Renzym Education

Renzym Education
We intend to upload videos covering various courses as well as tutorials on applying theory in practice. Most of these videos are collaborative work of Abasyn University Islamabad Campus and RENZYM.

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Abasyn Islamabad: http://www.abasynisb.edu.pk

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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
1×1 convolution, Inception/GoogLeNet, Transfer learning, Machine Learning Lec 25/30 [Urdu]
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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]
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