This curated selection of YouTube courses on machine learning and deep learning offers a comprehensive pathway for learners at all levels. From foundational concepts to specialized applications, these courses cover topics such as statistical machine learning, deep learning, specialized applications in healthcare, NLP, real-world applications, computer vision, and reinforcement learning.
Prompt engineering, also known as natural language generation, is a rapidly growing career option. This tech-driven field creates human-like text for various applications, impacting content creation, customer service, virtual assistants, and personalized marketing campaigns. To excel, professionals need a mix of technical and creative skills, making it a promising career choice with increasing demand.
Machine Learning (ML), Artificial Intelligence (AI), and Generative Artificial Intelligence (GenAI) can revolutionize industries worldwide. AWS offers various services for ML and AI, including Amazon SageMaker and Amazon Bedrock. GenAI, such as OpenAI’s ChatGPT, holds promise but requires responsible use. Moving from Broad AI to GenAI represents significant advancements in AI capabilities. Hands-on projects are crucial for mastering ML and AI on AWS.
Natural language processing (NLP) is a branch of artificial intelligence focusing on computer-human language interaction. It enables machines to understand, analyze, and generate language. Transformer models use attention mechanisms to capture global context and dependencies, making them suitable for tasks like machine translation and text summarization. BERT, a pre-trained transformer model, excels in various NLP tasks.
Machine Learning (ML) powered by TensorFlow revolutionizes industries through data analysis and automation. Ubuntu, known for stability, offers an ideal environment for TensorFlow. System requirements and installation methods are outlined. Hands-on project implementation steps and advanced features like GPU acceleration are discussed. The combination of TensorFlow and Ubuntu unlocks endless possibilities in machine learning.
This is a guide to machine learning, breaking down its big ideas into easy-to-understand parts. It covers supervised and unsupervised learning techniques, including classification, regression, clustering, and association. Additionally, it explores semi-supervised learning, reinforcement learning, deep learning, generative AI, and natural language processing. The guide emphasizes the practical applications of these techniques in our daily lives.
To build a functional web app, you need a web interface and a server for data processing, storage, and ML/AI models. YouTube tutorials can be confusing and lead to bad coding habits. Tips include using Next.js, Tailwind CSS, FastAPI, TypeScript, Modal for GPU backend, AWS Lambda for deployment, and Firebase + Stripe for user authentication and payments. Sentry for error monitoring and avoiding building a web app using Spotify’s API are recommended.
This article discusses the importance of prompt engineering in natural language processing and artificial intelligence, particularly in optimizing interactions with language models like GPT. It emphasizes the need for clear, specific instructions, context, reference texts, and breaking down complex tasks. Additionally, it recommends giving the model time to think, using external tools, and systematically testing changes for optimal results.
The article discusses using imitation learning and a 3D Convolution ResNet model to build an AI game bot for the Google Snake game. It covers data collection using Selenium, dataset creation, model creation, balancing the dataset, training, and using the model to play the game. The skills learned can be applied to broader AI scenarios. GitHub Repository: akshayballal95/autodrive-snake at blog (github.com)
AI-based content generation tools like ChatGPT have revolutionized content creation but face resistance due to authenticity concerns. SmalSEOTools, DupliChecker, GPTZero.Me, and Writer.Com offer AI content detection, utilizing advanced techniques and NLP to distinguish AI-generated text. While helpful, these tools vary in accuracy and features, highlighting the ongoing debate over AI content use.
AI technologies are becoming more integrated into our daily lives, including advancements in multimodal models. TinyGPT-V, a compact vision-language model, demonstrates high performance while significantly reducing computational needs. By employing techniques like Parameter Efficient Fine-Tuning and normalization, it aims to make AI models more efficient, paving the way for widespread practical applications.
OpenAI launched the GPT Store, offering custom versions of ChatGPT to subscribers. Users can create tailored GPTs for specific tasks and easily share them. The store will also offer a revenue program for GPT builders. Customizable GPTs come with features like configurability, shareability, and integrated capabilities. A tutorial demonstrates creating and customizing a GPT with custom actions.
In the future, intelligent data apps will enable organizations to orchestrate operations like Amazon. The transition involves moving from historic systems to real-time models, unifying data semantics across platforms, and integrating predictive and prescriptive analytics. This shift requires new technology layers and tools to enhance language models. Ultimately, the goal is to create end-to-end models of businesses, albeit with challenges in transitioning legacy applications. The transition towards a data-centric approach in application development will lead to more accessible and user-friendly business semantics.
The post outlines the author’s endeavor to fine-tune Large Language Models (LLMs) for audio processing. Motivated by the potential to create LLMs capable of describing human voices, the author discusses their process, including adapting cross-domain encoders, debugging issues, and achieving promising training results. The ultimate goal is to expand the model’s capabilities to tasks such as transcription and speaker identification.
