Artificial Intelligence

WebScraping with Gen AI During this session, we'll explore the following topics: 1️⃣ Basics of Web Scraping: Understand the fundamental concepts and techniques of web scraping and its legal and ethical considerations. 2️⃣ Scraping with Gen AI: Discover how Gen AI revolutionizes the web scraping landscape with real-world examples. 3️⃣ Jina Reader API: Get acquainted with the Jina Reader API, a powerful tool for obtaining LLM-friendly input from URLs or web searches. 4️⃣ ScrapeGraphAI: Dive into ScrapeGraphAI, a groundbreaking Python library that combines LLMs and direct graph logic for creating robust scraping pipelines. Event Details: 🗓 Date: 22 June, Saturday ⏰ Time: 11:00 AM IST 🔗 Register now: https://www.buildfastwithai.com/events/web-scraping-with-gen-ai Connect with Founder from IIT Delhi; https://www.linkedin.com/in/satvik-paramkusham/

For working professionals willing to pivot their careers to AI: Here are the steps you can take right now: 1. Learn the basics of AI ================== You need to understand the differences among various AI jargons (e.g., what is the difference between statistical ML vs. deep learning? What exactly is an LLM?) and when to use which to solve a given business problem. Many fast-paced courses can teach you all of this without having to learn coding. (Shameless plug: I have a course that I will add in the comments section below) 2. Build an AI project in your current work ============================== Find a problem statement in your current work that can be solved using AI and will deliver some value. Work on this during your extra hours, then showcase it to your management to get official approval to make it a full-fledged project. 3. Collaborate with the AI team in your company for inner sourcing ================================================ Many companies have the concept of inner sourcing where, say, an AI team is too busy and has a list of tasks they have opened on their GitHub repository that others can work on. Use this as an opportunity to do some real AI work and build rapport with the AI team. 4. Attend AI conferences ================== By attending AI conferences, you will not only learn but also build a network with AI professionals who will help you in your AI career journey. 5. Attend an AI bootcamp at a university or online learning company ================================================= Artificial Intelligence 👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5 Like for more ❤️ All the best 👍👍

Save significant time every day with these ChatGPT's 7 prompts: 1. Make Hard Topics Easier to Understand: Prompt: Divide the (topic) into smaller, simpler pieces. Use comparisons and examples from everyday life to make the idea easier to grasp and more relevant. ChatGPT Prompts

Step 9: Career and Freelance Tips (1/2) Searching for Internships and Jobs: Using job portals and company websites. Leveraging university career centers. Joining professional associations. Networking and referrals. Crafting effective resumes and cover letters. (2/2) Preparing for Interviews: Technical preparation (coding practice, concepts). Behavioral preparation (STAR method). Researching companies. Mock interviews. Useful apps for interview preparation. Working with Freelance: Finding opportunities on freelance platforms. Building a strong profile and portfolio. Managing projects and client communication. Time management and payment methods. Artificial Intelligence Hope this helps you ☺️

People: AI will rule the world Meanwhile AI

Step 8: Practical Applications and Projects Identifying Real-World Problems: Planning and Outlining Projects: Choosing the Right Algorithm: Overcoming Overfitting: Building a Strong Portfolio Artificial Intelligence

Step 7: Generative Models Variational Autoencoders (VAEs): Encoder and decoder networks. Latent space representation. Reparameterization trick. KL divergence loss. Applications (data generation, anomaly detection). Generative Adversarial Networks (GANs): Generator and discriminator networks. Adversarial training. Loss functions (minimax, Wasserstein). DCGANs (Deep Convolutional GANs). Applications (image generation, style transfer). Artificial Intelligence

Step 6: Advanced Topics in Computer Vision Object Detection: Region-based methods (R-CNN, Fast R-CNN, Faster R-CNN). YOLO (You Only Look Once). SSD (Single Shot MultiBox Detector). RetinaNet. Anchor boxes and non-maximum suppression. Image Segmentation: Semantic segmentation (U-Net, SegNet). Instance segmentation (Mask R-CNN). Panoptic segmentation. Fully Convolutional Networks (FCNs). CRFs (Conditional Random Fields). Artificial Intelligence

Step 5: Deep Learning for Computer Vision Convolutional Neural Networks (CNNs): Convolutional layers. Pooling layers. Fully connected layers. Activation functions (ReLU, Sigmoid, Tanh). Batch normalization and dropout. Advanced CNN Architectures: AlexNet and VGGNet. ResNet (Residual Networks). Inception and GoogLeNet. DenseNet (Densely Connected Networks). MobileNet and EfficientNet. Artificial Intelligence

Step 4: Machine Learning for Computer Vision Classical Machine Learning Techniques: K-Nearest Neighbors (KNN). Support Vector Machines (SVM). Decision Trees and Random Forests. Naive Bayes. Clustering (K-means, DBSCAN). Dimensionality Reduction: Principal Component Analysis (PCA). Linear Discriminant Analysis (LDA). t-SNE (t-Distributed Stochastic Neighbor Embedding). Independent Component Analysis (ICA). Feature selection techniques. Artificial Intelligence

Step 3: Feature Extraction Traditional Feature Detectors: Edge detection (Sobel, Canny). Corner detection (Harris, Shi-Tomasi). Blob detection (LoG, DoG). SIFT and SURF features. ORB features. Image Segmentation: Thresholding. Watershed algorithm. Contours and shape detection. Region growing. Graph-based segmentation. Artificial Intelligence

Step 2: Basic Image Processing Image Manipulation with OpenCV: Reading, displaying, and saving images. Basic operations (resizing, cropping, rotating). Image filtering (blurring, sharpening, edge detection). Handling image channels and color spaces. Image Manipulation with PIL and Scikit-Image: Image enhancement techniques. Histogram equalization. Geometric transformations. Image segmentation (thresholding, watershed). Artificial Intelligence

Introduction to Computer Vision: Understanding images and pixels. Grayscale and color images. Basic image processing operations. Image formats and conversions. Mathematics for Computer Vision: Linear algebra (matrices, vectors, transformations). Calculus (derivatives, gradients). Probability and statistics (distributions, Bayes theorem). Fourier transforms and convolutions. Eigenvalues and eigenvectors. Artificial Intelligence

Before we start, what is computer vision and what do computer vision engineers do? Computer vision is a field of AI that enables machines to interpret and understand visual data from the world, such as images and videos. Computer vision engineers develop algorithms and systems to automate tasks like image classification, object detection, and image segmentation, transforming visual data into actionable insights for various applications including healthcare, autonomous driving, and security. Artificial Intelligence

Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go: 1. Computer vision 2. Natural language processing. I outlined a roadmap for computer vision I believe many beginners will find helpful.