Benjamin Blehm’s Post

🧠 Demystifying Neural Networks: A Deep Dive into the "Black Box" 🔍 Ever wondered how neural networks actually work? I've just published a comprehensive guide on Medium that breaks down these powerful algorithms into easy-to-understand concepts. In this article, you'll discover: • The basic structure of neural networks • How they process data, step-by-step • A simple, calculable example of a neural network in action • Real-world applications and limitations Whether you're a curious beginner or a seasoned pro looking to refresh your knowledge, this post offers valuable insights into the world of AI and machine learning. 👉 Read the full article here: https://lnkd.in/g2QNVPH2 #MachineLearning #ArtificialIntelligence #DataScience #NeuralNetworks Let's discuss: What's your experience with neural networks? How do you see them shaping the future of technology?

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.

I have successfully completed a course on Attention Mechanisms in Machine Learning and Deep Learning! Understanding how models like transformers and attention-based networks can focus on the most relevant parts of the input data has been a game-changer. This powerful concept is behind so many innovations, from language models to image recognition systems. I'm thrilled to apply this knowledge to real-world problems and push the boundaries of what's possible with AI! Google Cloud Skills Boost #MachineLearning #DeepLearning #AttentionMechanism #AI #DataScience #ContinuousLearning #ProfessionalGrowth

🚀 Excited to share my latest Medium blog post: "Dropout Dramatics: Unveiling the Secret Weapon of Neural Networks"! 🧠✨ In the dynamic world of neural networks, achieving peak performance is a delicate dance between learning and adaptability. But beware the pitfalls of overfitting—where models become too rigid with their training data. Fear not! Dropout layers are here to revolutionize the game! Join me on a journey through the captivating world of dropout layers in my latest Medium blog post. 🤓💡 Discover how this ingenious technique, pioneered by Geoffrey Hinton and team, acts as the secret weapon in preventing overfitting by promoting diversity and resilience among neurons. Plus, uncover how dropout layers turbocharge training and simplify model development for us mere mortals! 🚀🧩 Whether you're a seasoned ML aficionado or an eager enthusiast, this post is your ticket to unraveling the mysteries of dropout layers and unleashing the full potential of AI. 💪🔓 Dive into the secrets of neural networks—read the full post on Medium now! 📚🔍 https://lnkd.in/gqJzeWsE #MachineLearning #NeuralNetworks #DropoutLayers #AI #TechBlog #Medium #LinkedIn

Exploring the architectures of Recurrent Neural Networks (RNNs) 🤖 – One-to-One, One-to-Many, Many-to-One, and Many-to-Many! These foundational models power sequence-based tasks like text generation ✍️, speech recognition 🎤, and machine translation 🌍. Visualized here with distinct layers, showcasing the beauty of data flow through input, hidden, and output stages. A heartfelt thanks to Krish Naik Sir 🙏 for his invaluable guidance in understanding RNNs and deep learning concepts. Grateful for this enriching learning journey! #RNN #DeepLearning #AI #MachineLearning #KrishNaik #DataScience #NeuralNetworks 🚀"

Developed a solid understanding of deep learning fundamentals, including neural networks, backpropagation, and common architectures, such as CNNs and RNNs, for solving real-world problems.

LSTMs are a type of Neural Network that excel at learning from sequences and solves long-term dependencies issues. Whether it's predicting stock prices or understanding natural language LSTMs have proven to be game-changers. If you’re passionate about AI and deep learning, LSTMs are definitely worth exploring! #DeepLearning #AI #MachineLearning #LSTM #NeuralNetworks

It seems a lot of people want a primer on AI. So here's a really good series of videos by a strong maths fellow explaining things in a very simple, very clear way. https://lnkd.in/ezHh4dct Three Blue One Brown, in case you're not familiar, does a lot of really good deep dives on all kinds of math problems, Tranformers, neural networks, deep learning, etc being only one small part of the channel's content. It will also give you a clue as to why I say machines are thinking now. A lot of people have simplified their understanding of LLMs as "next token predictors" which masks what's going on there. Markov chains are next token predictors. Transformers are, yes, at the end giving a list of tokens and the likelihood of each, but to get to that point requires a lot of sophisticated thought. To grok that, watch this video series and get a sense of the complexity that's hidden underneath transformers. This does not go into diffusion models which are thinking machines in a different sense. If I find a good video series on those, I'll post it.

Just finished the course “Deep Learning Specialization: Neural Networks and Deep Learning” by Andrew Ng ! Check it out: https://lnkd.in/gJW6BdrB #ArtificialNeuralNetwork #Backpropagation #PythonProgramming #DeepLearning #NeuralNetworkArchitecture #AndrewNg.