Beginner PyTorch

This course focuses on the foundational concepts of LL Ms, specifically tokenization and word embedding models. It includes practical, hands-on exercises for building and training these models using PyTorch.

Are you looking to master Data Science,Machine Learning (ML), Deep Learning(DL) and Natural Language Processing (NLP) from the ground up? This comprehensive course is designed to take you on a journey from understanding the basics to mastering advanced concepts, all while providing practical insights and hands-on experience.What You'll Learn:Foundational Concepts: Start with the basics of ML and NLP, including algorithms, models, and techniques used in these fields. Understand the core principles that drive machine learning and natural language processing.Advanced Topics: Dive deeper into advanced topics such as deep learning, reinforcement learning, and transformer models. Learn how to apply these concepts to build more complex and powerful models.Practical Applications: Gain practical experience by working on real-world projects and case studies. Apply your knowledge to solve problems in various domains, including healthcare, finance, and e-commerce.Mathematical Foundations: Develop a strong mathematical foundation by learning the math behind ML and NLP algorithms. Understand concepts such as linear algebra, calculus, and probability theory.Industry-standard Tools: Familiarize yourself with industry-standard tools and libraries used in ML and NLP, including TensorFlow, PyTorch, and Scikit-Learn. Learn how to use these tools to build and deploy models.Optimization Techniques: Learn how to optimize ML and NLP models for better performance and efficiency. Understand techniques such as hyperparameter tuning, model selection, and model evaluation.Who Is This Course For:This course is suitable for anyone interested in learning machine learning and natural language processing, from beginners to advanced learners. Whether you're a student, a professional look

Dive into Computer Vision with PyTorch: Master Deep Learning, CNNs, and GPU Computing for Real-World Applications - 2024 Edition"Unlock the potential of Deep Learning in Computer Vision, where groundbreaking advancements shape the future of technology. Explore applications ranging from Facebook's image tagging and Google Photo's People Recognition to fraud detection and facial recognition. Delve into the core operations of Deep Learning Computer Vision, including convolution operations on images, as you master the art of extracting valuable information from digital images.In this comprehensive course, we focus on one of the most widely used Deep Learning frameworks – PyTorch. Recognized as the go-to tool for Deep Learning in both product prototypes and academia, PyTorch stands out for its Pythonic nature, ease of learning, higher developer productivity, dynamic approach for graph computation through Auto Grad, and GPU support for efficient computation.Why PyTorch?Pythonic: PyTorch aligns seamlessly with the Python programming language, offering a natural and intuitive experience for learners.Easy to Learn: The simplicity of PyTorch makes it accessible for beginners, allowing a smooth learning curve.Higher Developer Productivity: PyTorch's design prioritizes developer productivity, promoting efficiency in building and experimenting with models.Dynamic Approach for Graph Computation - Auto Grad: PyTorch's dynamic computational graph through Auto Grad enables flexible and efficient model development.GPU Support: PyTorch provides GPU support for accelerated computation, enhancing performance in handling large datasets and complex models.Course Highlights:Gain a foundational understanding of PyTorch, essential for delving into the world of Deep Learning.Learn GPU programming and explore how to access free GPU resources for efficient learning.Master the Auto

Are you ready to unlock the full potential of Deep Learning and AI by mastering not just one but multiple tools and frameworks? This comprehensive course will guide you through the essentials of Deep Learning using Python, PyTorch, and TensorFlow—the most powerful libraries and frameworks for building intelligent models.Whether you're a beginner or an experienced developer, this course offers a step-by-step learning experience that combines theoretical concepts with practical hands-on coding. By the end of this journey, you'll have developed a deep understanding of neural networks, gained proficiency in applying Deep Neural Networks (DNNs) to solve real-world problems, and built expertise in cutting-edge deep learning applications like Convolutional Neural Networks (CNNs) and brain tumor detection from MRI images.Why Choose This Course?This course stands out by offering a comprehensive learning path that merges essential aspects from three leading frameworks: Python, PyTorch, and TensorFlow. With a strong emphasis on hands-on practice and real-world applications, you'll quickly advance from fundamental concepts to mastering deep learning techniques, culminating in the creation of sophisticated AI models.Key Highlights:Python: Learn Python from the basics, progressing to advanced-level programming essential for implementing deep learning algorithms.PyTorch: Master PyTorch for neural networks, including tensor operations, optimization, autograd, and CNNs for image recognition tasks.TensorFlow: Unlock TensorFlow's potential for creating robust deep learning models, utilizing tools like Tensorboard for model visualization.Real-world Projects: Apply your knowledge to exciting projects like IRIS classi

Unlock the power of artificial intelligence with our comprehensive course, "Deep Learning with Python ." This course is designed to transform your understanding of machine learning and take you on a journey into the world of deep learning. Whether you're a beginner or an experienced programmer, this course will equip you with the essential skills and knowledge to build, train, and deploy deep learning models using Python and PyTorch. Deep learning is the driving force behind groundbreaking advancements in generative AI, robotics, natural language processing, image recognition, and artificial intelligence. By enrolling in this course, you’ll gain practical knowledge and hands-on experience in applying Python skills to deep learning Course Outline Introduction to Deep Learning Understanding the paradigm shift from machine learning to deep learning Key concepts of deep learning Setting up the Python environment for deep learning Artificial Deep Neural Networks: Coding from Scratch in Python Fundamentals of artificial neural networks Building and training neural networks from scratch Implementing forward and backward propagation Optimizing neural networks with gradient descent Deep Convolutional Neural Networks: Coding from Scratch in Python Introduction to convolutional neural networks (CNNs)Building and training CNNs from scratch Understanding convolutional layers, pooling, and activation functions Applying CNNs to image data Transfer Learning with Deep Pretrained Models using Python Concept of transfer learning and its benefits Using pretrained models for new tasks Fine-tuning and adapting pretrained models Practical applications of

Unlock the potential of Generative AI with our comprehensive course, "Mastering Generative AI: LL Ms, Prompt Engineering & More." This course is designed for both beginners and seasoned developers looking to deepen their understanding of the rapidly evolving field of artificial intelligence.In this course, you will explore a wide range of essential topics, including:· Python Programming: Learn the fundamentals of Python, the go-to language for AI development, and become proficient in data manipulation using libraries like Pandas and Num Py.· Natural Language Processing (NLP): Dive into the world of NLP, mastering techniques for text processing, feature extraction, and leveraging powerful libraries like NLTK and Spa Cy.· Deep Learning and Transformers: Understand the architecture of Transformer models, which are at the heart of many state-of-the-art AI applications. Discover the principles of deep learning and how to implement neural networks using TensorFlow and PyTorch.· Large Language Models (LL Ms): Gain insights into LL Ms, their training, and fine-tuning processes. Learn how to effectively use these models in various applications, from chatbots to content generation.· Retrieval-Augmented Generation (RAG): Explore the innovative concept of RAG, which combines retrieval techniques with generative models to enhance AI performance.· Prompt Engineering: Master the art of crafting effective prompts to improve the interaction with LL Ms and optimize the output for specific tasks.· Vector Databases: Discover how to implement and utilize vector databases for storing and retrieving high-dimensional data, a crucial skill in managing AI-generated content.The course culminates in a Capstone Project, where you will apply everything you've learned to solve a real-world problem using Generative AI te

Welcome to the Deep Learning Fundamentals course on Udemy! Are you ready to unlock the power of neural networks and delve into the exciting world of artificial intelligence? Look no further! This comprehensive course is designed to equip you with the essential knowledge and practical skills needed to become proficient in both TensorFlow and PyTorch based deep learning together!Deep learning has revolutionized the field of AI, enabling machines to learn from vast amounts of data and make accurate predictions, recognize patterns, and perform complex tasks. In this course, we will demystify the concepts behind deep learning and guide you through hands-on exercises to build and train your neural networks.Here's an overview of what you'll learn:Introduction to Deep Learning:Understand the fundamentals of artificial neural networks.Explore the history and evolution of deep learning.Gain insights into real-world applications and their impact.Neural Networks and Architectures:Study the structure and functioning of artificial neurons.Learn about various neural network architectures, including feedforward, convolutional, and recurrent networks.Explore activation functions, weight initialization, and regularization techniques.Building Deep Learning Models:Implement deep learning models using popular frameworks such as TensorFlow or PyTorch.Understand the process of data preprocessing, including feature scaling and one-hot encoding.Design effective training and validation sets for model evaluation.Training Neural Networks:Grasp the concept of backpropagation and how it enables model training.Explore optimization algorithms like stochastic gradient descent (SGD) and Adam.Learn techniques to prevent overfitting, such as dropout and ea

Welcome to the course "Modern Computer Vision & Deep Learning with Python & PyTorch"! Imagine being able to teach computers to see just like humans. Computer Vision is a type of artificial intelligence (AI) that enables computers and machines to see the visual world, just like the way humans see and understand their environment. Artificial intelligence (AI) enables computers to think, where Computer Vision enables computers to see, observe and interpret. This course is particularly designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to major Computer Vision problems including Image Classification, Semantic Segmentation, Instance Segmentation, and Object Detection. In this course, you'll start with an introduction to the basics of Computer Vision and Deep Learning, and learn how to implement, train, test, evaluate and deploy your own models using Python and PyTorch for Image Classification, Image Segmentation, and Object Detection. Computer Vision plays a vital role in the development of autonomous vehicles. It enables the vehicle to perceive and understand its surroundings to detect and classify various objects in the environment, such as pedestrians, vehicles, traffic signs, and obstacles. This helps to make informed decisions for safe and efficient vehicle navigation. Computer Vision is used for Surveillance and Security using drones to track suspicious activities, intruders, and objects of interest. It enables real-time monitoring and threat detection in public spaces, airports, banks, and other security-sensitive areas. Today Computer Vision applications in our daily life are very common including Face Detection in cameras and cell phones, logging in to devices with fingerprints and face recognition, interactive games, MRI, CT scans, image guided surgery and much more. This comprehensive course is especially designed to give you hands-on experience using Python and PyTorch coding to build, train, test and deploy

Course Contents Deep Learning and revolutionized Artificial Intelligence and data science.  Deep Learning teaches computers to process data in a way that is inspired by the human brain.This is complete and comprehensive course on deep learning. This course covers the theory and intuition behind deep learning models and then implementing all the deep learning models both in PyTorch and TensorFlow.Practical Oriented explanations Deep Learning Models with implementation both in PyTorch and TensorFlow.No need of any prerequisites. I will teach you everything from scratch.Job Oriented Structure Sections of the Course· Introduction of the Course· Introduction to Google Colab· Python Crash Course· Data Preprocessing· Regression Analysis· Logistic Regression· Introduction to Neural Networks and Deep Learning· Activation Functions· Loss Functions· Back Propagation· Neural Networks for Regression Analysis· Neural Networks for Classification· Dropout Regularization and Batch Normalization· Optimizers· Adding Custom Loss Function and Custom Layers to Neural Networks· Convolutional Neural Network (CNNs)· One Dimensional CNNs· Setting Early Stopping Criterion in CNNs· Recurrent Neural Network (RNNs)· Long Short-Term Memory (LSTMs) Network· Bidirectional LSTMs· Generative Adversarial Network (GANs)· DCGA Ns· Autoencoders· LSTMs Autoencoders· Variational Autoencoders· Neural Style Transfer· Transformers· Vision Transformer· Time Series Transformers. K-means Clustering. Principle Component Analysis.  Deep Learning Models with implementation both in PyTorch and TensorFlow.

Unlock the potential of Generative AI with our comprehensive course, "Gen AI Masters 2025 - From Python To LL Ms and Deployment" This course is designed for both beginners and seasoned developers looking to deepen their understanding of the rapidly evolving field of artificial intelligence.Learn how to build Generative AI applications using Python and LL Ms. Understand prompt engineering, explore vector databases like FAISS, and deploy real-world AI chatbots using RAG architecture.In this course, you will explore a wide range of essential topics, including:Python Programming: Learn the fundamentals of Python, the go-to language for AI development, and become proficient in data manipulation using libraries like Pandas and Num Py.Natural Language Processing (NLP): Dive into the world of NLP, mastering techniques for text processing, feature extraction, and leveraging powerful libraries like NLTK and Spa Cy.Deep Learning and Transformers: Understand the architecture of Transformer models, which are at the heart of many state-of-the-art AI applications. Discover the principles of deep learning and how to implement neural networks using TensorFlow and PyTorch.Large Language Models (LL Ms): Gain insights into LL Ms, their training, fine-tuning processes (including PEFT, LoRA, and QLoRA), and learn how to effectively use these models in various applications, from chatbots to content generation.Retrieval-Augmented Generation (RA Gs): Explore the innovative concept of RAG, which combines retrieval techniques with generative models to enhance AI performance. You'll also learn about RAG evaluation methods, including the RAGAS framework, BLEU, ROUGE, BAR Score, and BERT Score.Prompt Engineering</str

Welcome to the era of Artificial Intelligence, where everything is rapidly evolving. In this dynamic era, it's crucial to enhance your skills by acquiring the most essential, cutting-edge knowledge that is currently in high demand in the market: Artificial Intelligence. This course takes you on a comprehensive learning journey, delving into the most advanced concepts in AI, such as Computer Vision Generative A.IRNN Variational Autoencoder PyTorch With Python and C++Numpy and Pandas And lot of more things..There are numerous cutting-edge concepts in high demand at the moment. I am formerly engaged in the Trustline security limited organization, where we harness real-world experience to create resilient AI solutions. I leverage this experience to instruct you on crafting advanced, industry-ready, robust A.I.In this course, we embark on a journey to develop AI across various domains, including stock market analysis, human face generation, image classification, and more. This course not only reinforces your programming and mathematical fundamentals but also equips you to build AI solutions in two distinct languages: Python and C++. This proficiency in both languages is a rare and valuable asset in the deep learning space.Furthermore, we explore best practices that enable the systematic creation of AI solutions. We delve into the theory of MLOPS (Machine Learning Operations), enhancing your capabilities and making your talents shine brightly in the competitive AI market.We also explore how Chat GPT LLM can enhance and expedite our AI development in the realm of Data Science. This section is particularly engaging, as Chat GPT serves as a valuable assistant in addressing repetitive and logic-free tasks, making our AI journey even more exciting and efficient.At the

This course is designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to Semantic Image Segmentation problems. Are you ready to take your understanding of deep learning to the next level and learn how to apply it to real-world problems? In this course, you'll learn how to use the power of Deep Learning to segment images and extract meaning from visual data. You'll start with an introduction to the basics of Semantic Segmentation using Deep Learning, then move on to implementing and training your own models for Semantic Segmentation with Python and PyTorch.This course is designed for a wide range of students and professionals, including but not limited to:Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Image Segmentation tasks Computer Vision Engineers and Researchers who want to learn how to use PyTorch to build and train Deep Learning models for Semantic Segmentation Developers who want to incorporate Semantic Segmentation capabilities into their projects Graduates and Researchers in Computer Science, Electrical Engineering, and other related fields who want to learn about the latest advances in Deep Learning for Semantic Segmentation In general, the course is for Anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Semantic Segmentation using Python and PyTorch The course covers the complete pipeline with hands-on experience of Semantic Segmentation using Deep Learning with Python and PyTorch as follows:Semantic Image Segmentation and its Real-World Applications in Self Driving Cars or Autonomous Vehicles etc.De

Are you interested in unlocking the full potential of Artificial Intelligence? Do you want to learn how to create powerful image recognition systems that can identify objects with incredible accuracy? If so, then our course on Deep Learning with Python for Image Classification is just what you need! In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models and Transfer Learning. Image Classification is a computer vision task to recognize an input image and predict a single-label or multi-label for the image as output using Machine Learning techniques. Embark on a journey into the fascinating world of deep learning with Python and PyTorch, tailored specifically for image classification tasks. In this hands-on course, you'll delve deep into the principles and practices of deep learning, mastering the art of building powerful neural networks to classify images with remarkable accuracy. From understanding the fundamentals of convolutional neural networks&nbsp; to implementing advanced techniques using PyTorch, this course will equip you with the knowledge and skills needed to excel in image classification projects.Deep learning has emerged as a game-changer in the field of computer vision, revolutionizing image classification tasks across various domains. Understanding how to leverage deep learning frameworks like PyTorch to classify images is crucial for professionals and enthusiasts alike. Whether you're a data scientist, software engineer, researcher, or student, proficiency in deep learning for image classification opens doors to a wide range of career opportunities. Moreover, with the exponential growth of digital imagery in fields such as healthcare, autonomous vehicles, agriculture, and more, the demand for experts in image classification continues to soar.Course Breakdown:You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models. </l

This course is diving into Generative AI State-Of-Art Scientific Challenges. It helps to uncover ongoing problems and develop or customize your Own Large Models Applications. Course mainly is suitable for any candidates(students, engineers,experts) that have great motivation to Large Language Models with Todays-Ongoing Challenges as well as their&nbsp; deeployment with Python Based and Javascript Web Applications, as well as with C/C++ Programming Languages. Candidates will have deep knowledge on&nbsp; TensorFlow , PyTorch,&nbsp; Keras models, Hugging Face with Docker Service. In addition, one will be able to optimize and quantize TensorRT frameworks for deployment in variety of sectors. Moreover, They will learn deployment of LLM quantized model to Web Pages developed with React, Javascript and FLASK Here you will also learn how&nbsp; to integrate Reinforcement Learning(PPO) to Large Language Model, in order to fine them with Human Feedback based.&nbsp; Candidates will learn how to code and debug in C/C++ Programming languages at least in intermediate level.LLM Models used: The Falcon, LLAMA2, BLOOM, MPT, Vicuna,FLAN-T5, GPT2/GPT3, GPT&nbsp;NEOXBERT 101, Distil BERTFINE-Tuning Small Models under supervision of BIG Models Image Generation :LLAMA models Gemini Dall-E OpenAI Hugging Face Models Learning and Installation of Docker from scratch Knowledge of Javscript, HTML ,CSS, Bootstrap React Hook,&nbsp;DOM and Javacscript Web Development Deep Dive on

In this comprehensive course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNNs) and object detection models using TensorFlow and PyTorch. This course is designed to equip you with the skills required to build robust computer vision applications from scratch.What You Will Learn Throughout this course, you will gain expertise in:Introduction to Computer Vision Understanding image data and its structure.Exploring pixel values, channels, and color spaces.Learning about OpenCV for image manipulation and preprocessing.Deep Learning Fundamentals for Computer Vision Introduction to Neural Networks and Deep Learning concepts.Understanding backpropagation and gradient descent.Key concepts like activation functions, loss functions, and optimization techniques.Convolutional Neural Networks (CNNs)Introduction to CNNs architecture and its components.Understanding convolution layers, pooling layers, and fully connected layers.Implementing CNNs models using TensorFlow and PyTorch.Data Augmentation and Preprocessing Techniques for improving model performance through data augmentation.Using libraries like imgaug, Albumentations, and TensorFlow Data Pipeline.Transfer Learning for Computer Vision Utilizing pre-trained models such as Res Net, VGG, and Efficient Net.Fine-tuning and optimizing transfer learning models.Object Detection Models Exploring object detection algorithms like:YOLO (You Only Look Once)Faster R-CNNs Implement

Have you ever watched AI automatically classify images or detect spam and thought, “I wish I could do that”? Have you ever wondered how a spam filter works? Or do you want to master Deep Learning in a hands-on way?&nbsp;With this course, you’ll learn how to build and deploy your own deep learning models in just 15 days - gaining practical, hands-on experience every step of the way.Why This Course?From day one, you’ll get comfortable with the essential concepts that power modern AI. No fluff, no endless theory - you'll learn by building real-world projects like Spam filters, or image detections. By the end, you won’t just know what neurons and neural networks are - you’ll be able to train, refine, and apply them to projects that truly matter.Who Is This Course For?Absolute beginners eager to break into the world of AI and deep learning.Data enthusiasts who want to strengthen their portfolios with hands-on projects.Developers and data scientists looking to deepen their PyTorch and model deployment skills.Anyone who craves a clear roadmap to mastering deep learning, one day at a time.What Makes This Course Unique?Day-by-Day Progression: Follow a structured, 15-day plan that ensures you never feel lost or overwhelmed.Real-World Projects: Predict used car prices, detect spam in SMS, classify handwritten digits, recognize fashion items—all using deep learning techniques.Modern Tools &amp; Frameworks: Master industry-standard tools like PyTorch and dive into CNNs, transfer learning with Res Net, and more.Practical Deployment: Learn how to turn your trained models into interactive apps with Gradio, making your projects truly come alive.By the End of This Course, You Will:Confid

Deep learning has become one of the most popular machine learning techniques in recent years, and PyTorch has emerged as a powerful and flexible tool for building deep learning models. In this course, you will learn the fundamentals of deep learning and how to implement neural networks using PyTorch.Through a combination of lectures, hands-on coding sessions, and projects, you will gain a deep understanding of the theory behind deep learning techniques such as deep Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs). You will also learn how to train and evaluate these models using PyTorch, and how to optimize them using techniques such as stochastic gradient descent and backpropagation. During the course, I will also show you how you can use GPU instead of CPU and increase the performance of the deep learning calculation.In this course, I will teach you everything you need to start deep learning with PyTorch such as:Num Py Crash Course Pandas Crash Course Neural Network Theory and Intuition How to Work with Torchvision datasets Convolutional Neural Network (CNNs)Long-Short Term Memory (LSTMs)and much more Since this course is designed for all levels (from beginner to advanced), we start with basic concepts and preliminary intuitions.By the end of this course, you will have a strong foundation in deep learning with PyTorch and be able to apply these techniques to various real-world problems, such as image classification, time series analysis, and even creating your own deep learning applications.

Do you want to master Num Py and unlock your potential in data science? This course is your comprehensive, hands-on introduction to the foundational library of modern Python computing!Num Py is the absolute core building block for essential data science and machine learning libraries like Pandas, Scikit-Learn, and PyTorch. By mastering it, you gain the technical edge needed for advanced topics like linear algebra, image processing, and fast numerical computations. If you want to start a career in Data Science or understand the engine behind Machine Learning in Python, this course is for you.What You'll Master in this Hands-On Python Course:This course will teach you everything you need to professionally use Num Py for scientific computing. We start with the basics and rapidly move into advanced techniques crucial for complex data science tasks.Foundation: Introduction to Num Py arrays, N-dimensional arrays, and the fundamental concepts of vectors and matrices.Data Analysis Tools: Leverage Universal Functions (ufuncs), Randomness, and Statistics to analyze and explore data efficiently in Python.Linear Algebra for ML: Master Basic and Advanced Linear Algebra operations, which are the backbone of all Machine Learning algorithms.Advanced Techniques: Understand Broadcasting and Advanced Indexing to write fast, memory-efficient Python code.Real-World Scientif

Growing Importance of Deep Learning Deep learning underpins a lot of important and increasingly important applications today ranging from facial recognition, to self-driving cars, to medical diagnostics and more. Made for Anyone Although many courses are very mathematical or too practical in nature, this course strikes a careful balance between the two to provide a solid foundation in deep learning for you to explore further if you are interested in research in the field of deep learning and/or applied deep learning. It is purposefully made for anyone without a strong background in mathematics. And for those with a strong background, it would accelerate your learning in understanding the different models in deep learning. Code As You Learn This entire course is delivered in a Python Notebook such that you can follow along the videos and replicate the results. You can practice and tweak the models until you truly understand every line of code as we go along. I highly recommend you to type every line of code when you are listening to the videos as this will help a lot in getting used to the syntax. Gradual Learning Style The thing about many guides out there is that they lack the transition from the very basics and people often get lost or miss out vital links that are critical in understanding certain models. Because of this, you can see how every single topic is closely linked with one another. In fact, at the beginning of every topic from logistic regression, I take the time to carefully explain how one model is simply a modification from the previous. That is the marvel of deep learning, we can trace back some part of it to linear regression where we will start. Diagram-Driven Code This course uses more than 100 custom-made diagrams where I took hundreds of hours to carefu

Preparing for a deep learning certification can feel overwhelming, especially with the wide range of neural network concepts, frameworks, and exam-style questions you need to master. This exam prep course is designed to help you build confidence, sharpen your knowledge, and get exam-ready with structured practice.Unlike generic tutorials, this course is focused on exam preparation. You’ll review the essential foundations of neural networks, dive into advanced architectures, and practice applying your skills across major frameworks such as TensorFlow and PyTorch. Each module is carefully aligned with the topics most commonly assessed in certification exams.By the end of this course, you will not only reinforce your theoretical understanding but also practice solving question styles that mirror real exam challenges. While this is not an official certification product, it provides the structure, depth, and practice environment you need to approach the test with clarity.What you’ll gain from this course:Comprehensive coverage of key deep learning concepts and frameworks Practice-based learning through 134 exam-style questions across 4 modules Clarity on architectures such as CNNs, RNNs, LSTMs, and Transformers Hands-on readiness with TensorFlow and PyTorch fundamentals Awareness of exam strategies to manage time, avoid common pitfalls, and improve accuracy Who is this course for?Learners preparing for deep learning certification exams Professionals aiming to validate their AI/ML knowledge Students who want structured revision in neural networks and frameworks Important Note: This is not an official certification course and is not affiliated with any certifyin

Master Deep Learning and Computer Vision: From Foundations to Cutting-Edge Techniques Elevate your career with a comprehensive deep dive into the world of machine learning, with a focus on object detection, image classification, and object tracking.This course is designed to equip you with the practical skills and theoretical knowledge needed to excel in the field of computer vision and deep learning. You'll learn to leverage state-of-the-art techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and advanced object detection models like YOL Ov8.Key Learning Outcomes:Fundamental Concepts:Grasp the core concepts of machine learning and deep learning, including supervised and unsupervised learning.Understand the mathematical foundations of neural networks, such as linear algebra, calculus, and probability theory.Computer Vision Techniques:Master image processing techniques, including filtering, noise reduction, and feature extraction.Learn to implement various object detection models, such as YOL Ov8, Faster R-CNNs, and SSD.Explore image classification techniques, including CNNs architectures like Res Net, Inception, and Efficient Net.Dive into object tracking algorithms, such as SORT, DeepSORT, and Kalman filtering.Practical Projects:Build real-world applications, such as license plate recognition, traffic sign detection, and sports analytics.Gain hands-on experience with popular deep learning frameworks like TensorFlow and PyTorch.Learn to fine-tune pre-trained models and train custom models for specific tasks.Why Choose This Course?Expert Instruction: Learn from experienced ins

PyTorch&amp;Hugginface Deep Learning Course(Colab Hands-On) Welcome to PyTorch Deep Learning From Zero To Hero Series.If you have already mastered the basic syntax of python and don't know what to do next, this course will be a rocket booster to skyrocket your programming skill to a business applicable level.In this course, you will be able to master implementing deep neural network from the very beginning(simple perceptron) to BERT transfer learning/Google's T5 by using PyTorch and Hugging Face yourself by colab. Each Section will have one assignment for you to think and code yourself. The Agenda is below.&nbsp; &nbsp;Agenda:Introduction Google Colaboratory Neuron Perceptron Make Your Perceptron Trainable Normalize Data Activation Function Loss Function Gradient Descent Elegant PyTorch Gradient Descent Final Project Final Project Explained Multi Layer Perceptron(MLPs)One Hot Encoding Prepare data for MLPs Define MLPs Train &amp; Evaluate MLPs Final Project for MLPFCNN ExplainedFCNN LOVE Letters Classification using MLPs Final Project For FCNNCNN ExplainedCNN Prepare data(Fashion MNIST) CNNs Define Model CNNs Train&amp;Evaluate ModelCNN Inference Final Project For CNNRNN ExplainedRNN Prepare dataRNN Define ModelRNN Train ModelRNN InferenceBERT Sesame StreetBERT Prepare Data IMDBBERT Model definitionBERT Model TrainingBERT Model Evaluation<p

Latest update: I will show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab.This course is a complete guide for setting up TensorFlow object detection api, Transfer learning and a lot moreI think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.Here is the details about the project.Here we will star from colab understating because that will help to use free GPU provided by google to train up our model.We’re going to bridge the gap between the basic CNNs architecture you already know and love, to modern, novel architectures such as Res Net, and Inception.We will understand object detection modules in detail using both TensorFlow object detection api as well as YOLO algorithms.We’ll be looking at a state-of-the-art algorithm called RESNET and Mobile NetV2 which is both faster and more accurate than its predecessors.One best thing is you will understand the core basics of CNNs and how it converts to object detection slowly.I hope you’re excited to learn about these advanced applications of CNNs Yolo and TensorFlow, I’ll see you in class!AMAGING FACTS:· This course give’s you full hand’s on experience of training models in colab GPU.· Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math.· Another result? No complicated low-level code such as that written in TensorFlow, Theano,YOLO, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.

Are you interested in Artificial Intelligence (AI), Machine Learning and Artificial Neural Network?Are you afraid of getting started with Deep Learning because it sounds too technical?Have you been watching Deep Learning videos, but still don’t feel like you “get” it?I’ve been there myself! I don’t have an engineering background. I learned to code on my own. But AI still seemed completely out of reach.This course was built to save you many months of frustration trying to decipher Deep Learning. After taking this course, you’ll feel ready to tackle more advanced, cutting-edge topics in AI.In this course:We assume as little prior knowledge as possible. No engineering or computer science background required (except for basic Python knowledge). You don’t know all the math needed for Deep Learning? That’s OK. We'll go through them all together - step by step.We'll "reinvent" a deep neural network so you'll have an intimate knowledge of the underlying mechanics. This will make you feel more comfortable with Deep Learning and give you an intuitive feel for the subject.We'll also build a basic neural network from scratch in PyTorch and PyTorch Lightning and train an MNIST model for handwritten digit recognition.After taking this course:You’ll finally feel you have an “intuitive” understanding of Deep Learning and feel confident expanding your knowledge further.If you go back to the popular courses you had trouble understanding before (like Andrew Ng's courses or Jeremy Howards' fast.ai course), you’ll be pleasantly surprised at how much more you can understand.You'll be able to understand

Computer Vision With Deep Learningرؤية الكمبيوتر باستخدام التعلم العميقDescription This is a complete course that will prepare you to work in Computer Vision Using Deep Learning. We will cover the fundamentals of Deep Learning/ computer Vision and its applications, this course is designed to reduce the time for the learner to Learn Computer Vision using Deep learning.هذه دورة كاملة ستعدك للعمل في رؤية الكمبيوتر باستخدام التعلم العميق. سنغطي أساسيات التعلم العميق/رؤية الكمبيوتر وتطبيقاتها، وقد تم تصميم هذه الدورة لتقليل الوقت الذي يستغرقه المتعلم لتعلم رؤية الكمبيوتر باستخدام التعلم العميق.What Skills will you Learn:In this course, you will learn the following skills:Understand the Math behind Deep Learning Algorithms.Understand How computer vision Algorithms works.Write and build computer vision Algorithms using Deep learning technologies.Use opensource libraries.We will cover:Fundamentals of Computer Vision.Image Preprocessing.Deep Neural Network (DNNs) - PyTorch . Convolutional Neural Network (CNNs)- TensorFlow.Semantic Segmentation.Object Detection.Instance Segmentation.Pose Estimation.Generative AI.Face Recognition.If you do not have prior experience in Machine Learning OR&nbsp;Computer vision, that's NO PROBLEM!. This course is complete and concise, covering the fundamental Theory and needed coding knowledge. Let's work together to learn Computer Vision Using Deep Learning.إذا لم تكن لديك خبرة سابقة في التعلم الآلي أو رؤية الك

Dans ce cours accéléré, nous allons aborder les opportunités qu'offrent les modèles génératifs et ensuite, nous nous intéresserons plus particulièrement aux Generative Adversarial Networks (GANs). Je vais vous expliquer le fonctionnement des GANs de manière intuitive et ensuite, nous nous plongerons dans l'article qui les a introduit en 2014 (Ian J. Goodfellow et al.). Je vous expliquerai donc de manière mathématique le fonctionnement des GANs, ce qui vous permettra d'avoir les bases nécessaires pour implémenter votre premier GANs en partant de zéro.Nous implémenterons en approximativement 100 lignes de code un générateur, un discriminateur et le pseudo-code décrit dans l'article afin d'entraîner ces derniers. Nous utiliserons le langage de programmation Python et le framework PyTorch. Après entraînement, le générateur nous permettra de générer des images synthétiques.J'ai la conviction qu'un concept s'apprend par la pratique et ce cours accéléré a pour objectif de vous donner les bases nécessaires afin de continuer votre apprentissage du Machine Learning, de PyTorch et des modèles génératifs (GANS, Variational Autoencoders, Normalizing Flows, ...).À l'issue de ce cours, le participant aura la possibilité d'utiliser Python (et plus particulièrement le framework PyTorch) afin d'implémenter des articles scientifiques et des solutions d'intelligence artificielle. Ce cours a également pour objectif d'être un tremplin dans votre apprentissage des modèles génératifs.Au-delà des GANs, ce cours est également une introduction générale au framework PyTorch et un cours de Machine learning de niveau intermédiaire .Concepts abordés:Le framework PyTorch afin d'implémenter et d'optimiser des réseaux de neurones.Le framework Keras afin de charger un ensemble de données.Google colab.L'utilisation des modèles génératifs dans le monde de la recherche et industri