Learn the basics of computer vision with deep learning and how to implement the algorithms using Tensorflow. Author: Folefac Martins from Neuralearn.ai More Courses: www.neuralearn.ai Link to Code: https://colab.research.google.com/drive/18u1KDx-9683iZNPxSDZ6dOv9319ZuEC_ YouTube Channel: https://www.youtube.com/@neuralearn ⭐️ Contents ⭐️ Introduction ⌨️ (0:00:00) Welcome ⌨️ (0:05:54) Prerequisite ⌨️ (0:06:11) What we shall Learn Tensors and Variables ⌨️ (0:12:12) Basics ⌨️ (0:19:26) Initialization and Casting ⌨️ (1:07:31) Indexing ⌨️ (1:16:15) Maths Operations ⌨️ (1:55:02) Linear Algebra Operations ⌨️ (2:56:21) Common TensorFlow Functions ⌨️ (3:50:15) Ragged Tensors ⌨️ (4:01:41) Sparse Tensors ⌨️ (4:04:23) String Tensors ⌨️ (4:07:45) Variables Building Neural Networks with TensorFlow [Car Price Prediction] ⌨️ (4:14:52) Task Understanding ⌨️ (4:19:47) Data Preparation ⌨️ (4:54:47) Linear Regression Model ⌨️ (5:10:18) Error Sanctioning ⌨️ (5:24:53) Training and Optimization ⌨️ (5:41:22) Performance Measurement ⌨️ (5:44:18) Validation and Testing ⌨️ (6:04:30) Corrective Measures Building Convolutional Neural Networks with TensorFlow [Malaria Diagnosis] ⌨️ (6:28:50) Task Understanding ⌨️ (6:37:40) Data Preparation ⌨️ (6:57:40) Data Visualization ⌨️ (7:00:20) Data Processing ⌨️ (7:08:50) How and Why ConvNets Work ⌨️ (7:56:15) Building Convnets with TensorFlow ⌨️ (8:02:39) Binary Crossentropy Loss ⌨️ (8:10:15) Training Convnets ⌨️ (8:23:33) Model Evaluation and Testing ⌨️ (8:29:15) Loading and Saving Models to Google Drive Building More Advanced Models in Teno Convolutional Neural Networks with TensorFlow [Malaria Diagnosis] ⌨️ (8:47:10) Functional API ⌨️ (9:03:48) Model Subclassing ⌨️ (9:19:05) Custom Layers Evaluating Classification Models [Malaria Diagnosis] ⌨️ (9:36:45) Precision, Recall and Accuracy ⌨️ (10:00:35) Confusion Matrix ⌨️ (10:10:10) ROC Plots Improving Model Performance [Malaria Diagnosis] ⌨️ (10:18:10) TensorFlow Callbacks ⌨️ (10:43:55) Learning Rate Scheduling ⌨️ (11:01:25) Model Checkpointing ⌨️ (11:09:25) Mitigating Overfitting and Underfitting Data Augmentation [Malaria Diagnosis] ⌨️ (11:38:50) Augmentation with tf.image and Keras Layers ⌨️ (12:38:00) Mixup Augmentation ⌨️ (12:56:35) Cutmix Augmentation ⌨️ (13:38:30) Data Augmentation with Albumentations Advanced TensorFlow Topics [Malaria Diagnosis] ⌨️ (13:58:35) Custom Loss and Metrics ⌨️ (14:18:30) Eager and Graph Modes ⌨️ (14:31:23) Custom Training Loops Tensorboard Integration [Malaria Diagnosis] ⌨️ (14:57:00) Data Logging ⌨️ (15:29:00) View Model Graphs ⌨️ (15:31:45) Hyperparameter Tuning ⌨️ (15:52:40) Profiling and Visualizations MLOps with Weights and Biases [Malaria Diagnosis] ⌨️ (16:00:35) Experiment Tracking ⌨️ (16:55:02) Hyperparameter Tuning ⌨️ (17:17:15) Dataset Versioning ⌨️ (18:00:23) Model Versioning Human Emotions Detection ⌨️ (18:16:55) Data Preparation ⌨️ (18:45:38) Modeling and Training ⌨️ (19:36:42) Data Augmentation ⌨️ (19:54:30) TensorFlow Records Modern Convolutional Neural Networks [Human Emotions Detection] ⌨️ (20:31:25) AlexNet ⌨️ (20:48:35) VGGNet ⌨️ (20:59:50) ResNet ⌨️ (21:34:07) Coding ResNet from Scratch ⌨️ (21:56:17) MobileNet ⌨️ (22:20:43) EfficientNet Transfer Learning [Human Emotions Detection] ⌨️ (22:38:15) Feature Extraction ⌨️ (23:02:25) Finetuning Understanding the Blackbox [Human Emotions Detection] ⌨️ (23:15:33) Visualizing Intermediate Layers ⌨️ (23:36:20) Gradcam method Transformers in Vision [Human Emotions Detection] ⌨️ (23:57:35) Understanding ViTs ⌨️ (24:51:17) Building ViTs from Scratch ⌨️ (25:42:39) FineTuning Huggingface ViT ⌨️ (26:05:52) Model Evaluation with Wandb Model Deployment [Human Emotions Detection] ⌨️ (26:27:13) Converting TensorFlow Model to Onnx format ⌨️ (26:52:26) Understanding Quantization ⌨️ (27:13:08) Practical Quantization of Onnx Model ⌨️ (27:22:01) Quantization Aware Training ⌨️ (27:39:55) Conversion to TensorFlow Lite ⌨️ (27:58:28) How APIs work ⌨️ (28:18:28) Building an API with FastAPI ⌨️ (29:39:10) Deploying API to the Cloud ⌨️ (29:51:35) Load Testing with Locust Object Detection with YOLO ⌨️ (30:05:29) Introduction to Object Detection ⌨️ (30:11:39) Understanding YOLO Algorithm ⌨️ (31:15:17) Dataset Preparation ⌨️ (31:58:27) YOLO Loss ⌨️ (33:02:58) Data Augmentation ⌨️ (33:27:33) Testing Image Generation ⌨️ (33:59:28) Introduction to Image Generation ⌨️ (34:03:18) Understanding Variational Autoencoders ⌨️ (34:20:46) VAE Training and Digit Generation ⌨️ (35:06:05) Latent Space Visualization ⌨️ (35:21:36) How GANs work ⌨️ (35:43:30) The GAN Loss ⌨️ (36:01:38) Improving GAN Training ⌨️ (36:25:02) Face Generation with GANs Conclusion ⌨️ (37:15:45) What's Next

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So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids

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Advancements in deep learning (especially invention of convolutional neural network or CNN or ConvNet) has made possible many amazing things in the field of computer vision. In this video we will be looking at application of deep learning and computer vision in following industries, 00:00 Overview of computer vision 00:26 Personal photo management 02:00 Banking (https://www.youtube.com/watch?v=-cGT_khFQsQ) 03:00 Agriculture (https://www.youtube.com/watch?v=oot55bK62pk) 04:47 Autonomus cars (https://www.youtube.com/watch?v=tlThdr3O5Qo) 06:25 Retail (Amazon Go) (https://www.youtube.com/watch?v=NrmMk1Myrxc) Do you want to learn technology from me? Check https://codebasics.io/ for my affordable video courses. ***This is NOT a sponsored video by any company (amazon, bank of america etc)*** #computervision #computervisionapplications #computervisiondeeplearning #deeplearningtutorial #whatiscomputervision Deep learning playlist: https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO Machine learning playlist : https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw   Google computer vision in healthcare: https://www.youtube.com/watch?v=Rj4Nwgk08Mc Website: https://codebasics.io/ Facebook: https://www.facebook.com/codebasicshub Twitter: https://twitter.com/codebasicshub Linkedin: https://www.linkedin.com/company/codebasics/ Discord: https://discord.gg/r42Kbuk DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

Have you ever wondered how self driving cars such as Tesla are able to navigate all kinds of roads with such ease, precision, and accuracy, essentially allowing individuals to sit back and relax in a car that has no human human operating it? How is a car able to see the road make sense out of the entities that it's seeing and maneuver accordingly? A car obviously doesn't have eyes like a human does, so how can it see things like pedestrians, stop and go lights, stop signs, road lines, other road signs, etc. Well, it turns out that cars do have eyes, but not in the form that we're familiar with. Computer vision is the field of artificial intelligence that gives machines the ability to see the environment around them. It trains computers to understand and interpret the world. Utilizing digital images from cameras, videos, and machine learning models. Machines are given the power to identify and classify objects, giving them the ability to react to all that they "see" Now, before we get into the nuances of how this fascinating field works, let's travel back in time and observe computer vision in its early days. In the early 1950s, the first experiments for computer vision were conducted. Neural networks were used to detect object edges and interpret simple handwritten text. As big data surged in the 1990s, large sets of images of people and things emerged on the Internet, and machines could identify specified objects in photos and videos. Computer vision has now flourished given the abundance of photos and videos, also known as big data in our society, as well as the advanced hardware, software, and algorithms. So how exactly does computer vision work? Well, think of it like a puzzle. You have with you several different kinds of pieces that all fit together in some way to form a complete image. You look at the edges and the individual elements of each puzzle piece to perceive which components fit together and approximately where they should be placed to create a cohesive whole. This is analogous to the process that a machine, specifically neural networks, go through when trying to understand visual images. They identify edges and borders and try to identify model sub components. Instead of being given a complete image,as humans are usually at top of the puzzle box, they are trained using hundreds of thousands of similar images, and these images are available thanks to the big data that's available in society, as we touched on in the beginning of this video. Now if you'd like to learn more about big data, I have a video that covers the basic components of this concept. And if you'd like to know more about how the machine actually trains its algorithm using the hundreds of thousands of images it receives through machine learning, I'd recommend you check out my video on this subfield of artificial intelligence. Right now, let's take a look at how a specific architecture of neural networks ,called convolutional neural networks, powers computer vision. So we all know that images are made up of big grids of pixels. Each Pixel has a designated color on the red green blue scale where the primary colors are combined in various ways to represent diverse colors. To identify features in images, computer vision considers small patches of pixels through mathematical notation called kernel or filter, which contain values for Pixel wise multiplication. So if you've watched my video on neural networks, you might recall that an artificial neuron, the basic component of neural nets, takes in a series of inputs, multiplies the inputs by specified weights and biases, and uses back propagation to learn from mistakes. These input weights are analogous to kernel values where neural nets learn useful kernels that are able to recognize unique features and images. Convolutional Neural nets utilize a range of preexisting neurons to process each new image. With each layer, the prior image is digested and manipulated by different learned kernels and a new image is output. This output is then processed by the next layer of neurons resulting in repeated convolutions. Connecting this to our puzzle example, for example, the first convolution might discover edges. Then the next layer might convolve on the edge features to detect simple shapes made up of edges or corners. Then the next layer might convolve on the corner features and utilize neurons that can detect simple entities such as noses and mouths. This process repeats and grows in complexity with every passing layer until the machine reaches a layer that can recognize all parts of the image, for example the eyes, nose, ears and mouths and deem the image of face. So in summary, computer vision works by acquiring an image, processing the image and understanding the image. For more videos on your journey towards mastering AI, be sure to Subscribe to youtube.com/acadaimy. Thanks for watching!

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Checkout our courses at https://opencv.org/courses/ Kickstarter Page: https://www.kickstarter.com/projects/satyamallick/ai-courses-by-opencvorg This course is designed to build a strong foundation in Computer Vision. You will get a solid understanding of all the tools in OpenCV for Image Processing, Computer Vision, Video Processing and the basics of AI. you will build systems or applications using the techniques learned in the first course as well as some new ones that we cover as we go along. We also cover a substantial amount of Machine Learning and the basics of Deep Learning in this course. We will also build interesting Web Applications as part of the course curriculum. we will start with a theoretical understanding of simple neural nets and gradually move to Deep Neural Nets and Convolutional Neural Networks. Not only will we go over Deep Net architectures used for solving various computer vision problems, we will also go over practical considerations needed to successfully train Deep Neural Networks. These include how to prepare datasets, how to perform sanity checks before embarking on training that can take hours, how to use visualization tools to debug the training process, what workflows to use when the results are not satisfactory, and finally how to deploy your network on the cloud.

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Travel the news world without a passport and access local perspectives with Ground News. Plans start at under $1/month or go to https://ground.news/easyactually to get 30% off the unlimited access Vantage plan. While some languages take as little as 600 hours to reach fluency, you can also just watch videos about how to learn the language for 1000 hours. Should achieve the same effect, honestly. the new community tab: https://discord.gg/xJZB4CwWk3

Marianna Pascal shows how the secret to speaking a new language with confidence is all about attitude, not ability. Marianna Pascal helps professionals achieve greater success in life by communicating effectively in English. Marianna began her career as an actor and performed across her native Canada in film television and on stage for 15 years. Today, Marianna improves the way employees relate to their bosses, clients, colleagues and other key stakeholders. Marianna is also the Official Communication Trainer for Miss Malaysia World. An award-wining speaker, Marianna is known for her humorous speech on Youtube titled "Local English or Standard English" which has been seen by over a million people worldwide. Marianna is also the author of the best-selling book series English Fast & Easy. The most recent was #2 on Borders' Best-Selling List for non-fiction. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx

Unlock faster progress in language learning with three powerful mindset shifts! Discover how the right language learning mindset and motivation can help you overcome common blocks. Rooted in social psychology, these tips boost confidence in language learning and make fluency achievable—no need to move abroad! 💬 Each week, I select one comment to receive a FREE guide—drop a comment below to enter! Feeling overwhelmed by language learning? Start speaking with confidence and beat the frustration today! Dr. Alex, an award-winning social psychologist and Fortune 500 leader, shares practical, research-backed techniques to make your language learning journey more enjoyable and effective. From building tiny, powerful habits with the bestselling Everyday Nano Method to leveraging mindset shifts and AI tools, you'll find everything you need to take small steps toward big results. 🌟 Start Here: Great Promos on Bestsellers: dralexsteinberg.gumroad.com 🎧 Prefer listening? FREE on Spotify: https://podcasters.spotify.com/pod/show/alex-steinberg Read The Blog: medium.com/@bluereindeer Buy Wellbeing Gifts: amazingpositiveenergy.myspreadshop.fr Get Book on Amazon: amazon.com/ACHIEVE-TEAM-FLOW-collaborative-Simplicity-ebook/dp/B09ZBDBGW2 Follow on X 👉 x.com/@allentrepreneur Get my favourite AI Tools: writesonic.com/?via=alexandra 🎧 Need study music? Visit my Focus & Study Music channel: @bluereindeer 📚 More personal growth content: Visit @agilitysimplicity 🔔 Subscribe for weekly videos on language learning, mindset mastery, and productivity hacks. Don’t forget to turn on notifications for the latest tips and tricks on making your language journey fun and achievable! 💡 Popular Videos On This Channel: Confidence Hacks for Language Learners How to Overcome Intermediate Plateau Productivity Tips for Faster Language Progress #languagelearning

AI-driven robots need to learn new actions as fast as possible. This is very important.

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In this episode, I discuss how to build and apply a growth mindset — the practice of self-rewarding and focusing on learning and skill development through effort — to improve learning and performance. I also discuss how our internal narratives drive our ability to make progress. I contrast the growth mindset vs. the fixed mindset and describe how the type of feedback or praise we receive shapes our mindsets, and the huge advantage of rewarding and celebrating “verbs” (actions) rather than adjectives (performance labels). I also discuss data showing how stress can enhance performance and explain why the growth mindset works synergistically with the “stress-is-enhancing mindset,” and how to combine them. Whether you are a student, coach, therapist, parent, teacher or simply someone seeking to improve at something, this episode provides numerous science-supported tools for how to adopt a performance enhancing mindset. #HubermanLab #Science #GrowthMindset Thank you to our sponsors AG1: https://drinkag1.com/huberman Eight Sleep: https://eightsleep.com/huberman ROKA: https://roka.com/huberman InsideTracker: https://insidetracker.com/huberman Supplements from Momentous https://www.livemomentous.com/huberman Social & Website Instagram: https://www.instagram.com/hubermanlab Twitter: https://twitter.com/hubermanlab Facebook: https://www.facebook.com/hubermanlab TikTok: https://www.tiktok.com/@hubermanlab LinkedIn: https://www.linkedin.com/in/andrew-huberman Website: https://hubermanlab.com Newsletter: https://hubermanlab.com/neural-network Articles Praise for intelligence can undermine children's motivation and performance: https://psycnet.apa.org/record/1998-04530-003 Why do beliefs about intelligence influence learning success? A social cognitive neuroscience model: https://academic.oup.com/scan/article/1/2/75/2362769 Rethinking stress: The role of mindsets in determining the stress response: https://psycnet.apa.org/record/2013-06053-001 A synergistic mindsets intervention protects adolescents from stress: https://www.nature.com/articles/s41586-022-04907-7 Other Resources Dr. Carol Dweck: https://profiles.stanford.edu/carol-dweck Dr. Alia Crum: https://profiles.stanford.edu/alia-crum Dr. Alia Crum: Science of Mindsets for Health & Performance (Huberman Lab episode): https://hubermanlab.com/dr-alia-crum-science-of-mindsets-for-health-performance Dr. David Yeager: https://liberalarts.utexas.edu/psychology/faculty/yeagerds Timestamps 00:00:00 Growth Mindset 00:02:55 Sponsors: Eight Sleep & ROKA 00:05:17 Mindset & Narrative 00:12:58 Intelligence Feedback vs. Effort Feedback, Identity Labels 00:20:10 Intelligence vs. Effort Praise: Performance, Persistence & Self-Representation 00:26:45 Fixed Intelligence vs. Growth Mindset 00:28:53 Tool: Intelligence (Performance) vs. Effort Narrative, Labels 00:32:30 Tool: Failure & Identity; Effort & Verbs 00:34:36 Sponsor: AG1 00:35:41 Tool: Timing, Intelligence vs. Effort Praise & Performance 00:40:08 Fixed Mindset vs. Growth Mindset: Failure & Performance 00:50:28 Tool: Shift from Fixed Mindset 00:54:23 Sponsor: InsideTracker 00:55:30 Stress-is-Enhancing Mindset 01:05:53 How Stress Can Enhance Performance 01:13:21 Growth Mindset + Stress-is-Enhancing Mindset & Performance 01:20:36 Reframing Stress 01:25:45 Tool 1: Student & Teacher Mindset 01:28:17 Tool 2: Effort Praise/Feedback: Verbs not Labels 01:31:24 Tool 3: Errors & Seeking Help 01:32:58 Tool 4: Self-Teaching & Growth Mindset 01:34:35 Tool 5: Reframe “Mind is Like a Muscle” Analogy 01:39:20 Zero-Cost Support, YouTube Feedback, Spotify & Apple Reviews, Sponsors, Momentous, Social Media, Neural Network Newsletter Title Card Photo Credit: Mike Blabac - https://www.blabacphoto.com Disclaimer: https://hubermanlab.com/disclaimer