fudo (@0xfud0) - Twitter Profili | Zamantika Mersobahis Locabet

fudo@0xfud0·8h

Day 163 of my AI Journey. Deep Learning Specialization. Course 4 : CNNs, Module 3 : Object Detection Lab : Autonomous Driving Application Car Detection. - Implemented YOLO Non-max Suppression ✅ - YOLO Pretrained model on Images✅

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fudo@0xfud0·16h

@Pseudo_Sid26 Same with me I saw some of his videos and I read few pages of his book. I loved the way of his teaching and explanations in his book. I will complete his videos and book atleast one time.

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Siddharth@Pseudo_Sid26·19h

Joseph Blitzstein has inspired me so much. I haven't read his whole book, but I've watched many of his lectures and he's genuinely one of the most fun people to learn probability and stats from. He makes hard concepts -feel intuitive -keeps things engaging -teaches with a clarity that just clicks. If you're into stats/probability and haven't checked out his Harvard Stat 110 lectures, you're missing out.

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fudo@0xfud0·16h

Again I'm telling you. If you want to have strong foundations in math for AI/ML. Start with them. Linear Algebra : Strang's MIT 18.06 videos and his Introduction to Linear Algebra book. Calculus : Professor Leonard videos and reference book James Stewart Calculus Probability : Joseph Blitzstein's Harvard Stat 110 videos and his book Introduction to probability.

Siddharth@Pseudo_Sid26

Joseph Blitzstein has inspired me so much. I haven't read his whole book, but I've watched many of his lectures and he's genuinely one of the most fun people to learn probability and stats from. He makes hard concepts -feel intuitive -keeps things engaging -teaches with a clarity that just clicks. If you're into stats/probability and haven't checked out his Harvard Stat 110 lectures, you're missing out.

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fudo@0xfud0·1d

This is the first module in the specialization that I found somewhat challenging. Not because of the mathematics, but due to the complexity of the architectures. ChatGPT helped me understand many of these complex concepts clearly.

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fudo@0xfud0·1d

Day 162 of my AI Journey. Deep Learning Specialization. Course 4 : CNNs, Module 3 : Object Detection - Intersection Over Union (IOU) ✅ -Non max Suppression ✅ -Anchor Boxes ✅ -YOLO Algorithm. ✅ -Revised the notes and clarified some doubts Will Do YOLO Lab tomorrow.

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fudo@0xfud0·1d

@Rahulzz07 Yes. I'm using the free trail and we have access to every video of the course but not labs. But labs are also freely available on GitHub

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Rahul@Rahulzz07·1d

@0xfud0 Are you using the free trial . If so how will u practice the ones. Does the course contains practice section also??

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fudo@0xfud0·2d

Day 161 of my AI Journey. Deep Learning Specialization. Course 4 : CNNs, Module 3 : Object Detection -Object Localization✅ -Landmark Detection✅ -Sliding windows with convolution✅ -Bounding Box predictions✅ Transfer Learning with mobile NET(module 2)✅

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fudo@0xfud0·1d

@athrvam_ Thank you. Keep going

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Athrvām@athrvam_·1d

@0xfud0 well done! Trying to do same here!👏

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fudo@0xfud0·2d

@herooffjustice atb hero

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Hero Of Justice@herooffjustice·2d

today's the last exam, I will get back to my studies and project work from tomorrow 👊 Have a beautiful day y'all 🤍✨✨

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fudo@0xfud0·3d

Day 160 of my AI Journey. Deep Learning Specialization. Course 4 : CNNs, Module 2 : Deep CNNs Case Studies. -EfficientNet ✅ -Transfer Learning and Data Augmentation in CNNs ✅ -Implemented ResNet Lab ✅

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fudo@0xfud0·4d

@Asura1612913 Keep going. Be strong in that module as every concept you see in module 2 is built on top of those foundations like filters, stride, padding etc atb

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Asura@Asura1612913·4d

@0xfud0 Haha, I am also on the same spot in this course but on module 1 I need to complete this as well.

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fudo@0xfud0·4d

Day 159 of my AI Journey. Deep Learning Specialization. Course 4 : CNNs, Module 2 : Deep CNNs Case Studies. Understood the architectures: -LeNET, AlexNet, VGG✅ -ResNet✅ -Inception Networks✅ -MobileNet✅ Ng did an excellent job explaining these concepts intuitively.

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fudo@0xfud0·28 Mar

@Pseudo_Sid26 Agreed 💯 Math → ML → AI. ML isn’t mandatory, but it’s the layer that makes everything in AI actually make sense instead of feeling like magic.

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Siddharth@Pseudo_Sid26·27 Mar

ML is such a fundamental. Like even if you are into AI or agents and stuff, if you know core ML stuff you would enjoy and understand stuff much faster. Obviously without ML too, you can deep dive into AI engineering but with ML, its just the perfect circle.

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fudo@0xfud0·24 Mar

Day 158 of my AI Journey. Deep Learning Specialization. Course 4 : CNNs, Module 1 : Foundations of CNN Lab: Convolution Model Application ✅ Revised Linear Algebra (soon will resume ). Will start Module 2 (Deep Convolution Models) from tomorrow.

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fudo@0xfud0·23 Mar

@gdavlin Okay. yes it's a unix time stamp but it is very special for this app

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Garry Davlin@gdavlin·23 Mar

@0xfud0 It's about time someone answered.

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fudo@0xfud0·23 Mar

How many of you know what this number means? 1288834974657

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fudo@0xfud0·23 Mar

@nikku762 ChatGPT also says CNNs and Sequence (course 4 and 5 in this specialization) will take time. Excited.

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Nikhil | Data Analytics@nikku762·23 Mar

@0xfud0 nicee 🔥 CNN basics done, now real fun starts 😄

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fudo@0xfud0·22 Mar

Day 157 of my AI Journey. Deep Learning Speciaization. Course 4 : CNNs, Module 1 : Foundations of CNNs -Simple CNN and Example✅ -Pooling Layers✅ Lab : Convolution model step by step ✅