Month: June 2023

Building SEO Topical Authority: Increase Your Organic Traffic From Search Engines



If you want to rank high on Google and other search engines, you need to build SEO topical authority. Explode your organic search engine traffic by following along with my steps in this video. I will make it very easy for you to understand how to increase your Search Engine Optimization topical authority so you can demonstrate your experience, expertise, and authority on the topics you cover. What is Topical Authority for SEO? Topical authority in SEO refers to the experience, expertise, and credibility that a website has about a specific topic. Credibility is determined by the amount of quality, relevant, and updated content that a website has created about a topic. Questions to Ask about Websites that Rank: How much can you learn about a specific topic from a single website? Which website covers a topic with detailed & accurate information? Why is Topical Authority Important? – Building topical authority can improve your website's visibility in search engine results. – Search engines like Google tend to rank sites higher that demonstrate comprehensive knowledge on a particular topic. For example, a website like RunnersWorld.com has created content about running topics for years. They are much more likely to rank for competitive running keywords than most competitors & new websites. Topical Authority – My 9-Step SEO Strategy: 1 – Pick a topic/keyword to focus on for new content creation
2 – Benchmark my KPI – Google Search Console Clicks & Impressions
3 – Export keywords from Google Search Console
4 – Keyword research using Google Keyword Planner
5 – Create Content Strategy using SEO keyword research list
6 – Create Quality Blog Content based on the topic I am targeting
7 – Create Quality Video Content based on the topic I am targeting
8 – Internal Linking & Optimization of blog and video content
9 – Market Content using social media and email

Commonly Used Minikube Commands (Kubectl – Minikube – Kubernetes)



Discover commonly used Minikube commands for managing and interacting with your local Kubernetes cluster. #Minikube #Kubernetes #Kubectl Minikube is a tool that allows you to run a single-node Kubernetes cluster on your local machine. With Minikube, you can experiment with Kubernetes and test your applications without the need for a full-scale production cluster. In this tutorial, we will introduce you to some commonly used Minikube commands that work in conjunction with kubectl, the Kubernetes command-line tool: 1. Start the Minikube cluster: Use the command to start the Minikube cluster and spin up the virtual machine that hosts your Kubernetes cluster. 2. Stop the Minikube cluster: When you're done with your local cluster, you can stop it using the command. This will free up system resources and pause the cluster. 3. Delete the Minikube cluster: If you want to completely remove the Minikube cluster from your machine, you can delete it using the command. Be cautious as this will permanently delete all associated resources. 4. Access the Kubernetes dashboard: The command launches the Kubernetes dashboard, a web-based user interface that provides insights and control over your Minikube cluster. 5. Configure kubectl to use Minikube: To interact with your Minikube cluster using kubectl, you need to configure kubectl to use the Minikube context. We'll guide you through the necessary steps. 6. View cluster information: Use the command to get information about your Minikube cluster, such as the IP address, status, and running services. 7. Interact with pods and services: We'll demonstrate how to create, list, and manage pods and services within your Minikube cluster using kubectl commands. By familiarizing yourself with these commonly used Minikube commands, you'll have the foundational knowledge to manage and interact with your local Kubernetes cluster effectively. Join us in this tutorial and gain confidence in using Minikube and kubectl to work with Kubernetes on your local machine! #MinikubeCommands #KubernetesCluster #KubectlCommands #MinikubeTips #LocalKubernetes

Meet VSDC 8.2: Proxy file support, color keyframes, vectorscope, and more



The new version of VSDC is already out, and we can't wait to hear what you think about it! VSDC 8.2. introduces several exciting features and enhancements, including:
– Proxy file support
– Color Keyframes
– Vectorscope
– Preview of resources and effects in the Sources window
– Text stretching between two curves In addition to these features, we have made other improvements and optimizations to enhance your video editing experience and ensure smoother performance. Are you excited to explore all these new additions? Download the latest version of VSDC from our website today and start creating stunning videos with ease: https://bit.ly/3OuETFS

Deep Learning for Computer Vision with Python and TensorFlow – Complete Course



Learn the basics of computer vision with deep learning and how to implement the algorithms using Tensorflow. Author: Folefac Martins from Neuralearn.ai
More Courses: www.neuralearn.ai
Link to Code: https://colab.research.google.com/drive/18u1KDx-9683iZNPxSDZ6dOv9319ZuEC_
YouTube Channel: https://www.youtube.com/@neuralearn ⭐️ Contents ⭐️ Introduction
⌨️ (0:00:00) Welcome
⌨️ (0:05:54) Prerequisite
⌨️ (0:06:11) What we shall Learn Tensors and Variables
⌨️ (0:12:12) Basics
⌨️ (0:19:26) Initialization and Casting
⌨️ (1:07:31) Indexing
⌨️ (1:16:15) Maths Operations
⌨️ (1:55:02) Linear Algebra Operations
⌨️ (2:56:21) Common TensorFlow Functions
⌨️ (3:50:15) Ragged Tensors
⌨️ (4:01:41) Sparse Tensors
⌨️ (4:04:23) String Tensors
⌨️ (4:07:45) Variables Building Neural Networks with TensorFlow [Car Price Prediction]
⌨️ (4:14:52) Task Understanding
⌨️ (4:19:47) Data Preparation
⌨️ (4:54:47) Linear Regression Model
⌨️ (5:10:18) Error Sanctioning
⌨️ (5:24:53) Training and Optimization
⌨️ (5:41:22) Performance Measurement
⌨️ (5:44:18) Validation and Testing
⌨️ (6:04:30) Corrective Measures Building Convolutional Neural Networks with TensorFlow [Malaria Diagnosis]
⌨️ (6:28:50) Task Understanding
⌨️ (6:37:40) Data Preparation
⌨️ (6:57:40) Data Visualization
⌨️ (7:00:20) Data Processing
⌨️ (7:08:50) How and Why ConvNets Work
⌨️ (7:56:15) Building Convnets with TensorFlow
⌨️ (8:02:39) Binary Crossentropy Loss
⌨️ (8:10:15) Training Convnets
⌨️ (8:23:33) Model Evaluation and Testing
⌨️ (8:29:15) Loading and Saving Models to Google Drive Building More Advanced Models in Teno Convolutional Neural Networks with TensorFlow [Malaria Diagnosis]
⌨️ (8:47:10) Functional API
⌨️ (9:03:48) Model Subclassing
⌨️ (9:19:05) Custom Layers Evaluating Classification Models [Malaria Diagnosis]
⌨️ (9:36:45) Precision, Recall and Accuracy
⌨️ (10:00:35) Confusion Matrix
⌨️ (10:10:10) ROC Plots Improving Model Performance [Malaria Diagnosis]
⌨️ (10:18:10) TensorFlow Callbacks
⌨️ (10:43:55) Learning Rate Scheduling
⌨️ (11:01:25) Model Checkpointing
⌨️ (11:09:25) Mitigating Overfitting and Underfitting Data Augmentation [Malaria Diagnosis]
⌨️ (11:38:50) Augmentation with tf.image and Keras Layers
⌨️ (12:38:00) Mixup Augmentation
⌨️ (12:56:35) Cutmix Augmentation
⌨️ (13:38:30) Data Augmentation with Albumentations Advanced TensorFlow Topics [Malaria Diagnosis]
⌨️ (13:58:35) Custom Loss and Metrics
⌨️ (14:18:30) Eager and Graph Modes
⌨️ (14:31:23) Custom Training Loops Tensorboard Integration [Malaria Diagnosis]
⌨️ (14:57:00) Data Logging
⌨️ (15:29:00) View Model Graphs
⌨️ (15:31:45) Hyperparameter Tuning
⌨️ (15:52:40) Profiling and Visualizations MLOps with Weights and Biases [Malaria Diagnosis]
⌨️ (16:00:35) Experiment Tracking
⌨️ (16:55:02) Hyperparameter Tuning
⌨️ (17:17:15) Dataset Versioning
⌨️ (18:00:23) Model Versioning Human Emotions Detection
⌨️ (18:16:55) Data Preparation
⌨️ (18:45:38) Modeling and Training
⌨️ (19:36:42) Data Augmentation
⌨️ (19:54:30) TensorFlow Records Modern Convolutional Neural Networks [Human Emotions Detection]
⌨️ (20:31:25) AlexNet
⌨️ (20:48:35) VGGNet
⌨️ (20:59:50) ResNet
⌨️ (21:34:07) Coding ResNet from Scratch
⌨️ (21:56:17) MobileNet
⌨️ (22:20:43) EfficientNet Transfer Learning [Human Emotions Detection]
⌨️ (22:38:15) Feature Extraction
⌨️ (23:02:25) Finetuning Understanding the Blackbox [Human Emotions Detection]
⌨️ (23:15:33) Visualizing Intermediate Layers
⌨️ (23:36:20) Gradcam method Transformers in Vision [Human Emotions Detection]
⌨️ (23:57:35) Understanding ViTs
⌨️ (24:51:17) Building ViTs from Scratch
⌨️ (25:42:39) FineTuning Huggingface ViT
⌨️ (26:05:52) Model Evaluation with Wandb Model Deployment [Human Emotions Detection]
⌨️ (26:27:13) Converting TensorFlow Model to Onnx format
⌨️ (26:52:26) Understanding Quantization
⌨️ (27:13:08) Practical Quantization of Onnx Model
⌨️ (27:22:01) Quantization Aware Training
⌨️ (27:39:55) Conversion to TensorFlow Lite
⌨️ (27:58:28) How APIs work
⌨️ (28:18:28) Building an API with FastAPI
⌨️ (29:39:10) Deploying API to the Cloud
⌨️ (29:51:35) Load Testing with Locust Object Detection with YOLO
⌨️ (30:05:29) Introduction to Object Detection
⌨️ (30:11:39) Understanding YOLO Algorithm
⌨️ (31:15:17) Dataset Preparation
⌨️ (31:58:27) YOLO Loss
⌨️ (33:02:58) Data Augmentation
⌨️ (33:27:33) Testing Image Generation
⌨️ (33:59:28) Introduction to Image Generation
⌨️ (34:03:18) Understanding Variational Autoencoders
⌨️ (34:20:46) VAE Training and Digit Generation
⌨️ (35:06:05) Latent Space Visualization
⌨️ (35:21:36) How GANs work
⌨️ (35:43:30) The GAN Loss
⌨️ (36:01:38) Improving GAN Training
⌨️ (36:25:02) Face Generation with GANs Conclusion
⌨️ (37:15:45) What's Next