Zapier’s Post

🚀 Exciting Update in AI! 🚀 Delighted to announce my latest Medium blog post in the Google TensorFlow series. After delving into time series analysis with London Bike demand, my new piece explores TensorFlow's transformative role in dog breed prediction using Transfer Learning. This advanced multiclass classifier isn't just identifying dog breeds – it's revealing the vast diversity of our canine companions. Google TensorFlow is revolutionizing not only dog breed prediction but also our approach to complex challenges like urban mobility. This blog is more than just insights – it's a call to engage and build a community around the extraordinary potential of machine learning. Your thoughts and experiences are essential. Join the discussion, share your views, and let's harness AI to innovate across industries. Read, comment, and share – let's kickstart this conversation on LinkedIn. 😊 🚀🐕🔍📈 #AITransformation #MachineLearning #TensorFlow #DogBreedPrediction #CommunityEngagement

💡 Day 26/45 🎬 Exciting News! Introducing Our Movie Recommendation System Project! I'm thrilled to announce the completion of our latest project: a sophisticated Movie Recommendation System using Machine Learning! 🚀🤖 In today's world of endless entertainment options, finding the perfect movie can sometimes feel like searching for a needle in a haystack. That's why we embarked on this journey to develop a solution that simplifies the movie selection process and enhances user experience. Our Movie Recommendation System leverages the power of Machine Learning algorithms to analyze user preferences and historical movie data, providing personalized recommendations tailored to individual tastes. Whether you're a fan of action-packed blockbusters, heartwarming dramas, or side-splitting comedies, our system has got you covered! #MachineLearning #MovieRecommendation #DataScience #ArtificialIntelligence #TechInnovation #MovieLovers #LinkedInPost github repo -

Plug and play different LLMs across multiple use cases to compare performance. Deploy the right model for the right task. Regularly review your task configuration as new models are released. ➡ 🧠

Launching the free version of FireAI soon! Experience the power of FireAI by asking queries in simple English from publicly available datasets. Stay tuned!

What is new with Gemma 2? What new innovations did Google DeepMind introduce? We wrote a blog post covering all the details of the Gemma 2 release! 👀 > Sliding window attention: Interleave sliding window and full-quadratic attention for quality generation. > Logit soft-capping: Prevents logits from growing excessively by scaling them to a fixed range, improving training. > Knowledge Distillation: Leverage a larger teacher model to train a smaller model (for the 9B model). > Model Merging: Combines two or more LLMs into a single new model. Blog: https://lnkd.in/esjkDT9h

THG Advisors continues from yesterday's discussion on OpenAI tools, diving deeper into other tools, exploring their capabilities, practical applications, and best practices for maximizing their value in various business scenarios. You can read the article here. https://lnkd.in/eUZsH6BN

What a blast! And still another day of UNPARSED tomorrow 🔥 Some (personal) highlights of the day: 1. Almost everyone is experimenting with LLMs but most companies still rely on deterministic flows 🥹 2. Tools like Copilot may reduce the number of developers per team… but increase the number of teams 🔥(Josephine Scholtes) 3. “LLM vs Control” is a false dichotomy: You can have generative prompts and be in control of the output. CALM by Rasa is a good example. 🤓 4. The uncanny valley is LEARNED! Kids under 9 don’t experience it. Is it possible to have it in voice though? Good question by Falene McKenna 🙌 5. Special mention to Michael Kibedi (on the picture below) and his analogy between AI and a 13th century Brazen head (look that up, it’s totally worth it). I took pictures of all his slides ♥️ 6. And last but not least, great congrats to Nikoletta Ventoura on winning the Prompties tonight. Definitely someone to follow if you are on this field. 😎

Oh how I wish I had an assistant or a friend who could record my experiments as I focus on optimizing my machine learning model?  Oh did I get better results after using the previous sets of hyperparameters or by using the current ones? Oh no! Did I forget to note down the parameters when I got those stunning results from the model run? Gosh how do I replicate those excellent results again? If you have ever had one of these issues earlier look no further MLFlow is the trusted friend who records automatically while you experiment and play with your models ! At aimadeasy blog, Kunal K. and I have published part 1 of our series which introduces mlflow and demonstrates its experiment recording capacity, link in comments.