Nolojik Innovations’ Post

Deep Learning Made Simple: CNNs, RNNs, GANs, and More 🧠💡 Straightforwardly and understandably, Jyoti Dabass, Ph.D's article deconstructs intricate deep learning architectures such as CNNs, RNNs, GANs, Transformers, and Encoder-Decoder models. For anyone looking to gain a deeper knowledge of these powerful technologies!  #DeepLearning #MachineLearning #CNN #RNN #GAN #Transformers #AI #NeuralNetworks #TechEducation https://lnkd.in/dJE4dQkA

If you are wondering how to choose the right machine learning model for your problem, check out this article by Iván Palomares Carrascosa on Machine Learning Mastery. It gives a very good insight and practical guides, from basic linear regression to neural networks.

Hey there, 👋🏻 As I am on a journey to dive deep in machine learning, I cleared another check point of deep learning (specifically Neural Networks) on Kaggle I got to know about NN and it's application for the prediction through regression and classification including how to overcome challenges like over fitting and under fitting by the help of activation and Batch Normalization. It was a great experience and I am excited to work on projects based on Deep Learning. #deeplearning #Kaggle

As a tech enthusiast, I am fascinated by how AI is shaping and impacting our lives. Recently, I delved into neural networks and their fundamentals, a machine learning algorithm inspired by the human brain, and I was amazed by how it all boils down to math. Then I have decided to write a blog! #AI #MachineLearning #NeuralNetworks #TechEnthusiast #ArtificialIntelligence #DataScience #DeepLearning #TechnicalBlog #Technology #Blog #ComputerScience #computerscience #hashnode #blog #ai #engineering #developer https://lnkd.in/dCKc_4ns

LinkedIn Post for Day 71 🌟 Day 71/75: Introduction to Neural Networks with Keras Dive into the fascinating world of neural networks, the backbone of deep learning and AI! With Keras, you can build powerful models using an intuitive and user-friendly interface. 🔍 What you'll learn today: ✅ How neural networks mimic the brain to solve complex problems. ✅ Building a model in Keras: Define, Compile, Train, Evaluate. ✅ Using activation functions like ReLU, Sigmoid, and Softmax. ✅ Challenges and advantages of using neural networks. 💻 Ready to take your first step? Start by building a simple network with Keras and explore datasets like MNIST to bring your knowledge to life! #NeuralNetworks #DeepLearning #AI #MachineLearning #DataScience #ArtificialIntelligence #Keras #PythonProgramming #DeepLearningBasics #DataDriven #TechInnovation #BigData #MLAlgorithms #DeepLearningApplications #LearnNeuralNetworks #AIResearch #NeuralNets #AIForEveryone #DataAnalysis #Dr.Jitha P Nair #Entri Elevate #75DaysOfDataAnalysisChallenge

The next unit coming up for myself and other students in my cohort is Complex Game Systems, for this I want to challenge myself by developing a machine learning AI. I have found that simply creating an AI based around the use of Darwinism, (having the next generation inherit the positive traits from the best performing previous), was a good start, however there was a lot that went wrong. For example, a simple car sim I made had every generation flip on its side because the previous generation’s fastest performing car ended up crashing against a wall. This was interesting for sure, seeing how the best short-term goals didn’t mean the best in the long term. Having to constantly tweak how the AI was affected by its surroundings became tedious. This video on neural network structures was a really good look into the theory behind a more advanced algorithm. I am hoping I have more luck in finding information on this topic and applying it with more success.

🌟 Day 2 of #100DaysOfDeepLearning 🌟 Today was all about diving into the fascinating types of Neural Networks and understanding how they power various Deep Learning applications. Here's what I explored: 🧠 1. Artificial Neural Network (ANN): The foundation of all neural networks, ANN is inspired by the human brain and is used for tasks like classification and regression in structured data. 🔗 2. Multi-Layer Perceptron (MLP): A type of ANN with multiple hidden layers that helps capture complex patterns in data. Each layer is fully connected, making it powerful but computationally expensive. 📸 3. Convolutional Neural Network (CNN): Designed for image data, CNNs use convolutional layers to detect spatial features like edges, textures, and objects. They are widely used in computer vision tasks like image recognition and object detection. 🔄 4. Recurrent Neural Network (RNN): RNNs are ideal for sequential data as they have a "memory" to retain context across time steps. Perfect for tasks like language modeling, time-series forecasting, and speech recognition. 🔁 5. Autoencoders: These are unsupervised neural networks used for tasks like dimensionality reduction, anomaly detection, and data de-noising. They learn efficient representations of data by encoding and decoding it. 💡 Key Insight: Each type of neural network is tailored for specific tasks, showcasing the versatility of Deep Learning. Choosing the right architecture depends on the problem at hand. 🔍 Next Steps: I'll be diving deeper into the architectures, learning how to implement them, and understanding their practical applications. 💬 Have a favorite neural network or a resource you'd recommend? Let me know in the comments! Here’s to Day 2 and a deeper understanding of neural networks! 🤪 If you have any suggestion for learning deep learning please write it on comments, i will be thankful. #DeepLearning #DataScience #NeuralNetworks #AI #100DaysOfCode

Welcome to the first installment in the series on demystifying neural networks! If you've ever wondered how these powerful tools can recognize faces, translate languages, or even drive cars, you're in the right place. We're going back to basics, building a solid foundation for understanding how neural networks work from first principles. In this post, I tackle the following : - The Neuron: The Building Block: I break down the neuron, the fundamental unit of a neural network, and explore its role in processing and transmitting information. - Neural Network Anatomy: Think of this as a guided tour through the layers and connections that make up a typical neural network. - Jargon Buster: Don't let technical terms intimidate you! I have defined key concepts like weights, biases, activation functions, and more, making sure you're comfortable with the language of neural networks. Your feedback is invaluable as I continue this series. Let me know if you have any questions, suggestions, or topics you'd like me to cover. #AI #ML #DeepLearning

Excited to Share: #DeepLearning Essentials! As First Part before Stable Difusion, as per the request. I've just released a new video that dives deep into the world of Artificial Intelligence, Machine Learning, and Deep Learning Whether you're just starting out or looking to solidify your understanding, this video has you covered! 🌟 In this video, we explore: 🔹 The differences between AI, ML, and DL 🔹 How neural networks mimic the human brain 🔹 Key activation functions like ReLU, Sigmoid, and Softmax 🔹 A hands-on coding example to see these concepts in action This is a must-watch for anyone looking to stay ahead in the rapidly evolving field of AI and Deep Learning. Check it out, and let's continue learning together! Next part would be about CNN 🙂 🎥 Watch here: https://lnkd.in/gGFqw7JX #DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #ArtificialIntelligence #TechCommunity #LearningAndDevelopment #LinkedInLearning #TechTrends #Innovation

I recently started an exciting journey to truly understand the inner workings of neural networks. I’ve been diving into the fantastic Neural Networks from Scratch playlist by Sentdex on YouTube, which breaks down the concepts step by step in an incredibly approachable way.  - Day 2 What makes this series stand out is how it demystifies the complexities of neural networks. Each video translates a page of the accompanying book into clear, actionable lessons. Instead of relying solely on high-level frameworks like TensorFlow or PyTorch, this resource dives deep into the math, logic, and Python code that power neural networks. It’s all about understanding the why and how, not just the what.  Key Takeaways So Far - Building neural networks from scratch teaches the foundational mechanics—matrix operations, activation functions, backpropagation, and more.   - It bridges the gap between theory and implementation, making concepts like gradient descent feel tangible and actionable.   - Mastering these fundamentals strengthens your ability to debug, optimize, and innovate when working with real-world AI applications.  If you’ve ever been curious about what makes AI tick or want to deepen your understanding of neural networks, I highly recommend checking out the book and playlist. It’s a game-changer for anyone eager to learn the core mechanics of deep learning.  Resource Links  - Neural Networks from Scratch: https://nnfs.io - Sentdex YouTube Playlist: https://lnkd.in/d8q8tf2B Have you explored neural networks from scratch or a similar resource? I’d love to hear about your experience! Let’s discuss below. 👇  Follow Rehan Khan for more such insights :) #NeuralNetworks #DeepLearning #AI #MachineLearning #LearningJourney #Sentdex