Group items tagged

Liner.ai: Free tool to train ML models easily; no coding or ML expertise required (liner.ai).

Chai: Build and deploy Artificial Intelligence chatbot. Chat with Artificial Intelligence (chai.ml).

Jio Brain, unveiled by Reliance Jio in 2024, is a significant step forward in democratizing AI technology. Let's Dive into India's First 5G Powered ML Platform. #JioBrain #EnterpriseAI #MLPlatform #5GRevolution

Tech Budget 2024: Good news for AI, ML, cybersecurity enthusiasts! Discover how India is investing in tech advancements - what does it mean for you?

Lobe: Train ML models easily with Lobe (lobe.ai).

AI Experiments: Explore ML through pictures, drawings, language and music (experiments.withgoogle.com).

Teachable Machine: Teachable Machine: fast, easy ML models for everyone (teachablemachine.withgoogle.com).

Amazon CodeWhisperer: Write code faster with ML-powered code recommendations (aws.amazon.com).

Laion: Enables ML research with datasets, tools, models & resources (laion.ai).

Lobe: Train ML models easily with Lobe (lobe.ai).

Polymath: Convert any music library into a music production sample-library with ML (github.com).

Photor Artificial Intelligence: Present the best version of yourself with advanced image recognition and ML (photor.io).

Pyttipanna: Pyttipanna is an interface for creating videos with Pytti 5 & ML models (pyttipanna.xyz).

Facial Assessment Tool: Use ML to analyze your face and get cosmetic suggestions (qoves.com).

Master the skills required to become an AI product manager and drive the successful development and deployment of AI products to deliver value to your organization. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Build products that leverage AI for the common good and commercial success Take macro data and use it to show your customers you're a source of truth Best practices and common pitfalls that impact companies while developing AI product Book Description Product managers working with artificial intelligence will be able to put their knowledge to work with this practical guide to applied AI. This book covers everything you need to know to drive product development and growth in the AI industry. From understanding AI and machine learning to developing and launching AI products, it provides the strategies, techniques, and tools you need to succeed. The first part of the book focuses on establishing a foundation of the concepts most relevant to maintaining AI pipelines. The next part focuses on building an AI-native product, and the final part guides you in integrating AI into existing products. You'll learn about the types of AI, how to integrate AI into a product or business, and the infrastructure to support the exhaustive and ambitious endeavor of creating AI products or integrating AI into existing products. You'll gain practical knowledge of managing AI product development processes, evaluating and optimizing AI models, and navigating complex ethical and legal considerations associated with AI products. With the help of real-world examples and case studies, you'll stay ahead of the curve in the rapidly evolving field of AI and ML. By the end of this book, you'll have understood how to navigate the world of AI from a product perspective. What you will learn Build AI products for the future using minimal resources Identify opportunities where AI can be leveraged to meet business needs Collaborate with cross-function

How you build, format, and annotate your training dataset has a direct impact on the model you create. In fact, poorly processed data is one of the most common reasons that machine learning projects fail. However, if you haven't worked with training data before, it can be difficult to know where to start. After all, data can be surprisingly complex. It's hard to figure out what a dataset should look like and how to improve it.

Transformers have now become the defacto standard for NLP tasks. Originally developed for sequence transduction processes such as speech recognition, translation, and text to speech, transformers work by using convolutional neural networks together with attention models, making them much more efficient than previous architectures. And although transformers were developed for NLP, they've also been implemented in the fields of computer vision and music generation. However, for all their wide and varied uses, transformers are still very difficult to understand, which is why I wrote a detailed post describing how they work on a basic level. It covers the encoder and decoder architecture, and the whole dataflow through the different pieces of the neural network. In this post, we'll get deeper into looking at transformers by implementing our own English to German language translator.

My introductory guide to facial recognition in machine learning

From search engines and sentiment analysis to virtual assistants and chatbots, there are numerous areas of research within machine learning that require text annotation tools and services. In the AI research and development industries, annotated data is gold. Large quantities of high-quality annotated data is a goldmine. There are a variety of text annotation tools and services available that can provide you with the data you need. Some of these services include entity extraction, part-of-speech tagging, sentiment analysis, and more.