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Free Download TensorFlow for Deep Learning - Build AI Models Like a Pro Published 9/2024 Created by Deepak Rai MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 13 Lectures ( 2h 31m ) | Size: 1.93 GB Master the Art of Building and Deploying Advanced AI Models Using TensorFlow - From Beginner to Expert What you'll learn: Master TensorFlow basics, from setup to building and deploying deep learning models. Create neural networks for tasks like image recognition and text generation Build advanced models like CNNs, RNNs, and LSTMs using TensorFlow. Optimize model performance for faster, more accurate AI solutions. Learn how to deploy AI models into production environments efficiently. Requirements: Requirements and Prerequisites for Taking This Course: Basic Python knowledge: Familiarity with Python programming is recommended as we'll use Python to implement TensorFlow models. Fundamental understanding of machine learning: Prior knowledge of basic machine learning concepts like supervised learning and classification will be helpful. Interest in AI and deep learning: No prior deep learning or TensorFlow experience is required, but a strong interest in learning AI technologies is key. A computer with internet access: You'll need a computer to install TensorFlow, run code, and follow along with the hands-on exercises. Eagerness to learn and apply new skills: This course is designed for students ready to dive into practical deep learning with real-world applications. This ensures students are prepared while also welcoming beginners with the basics covered during the course. Description: Unlock the full potential of deep learning with TensorFlow, the leading open-source framework for building cutting-edge AI models. In this hands-on course, you'll learn how to master TensorFlow and create powerful artificial intelligence solutions, from basic concepts to advanced applications.Whether you're a beginner looking to dive into deep learning or an experienced developer aiming to sharpen your skills, this course is designed for you. With easy-to-follow lessons, real-world projects, and expert guidance, you'll build, train, and deploy neural networks for image recognition, natural language processing, and more.Key Highlights:Learn TensorFlow step-by-step - from installation to deploying AI models.Build your first neural network and progress to advanced architectures like CNNs, RNNs, and LSTMs.Hands-on projects that focus on practical applications of AI and machine learning.Explore real-world use cases like image classification, text generation, and predictive analytics.Perfect for beginners and experienced developers alike, no prior TensorFlow experience required.What You Will Learn:Deep learning fundamentals and how they apply to AI development.How to implement state-of-the-art models using TensorFlow.Techniques to optimize model performance and handle large datasets.How to deploy your AI models to production environments.Why This Course?TensorFlow powers the world's most advanced AI systems, and this course equips you with the skills to join this rapidly growing industry. With comprehensive resources and expert instruction, you'll be building AI models like a pro in no time.Enroll today and take the first step toward becoming a TensorFlow expert in deep learning! Who this course is for: Who is This Course For? Aspiring data scientists and AI enthusiasts who want to learn how to build deep learning models using TensorFlow. Software developers and engineers looking to enhance their machine learning and AI skills with hands-on TensorFlow projects. Students and professionals aiming to break into the AI field and develop practical skills for real-world applications. Researchers and analysts seeking to implement advanced neural networks and AI models for their work or projects. Beginners in machine learning who want a comprehensive introduction to TensorFlow and deep learning, no prior experience required. This course is perfect for anyone eager to master TensorFlow and apply it to build powerful AI models. Homepage https://www.udemy.com/course/tensorflow-for-deep-learning-build-ai-models-like-a-pro/ Rapidgator https://rg.to/file/fc1d1298c2f671c13a957635819cd74a/nlare.TensorFlow.for.Deep.Learning.Build.AI.Models.Like.a.Pro.part1.rar.html https://rg.to/file/58987379992cac3e67d66e1d873d0176/nlare.TensorFlow.for.Deep.Learning.Build.AI.Models.Like.a.Pro.part2.rar.html Fikper Free Download https://fikper.com/7Zf3StqxMS/nlare.TensorFlow.for.Deep.Learning.Build.AI.Models.Like.a.Pro.part1.rar.html https://fikper.com/NHPGxZUEXr/nlare.TensorFlow.for.Deep.Learning.Build.AI.Models.Like.a.Pro.part2.rar.html No Password - Links are Interchangeable

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After that you will learn toUse your custom trained Machine Learning Models in AndroidUse existing tensorflow lite models in Android AppsRegressionRegression is one of the fundamental techniques in Machine Learning which can be used for countless applications. Like you can train Machine Learning models using regression to predict the price of the houseto predict the Fuel Efficiency of vehiclesto recommend drug doses for medical conditionsto recommend fertilizer in agriculture to suggest exercises for improvement in player performanceand so on. So Inside this course, you will learn to train your custom linear regression models in Tensorflow Lite format and build smart Android Applications.Image Classification & ApplicationsImage classification is the process of recognizing different entities or things in an image or video. You can recognize animals, plants, diseases, food, activities, colors, things, fictional characters, drinks, etc with image recognition.In e-commerce applications image classification can be used to categorize products based on their visual features, So it is used to organize products into categories for easy browsing.Image classification can be used to power visual search in mobile apps, so users can take a picture of an object and then find similar items for sale.Image classification can be used in medical apps to diagnose disease based on medical images, such as X-rays or CT scans.We can use image classification to build countless recognition applications for performing number of tasks, like we can train a model and build applications to recognizeDifferent Breeds of dogsDifferent Types of plantsDifferent Species of AnimalsDifferent kind of precious stonesImage Classification & ApplicationsObject detection is a powerful computer vision technique that can accurately identify and pinpoint the location of various objects within images or videos. By recognizing objects like cars, people, and animals, this technology empowers applications such as security surveillance, autonomous vehicles, and smartphone apps that can identify objects through the camera lens.Key Applications:Autonomous Vehicles: Cars equipped with object detection can safely navigate roads, avoid collisions, and enhance driver assistance systems.Surveillance Systems: Security cameras can identify individuals, track suspicious activity, and detect intrusions.Retail: Stores can monitor customer behavior, manage inventory, and prevent theft.Healthcare: Medical imaging systems can detect anomalies like tumors and fractures.Agriculture: Farmers can monitor crops, livestock, and detect pests or diseases.Manufacturing: Quality control and automation can be improved through object inspection and robotic guidance.Sports Analytics: Tracking player movements and equipment can enhance performance analysis and fan experience.Environmental Monitoring: Wildlife conservation and habitat protection can benefit from object detection.Smart Cities: Traffic management, public space monitoring, and waste management can be optimized.I'm Muhammad Hamza Asif, and in this course, we'll embark on a journey to combine the power of predictive modeling with the flexibility of Android app development. Whether you're a seasoned Android developer or new to the scene, this course has something valuable to offer youCourse Overview: We'll begin by exploring the basics of Machine Learning and its various types, and then dive into the world of deep learning and artificial neural networks, which will serve as the foundation for training our machine learning models for Android.The Android-ML Fusion: After grasping the core concepts, we'll bridge the gap between Android and Machine Learning. To do this, we'll kickstart our journey with Python programming, a versatile language that will pave the way for our machine learning model trainingUnlocking Data's Power: To prepare and analyze our datasets effectively, we'll dive into essential data science libraries like NumPy, Pandas, and Matplotlib. These powerful tools will equip you to harness data's potential for accurate predictions.Tensorflow for Mobile: Next, we'll immerse ourselves in the world of TensorFlow, a library that not only supports model training using neural networks but also caters to mobile devices, including AndroidRegression Models TrainingTraining Your First Machine Learning Model:Harness TensorFlow and Python to create a simple linear regression modelConvert the model into TFLite format, making it compatible with AndroidLearn to integrate the tflite model into Android apps for AndroidFuel Efficiency Prediction:Apply your knowledge to a real-world problem by predicting automobile fuel efficiencySeamlessly integrate the model into a Android app for an intuitive fuel efficiency prediction experienceHouse Price Prediction in Android:Master the art of training machine learning models on substantial datasetsUtilize the trained model within your Android app to predict house prices confidentlyComputer Vision Model TrainingImage Classification in Android:Collect and process dataset for model trainingTrain image classification models on custom datasets with Teachable MachineTrain image classification models on custom datasets with Transfer LearningUse image classification models in Android with both images and live camera footageObject Detection in AndroidCollect and Annotate Dataset for Object Detection Model TrainingTrain Object Detection ModelsUse object detection models in Android with Images & VideosThe Android Advantage: By the end of this course, you'll be equipped to:Train advanced machine learning models for accurate predictionsSeamlessly integrate tflite models into your Android applicationsAnalyze and use existing regression & vision (ML) models effectively within the Android ecosystemWho Should Enroll:Aspiring Android developers eager to add predictive modeling to their skillsetBeginner Android developer with very little knowledge of mobile app development Intermediate Android developer wanted to build a powerful Machine Learning-based applicationExperienced Android developers wanted to use Machine Learning models inside their applications.Step into the World of Android and Machine Learning: Join us on this exciting journey and unlock the potential of Android and Machine Learning. 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