch?v=qUILAy-I204&list=PLoVsjlwzzlUT74osi5McUu7H80L6X6nzB<br /><br />Course Material (Slides/Assignments): https://tinyurl.com/ml-renzym<br /><br />SUBSCRIBE! Also Enable Notifications by clicking bell button on channel page<br />https://www.youtube.com/c/RenzymEducation?sub_confirmation=1
Neural networks, Error Analysis, F-score, SVM ,Course Review 2/3, Machine Learning Lec 29/30 [Urdu]
(0:00) What is machine learning<br />(1:58) Supervised Learning<br />(6:33) Model representation<br />(24:07) Gradient descent<br />(33:18) Briefest overview of matrices<br />(35:28) Multiple features<br />(42:22) Feature scaling, Mean normalization<br />(47:06) Learning rate<br />(49:51) Polynomial regression<br />(53:43) Normal equation<br />(59:42) Logistic regression/Classification<br />(1:15:03) Multi-class classification<br />(1:17:40) Regularization: Problem of overfitting<br /><br /> <br />Complete playlist: https://www.youtube.com/watch?v=qUILAy-I204&list=PLoVsjlwzzlUT74osi5McUu7H80L6X6nzB<br /><br />Course Material (Slides/Assignments): https://tinyurl.com/ml-renzym<br /><br />SUBSCRIBE! Also Enable Notifications by clicking bell button on channel page<br />https://www.youtube.com/c/RenzymEducation?sub_confirmation=1
Course Review 1/3, Linear and Logistic regression, Regularization, Machine Learning Lec 28/30 [Urdu]
(0:00) Face recognition intro<br />(4:40) One-shot learning<br />(9:58) Siamese Network<br />(14:00) Triplet loss<br />(26:42) Face verification and binary classification<br />(33:04) Neural style transfer<br />(35:15) What are deep ConvNets learning<br />(50:13) Neural style transfer cost function<br />(52:19) Content cost function<br />(54:16) Style cost function<br />(1:04:52) 1D and 3D generalzation of models<br /> <br />Complete playlist: https://www.youtube.com/watch?v=qUILAy-I204&list=PLoVsjlwzzlUT74osi5McUu7H80L6X6nzB<br /><br />Course Material (Slides/Assignments): https://tinyurl.com/ml-renzym<br /><br />SUBSCRIBE! Also Enable Notifications by clicking bell button on channel page<br />https://www.youtube.com/c/RenzymEducation?sub_confirmation=1
Face recognition/verification, FaceNet, DeepFace, Style transfer, Machine Learning Lec 27/30 [Urdu]
(0:00) Review: 1x1 convolution<br />(1:30) Object localization<br />(20:55) Landmark detection<br />(29:00) Skippable<br />(34:24) Object detection<br />(38:14) Convolutional implementation of sliding windows<br />(51:31) Yolo algorithm, Bounding box prediction.<br />(1:02:31) Intersection over union (IoU)<br />(1:04:00) Non-max suppression<br />(1:12:08) Anchor boxes<br />(1:16:22) Putting is together: YOLO algorithm<br /> <br />Complete playlist: https://www.youtube.com/watch?v=qUILAy-I204&list=PLoVsjlwzzlUT74osi5McUu7H80L6X6nzB<br /><br />Course Material (Slides/Assignments): https://tinyurl.com/ml-renzym<br /><br />SUBSCRIBE! Also Enable Notifications by clicking bell button on channel page<br />https://www.youtube.com/c/RenzymEducation?sub_confirmation=1
YOLO algorithm, Object detection, Non-Max suppression, Anchor box, Machine Learning Lec 26/30 [Urdu]
(0:00) Review: LeNet-5, ResNets, AlexNet, VGG<br />(4:57) 1x1 convolution: Network in Network<br />(15:06) Inception network motivation<br />(29:39) Inception network<br />(42:30) Transfer learning<br />(53:47) Data augmentatioon<br />(1:01:26) Practical advice: State of computer vision<br />(1:13:58) Using open source implementations<br /> <br />Complete playlist: https://www.youtube.com/watch?v=qUILAy-I204&list=PLoVsjlwzzlUT74osi5McUu7H80L6X6nzB<br /><br />Course Material (Slides/Assignments): https://tinyurl.com/ml-renzym<br /><br />SUBSCRIBE! Also Enable Notifications by clicking bell button on channel page<br />https://www.youtube.com/c/RenzymEducation?sub_confirmation=1
1x1 convolution, Inception/GoogLeNet, Transfer learning, Machine Learning Lec 25/30 [Urdu]
Load More... Subscribe
This error message is only visible to WordPress admins

Error 403: Requests from referer are blocked..

Domain code: global
Reason code: forbidden

Facebook

This message is only visible to admins.
Problem displaying Facebook posts.
Click to show error
Error: Server configuration issue

Twitter