GitHub Education’s Post

I’m thrilled to share that I’ve completed and obtained a new certification: Supervised Machine Learning: Regression and Classification from DeepLearning.AI! 🎉 This course, taught by the amazing Andrew Ng, deepened my understanding of essential machine learning concepts such as regression, classification, model evaluation, and much more. It has been a rewarding learning journey, filled with hands-on projects and real-world applications that are sure to boost my AI skills. 🚀 To further contribute to the community, I’ve created a GitHub repository where I’ve compiled all the interactive notebooks I worked on during the course. These notebooks contain detailed implementations and code snippets that may be useful for anyone exploring machine learning concepts. 👉 Here’s the link to my repository: https://lnkd.in/eqKrmuxK Feel free to take a look, use them, and drop any feedback or suggestions! Let’s continue learning and growing together. ❤️ #MachineLearning #AI #DeepLearning #DataScience #AndrewNg #Coursera #GitHub

🚀 Exciting News: Achieved Dog-Cat Image Recognition using CNN! 🐾🖥️ Hey LinkedIn fam! 👋 I am thrilled to share a major milestone in my coding journey. 🚀 After 50 intense epochs, I've successfully trained a Convolutional Neural Network (CNN) that can distinguish between images of dogs and cats with an outstanding accuracy of 94.90%! 📈🔥 👉 What's the Project About? I embarked on a mini project to create an image classifier using CNN. The goal was simple but challenging: Teach the model to identify whether an image contains a dog or a cat. The results? Absolutely mind-blowing! 📊 Performance Stats: Accuracy: 94.90% Epochs: 50 Framework: TensorFlow 2 Architecture: Convolutional Neural Network (CNN) 💻 GitHub Repository: Curious about the code? Dive into the project on my GitHub repository: GitHub Link: https://lnkd.in/dje_kg29 🌟 Key Takeaways: Hands-on Learning: This project was a fantastic hands-on experience in implementing CNN for image classification. Persistence Pays Off: It took dedication and fine-tuning, but achieving almost 95% accuracy was worth every late-night session! Open Source Spirit: I'm sharing the entire codebase on GitHub, encouraging collaboration and learning. Feel free to explore, contribute, or provide feedback! 🚀 Future Steps: This is just the beginning! I plan to expand and optimize this project further. Your insights and suggestions are more than welcome. Let's keep pushing the boundaries of what's possible in the world of AI and deep learning! 🌐💡 👨💻 #DeepLearning #MachineLearning #AI #CNN #ImageRecognition #TechInnovation #GitHub #CodingJourney #DataScience #convolutionalneuralnetworks #imageprocessing #imageclassification Can't wait to hear your thoughts and feedback on this exciting journey! Let's connect, learn, and grow together. 🚀🌟

Excited to share my Deep Learning Repository on GitHub(https://lnkd.in/g6hDSCQU)! Is still a work in progress but feel free to explore projects, resources, and code examples covering topics like ANN, CNN, RNN, and more. Check it out and give it a star ⭐️ if you find it a helpful resource! #DeepLearning #MachineLearning #AI #GitHub #OpenSource #Code #Projects #NeuralNetworks

If you were like me and came from a full stack development background, the idea of machine learning was very intimidating. All the math, statistics, and big brain stuff that goes into it can be very scary. I wanted to relieve a lot of that stress by creating a tutorial with freeCodeCamp that gives a beginner guide to machine learning. It teaches a lot of the important concepts without getting into the weeds. Ml5.js is perfect for this type of situation as it abstracts lots of this complexity while still revealing many of the important concepts. We go over things like pre-trained models, transfer learning, and building custom models with neural networks. This course should give you enough to be curious and to explore on your own personal projects. It was actually based on a course I taught in India, so big thanks to Athenia High School for letting me experiment with this approach. Tutorial : https://lnkd.in/e6wwfeg5

Bookmarking this will definitely help you in the future!

Finished. Partly ;) Today i successfully finished my first project on my learning path into AI. That was quite cool - to refresh my old coding skills, learning a new coding language and learning more from feedback in this project. One learning i want to share: Read the instructions in detail… had to do a review, because my output was not as expected. But, and that is great, the feedback was very professional, understandable and helpful! Big thanks to the reviewer!! Next step: working on my own very first image classifier. After a refresher on calculus and linear algebra as well as a deeper dive into neural networks. #udacity #AWS #AI #neverstoplearning

We’re stopping your lunchtime scroll for another edition of #TechBytes. A deep learning showdown: PyTorch vs. TensorFlow Are you diving into the world of deep learning? 🤖 Let’s explore these two powerful Python libraries 📊 #PyTorch: ▶️ Developed by Facebook’s AI Research lab, PyTorch is gaining popularity for its simplicity and user-friendliness ▶️ Powers OpenAI’s ChatGPT and Tesla's autopilot, as well as Facebook for its AI research and production models ▶️ It efficiently handles dynamic computational graphs, making it a favourite among developers and researchers ✅Pros: PyTorch’s dynamic computation graph allows for more intuitive model building and debugging. Python-like coding, good documentation, and strong community support. 🚫Cons: Third-party tools needed for visualisation, API server required for production. #TensorFlow: ▶️ Google’s brainchild, TensorFlow, excels in production capabilities and distributed training ▶️ It’s widely used by companies like Google (search), Uber, and Airbnb ✅Pros: Simple built-in high-level API, production-ready options and wide range of tools including TensorFlow Extended (TFX) for production machine learning, TensorFlow Lite for mobile, and TensorFlow.js for web applications. 🚫Cons: Static graph, debugging challenges, and less flexibility for quick changes. Both PyTorch and TensorFlow have their unique advantages. Your choice depends on your specific needs—whether it’s the flexibility and ease of use of PyTorch or the robust production tools of TensorFlow. #TechandDataPeople #TDP #DeepLearning #AI #MachineLearning #TechRecruitment

Looking to break into AI? This roadmap from Learnbay is your go-to guide. It covers everything you need to know to succeed in this fast-growing field—from basic programming skills to advanced machine learning techniques. Whether you're a beginner or aiming to advance your career, this guide has all the essential steps to help you excel. Don't miss out—check it out at : https://lnkd.in/gVJ72GPi Follow Learnbay for more valuable insights! #Learnbay #AI #DataScience #MachineLearning #CareerPath

🚀 Day 0: Embarking on My PyTorch Journey 🚀 Today, I'm kicking off my deep dive into PyTorch! After working extensively with TensorFlow, I’m excited to see why PyTorch is gaining so much traction across the ML community and industry. PyTorch has quickly become a powerhouse in AI and ML research, especially popular in academia and with companies like Meta, Microsoft, and OpenAI. 📊 Fun fact: as of 2023, PyTorch is used in more than 70% of AI research papers, and its GitHub repo has over 23,000 forks and 68,000 stars! For the next 7 days, I’ll be working through a 55-hour PyTorch bootcamp on Udemy (yes, ambitious!), and I’ll be sharing daily takeaways and thoughts. This will be a "learn in public" experiment to stay consistent and grow together with the amazing ML community here. If you’re a PyTorch enthusiast, I’d love any tips or insights. Here’s to tackling a new tool and broadening my Deep Learning toolkit! I'll be following the OG course from Daniel Bourke . Very excited for the song dude... #PyTorch #MachineLearning #AI #LearnInPublic #Day0

I have a goal to create a "small language model" with a "big brain" and decided I need to dive deep into the internal workings of the godfather of all modern large language models - GPT2. To truly understand how GPT models work, there is no better option than create one yourself. I could have copied python scripts and C++ code from github but that is not really learning and YouTube videos barely explain the "how" and don't address the "why." Trying to piece it all together was proving frustrating and then I found "spreadsheets are all you need" (https://lnkd.in/gwEXZ27e) by Ishan Anand and to my excitement discovered he is teaching a live class on the Maven e-learning site. Ishan readily shares all the intricate details of the internal workings of GPT2 - the formulas, the data and how the transformer algorithm works at the most basic - as I call it "the magic." His step-by-step demonstration of each piece of the GPT algorithm transformer model and presents all the data in an easy to navigate tool - a massive Microsoft Excel spreadsheet. My own goal is to implement a CPU only model inside an InterSystems IRIS database with a small language model that has direct access to local data. I have always classified LLMs as a database, after all they utilize vector databases to perform certain functions and IRIS is the perfect database system to implement an LLM since IRIS is highly performant and can implement ANY database model - vector, tabular, columar, relational and network. Even if you are not a developer, this course is the best training I have found to demystify the transformer algorithm. This knowledge is the basis for understanding the state of the art in AI and what it can really do and also why it fails to do certain tasks. Ishan's class explains it all and you can even ask questions and get understandable answers without being a math PhD.