Master PyTorch, the popular deep learning framework from Meta. Build dynamic neural networks, use torchvision for computer vision, and deploy models with TorchServe.
Top-down approach to deep learning using the fastai library. Build state-of-the-art models without needing a PhD.
Master deep learning using the PyTorch framework. Build and train neural networks for computer vision and NLP applications.
Complete PyTorch tutorial from basics to advanced topics. Learn tensors, autograd, neural networks, CNNs, RNNs, and more.
In this comprehensive course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNN) 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 LearnThroughout this course, you will gain expertise in:Introduction to Computer VisionUnderstanding 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 VisionIntroduction 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 (CNN)Introduction to CNN architecture and its components.Understanding convolution layers, pooling layers, and fully connected layers.Implementing CNN models using TensorFlow and PyTorch.Data Augmentation and PreprocessingTechniques for improving model performance through data augmentation.Using libraries like imgaug, Albumentations, and TensorFlow Data Pipeline.Transfer Learning for Computer VisionUtilizing pre-trained models such as ResNet, VGG, and EfficientNet.Fine-tuning and optimizing transfer learning models.Object Detection ModelsExploring object detection algorithms like:YOLO (You Only Look Once)Faster R-CNNImplement
PyTorch: written in Python, is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. PyTorch is a Deep Learning framework that is a boon for researchers and data scientists. It supports Graphic Processing Units and is a platform that provides maximum flexibility and speed. With PyTorch, you can dynamically build neural networks and easily perform advanced Artificial Intelligence tasks.This comprehensive 2-in-1 course takes a practical approach and is filled with real-world examples to help you create your own application using PyTorch! Begin with exploring PyTorch and the impact it has made on Deep Learning. Design and implement powerful neural networks to solve some impressive problems in a step-by-step manner. Build a Convolutional Neural Network (CNN) for image recognition. Also, predict share prices with Recurrent Neural Network and Long Short-Term Memory Network (LSTM). You’ll learn how to detect credit card fraud with autoencoders and much more! By the end of the course, you’ll conquer the world of PyTorch to build useful and effective Deep Learning models with the PyTorch Deep Learning framework with the help of real-world examples!Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Deep Learning with PyTorch, covers building useful and effective deep learning models with the PyTorch Deep Learning framework. In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto-Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you
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? 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 & Frameworks: Master industry-standard tools like PyTorch and dive into CNNs, transfer learning with ResNet, 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
Pada kursus ini, teman-teman akan belajar mengenai pengolahan citra dengan menggunakan Bahasa Python. Materi pada kursus ini didesain sesederhana mungkin agar teman-teman dapat lebih mudah dalam memahami materi yang disampaikan. Selain materi yang mudah dipahami dan dipelajari, materi pada kursus ini akan dikembangkan dan ditambahkan secara terus menerus seiring berkembangnya bidang computer vision atau pengolahan citra. Materi yang disajikan berawal dari materi paling sederhana yaitu pre-processing citra dan dilanjutkan dengan deep learning.Pada pre-processing citra, teman-teman akan belajar mengenai rotasi, shifting(pergeseran pixel), flipping, ruang warna dan masih banyak lagi. Pada materi ruang warna, teman-teman akan belajar juga mengenai perhitungan matematika secara manual sebelum implementasi dengan menggunakan python. Pada materi deep learning, teman-teman akan belajar mengenai Neural Network atau NN dan Convolutional Neural Network (CNN). Materi yang akan dipelajari pada Neural Network berupa perhitungan matematika dari forward pass dan backward pass. Selain perhitungan manual, teman-teman juga akan belajar bagaimana cara mengimplementasikan Neural Network dengan menggunakan Bahasa Python dengan library Pytorch. Pada materi Convolutional Neural Network, teman-teman akan mempelajari bagaimana sebuah mesin mempelajar sebuah data dan membuat sebuah sistem Artificial Intelligence (AI) secara sederhana. Materi Convolutional Neural Network yang disajikan antara lain, bagaimana penerapan dengan menggunakan Bahasa Python dengan library Pytorch dan bagaimana contoh-contoh penggunaan Convolutional Neural Network dalam kehidupan sehari-hari.
Welcome to this Deep Learning Image Classification course with PyTorch2.0 in Python3. Do you want to learn how to create powerful image classification recognition systems that can identify objects with immense accuracy? if so, then this course is for you what you need! In this course, you will embark on an exciting journey into the world of deep learning and image classification. This hands-on course is designed to equip you with the knowledge and skills necessary to build and train deep neural networks for the purpose of classifying images using the PyTorch framework.We have divided this course into Chapters. In each chapter, you will be learning a new concept for training an image classification model. These are some of the topics that we will be covering in this course:Training all the models with torch.compile which was introduced recently in Pytroch2.0 as a new feature.Install Cuda and Cudnn libraires for PyTorch2.0 to use GPU. How to use Google Colab Notebook to write Python codes and execute code cell by cell.Connecting Google Colab with Google Drive to access the drive data.Master the art of data preparation as per industry standards. Data processing with torchvision library. data augmentation to generate new image classification data by using:- Resize, Cropping, RandomHorizontalFlip, RandomVerticalFlip, RandomRotation, and ColorJitter.Implementing data pipeline with data loader to efficiently handle large datasets.Deep dive into various model architectures such as LeNet, VGG16, Inception v3, and ResNet50.Each model is explained through a nice block diagram through layer by layer for deeper understanding.Implementing the training and Inferencing pipeline.Understanding transfer learning to train models on less data.Display the model inferencing result
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:NumPy Crash CoursePandas Crash CourseNeural Network Theory and IntuitionHow to Work with Torchvision datasetsConvolutional Neural Network (CNN)Long-Short Term Memory (LSTM)and much moreSince 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.
This course focuses on optimizing machine learning workflows through efficient data handling and training techniques in PyTorch. It covers advanced DataLoader configurations, profiling tools, and modern optimization strategies like mixed precision training and gradient accumulation.
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
Learn Computer Vision with PyTorch
Learn PyTorch for Deep Learning and Computer Vision
TensorFlow is quickly becoming the technology of choice for deep learning and machine learning, because of its ease to develop powerful neural networks and intelligent machine learning applications. Like TensorFlow, PyTorch has a clean and simple API, which makes building neural networks faster and easier. It's also modular, and that makes debugging your code a breeze. If you’re someone who wants to get hands-on with Deep Learning by building and training Neural Networks, then go for this course.This course takes a step-by-step approach where every topic is explicated with the help of a real-world examples. You will begin with learning some of the Deep Learning algorithms with TensorFlow such as Convolutional Neural Networks and Deep Reinforcement Learning algorithms such as Deep Q Networks and Asynchronous Advantage Actor-Critic. You will then explore Deep Reinforcement Learning algorithms in-depth with real-world datasets to get a hands-on understanding of neural network programming and Autoencoder applications. You will also predict business decisions with NLP wherein you will learn how to program a machine to identify a human face, predict stock market prices, and process text as part of Natural Language Processing (NLP). Next, you will explore the imperative side of PyTorch for dynamic neural network programming. Finally, you will build two mini-projects, first focusing on applying dynamic neural networks to image recognition and second NLP-oriented problems (grammar parsing).By the end of this course, you will have a complete understanding of the essential ML libraries TensorFlow and PyTorch for developing and training neural networks of varying complexities, without any hassle.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Roland Meertens is currently developing computer vision algorithms for self-driving ca
Growing Importance of Deep LearningDeep 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
you will learn all these Topics and lot more 1. Core Concepts1. Perceptron2. MLP and its Notation3. Forward Propagation4. Backpropagation5. Chain Rule of Derivative in Backpropagation6. Vanishing Gradient Problem7. Exploding GradientActivation FunctionsList of Activation Functions1. Linear Function2. Binary Step Function3. Sigmoid Function (Logistic Function)4. Tanh (Hyperbolic Tangent Function)5. ReLU (Rectified Linear Unit)6. Leaky ReLU7. Parametric ReLU (PReLU)8. Exponential Linear Unit (ELU)9. Scaled Exponential Linear Unit (SELU)10. Softmax11. Swish.12. SoftPlus13. Mish14. Maxout15. GELU (Gaussian Error Linear Unit)16. SiLU (Sigmoid Linear Unit)17. Gated Linear Unit (GLU)18. SwiGLU19. Mish Activation FunctionDerivative of Activation FunctionsProperties of Activation Functions1. Saturating vs Non-Saturating2. Smooth vs Non-Smooth3. Generalized vs Specialized4. Underflow and Overflow5. Undefined and Defined6. Computationally Expensive vs Inexpensive.7. 0-Centered and Non-0-Centered8. Differentiable vs Non-Differentiable9. Bounded and Unbounded10. Monotonicity11. Linear Vs Non LinearIdeal Activation Function Characteristics1. Non-Linearity2. Differentiability3. Computational Efficiency4. Avoids Saturation5. Non-Sparse (Dense) Gradients6. Centered Output (0-Centered)7. Prevents Exploding Gradients8. Monotonicity (Optional)9. Sparse Activations (Optional)1
Empower Your Deep Learning Journey: Become a Self-Sufficient DL Programmer with the Ability to Read and Implement Research PapersNote: These prerequisites will ensure a solid foundation for understanding and implementing the concepts covered in the course.Basic proficiency in PythonBasic PyTorch skillsFamiliarity with NumPy for efficient data manipulationIn this course, you will:Learn PyTorch thoroughly, including dataset objects, data loaders, transfer learning, and different gradient modes.Acquire the ability to represent data effectively for solving complex problems.Gain hands-on experience in coding custom loss functions.Develop proficiency in training large models.Join us to unlock the full potential of PyTorch and gain the practical skills necessary to excel in deep learning.Take the Next Leap in Deep Learning: Enroll Now!Don't miss out on this opportunity to elevate your skills in PyTorch and master the art of deep learning. Join our course today and:Unlock the full potential of PyTorch.Unleash the power of PyTorch and NumPy to solve complex data representation problems with a practical example.Develop essential skills for solving complex problems.Gain hands-on experience with custom loss functions.Train and optimize large-scale models.Elevate your skills, conquer challenges, and revolutionize your data expertise today!
Learn Deep Learning: Getting Started
A área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina).Também dentro do contexto da Aprendizagem de Máquina existe a área de Aprendizagem por Reforço, que é um tipo de aprendizagem usado em sistemas multi-agente no qual os agentes devem interagir no ambiente e aprenderem por conta própria, ganhando recompensas positivas quando executam ações corretas e recompensas negativas quando executam ações que não levem para o objetivo. O interessante dessa técnica é que a inteligência artificial aprende sem nenhum conhecimento prévio, adaptando-se ao ambiente e encontrando as soluções sozinho!E para levar você até essa área, neste curso você terá uma visão teórica e principalmente prática sobre a construção de um carro autônomo virtual utilizando aprendizagem por reforço! Vamos trabalhar com técnicas modernas de Deep Learning com a biblioteca PyTorch e a linguagem Python! Ao final você terá todas as ferramentas necessárias para solucionar outros tipos de problemas com aprendizagem por reforço. O conteúdo do curso está dividido em três partes:Teoria sobre aprendizagem por reforço com o algoritmo Q-LearningTeoria da aprendizagem por reforço
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 ContentsDeep 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 Tensor flow.Practical Oriented explanations Deep Learning Models with implementation both in Pytorch and Tensor Flow.No need of any prerequisites. I will teach you everything from scratch.Job Oriented StructureSections 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 (CNN)· One Dimensional CNN· Setting Early Stopping Criterion in CNN· Recurrent Neural Network (RNN)· Long Short-Term Memory (LSTM) Network· Bidirectional LSTM· Generative Adversarial Network (GAN)· DCGANs· Autoencoders· LSTM 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 Tensor Flow.
Learn PyTorch for deep learning. CNNs, RNNs, GANs, transfer learning.
April 2024 Update: Two new sections have been added recently. New Section 5: learn to edit the clothes of a person in a picture by programming a combination of a segmentation model with the Stable Diffusion generative model. New bonus section 6: Journey to the latent space of a neural network - dive deep into the latent space of the neural networks that power Generative AI in order to understand in depth how they learn their mappings. ____________________________Generative A.I. is the present and future of A.I. and deep learning, and it will touch every part of our lives. It is the part of A.I that is closer to our unique human capability of creating, imagining and inventing. By doing this course, you gain advanced knowledge and practical experience in the most promising part of A.I., deep learning, data science and advanced technology.The course takes you on a fascinating journey in which you learn gradually, step by step, as we code together a range of generative architectures, from basic to advanced, until we reach multimodal A.I, where text and images are connected in incredible ways to produce amazing results.At the beginning of each section, I explain the key concepts in great depth and then we code together, you and me, line by line, understanding everything, conquering together the challenge of building the most promising A.I architectures of today and tomorrow. After you complete the course, you will have a deep understanding of both the key concepts and the fine details of the coding process.What a time to be alive! We are able to code and understand architectures that bring us home, home to our own human nature, capable of creating and imagining. Together, we will make it happen. Let's do it!
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 SegmentationDevelopers who want to incorporate Semantic Segmentation capabilities into their projectsGraduates and Researchers in Computer Science, Electrical Engineering, and other related fields who want to learn about the latest advances in Deep Learning for Semantic SegmentationIn 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 PyTorchThe 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 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
What is PyTorch and why should I learn it?PyTorch is a machine learning and deep learning framework written in Python.PyTorch enables you to craft new and use existing state-of-the-art deep learning algorithms like neural networks powering much of today’s Artificial Intelligence (AI) applications.Plus it's so hot right now, so there's lots of jobs available!PyTorch is used by companies like:Tesla to build the computer vision systems for their self-driving carsMeta to power the curation and understanding systems for their content timelinesApple to create computationally enhanced photography.Want to know what's even cooler?Much of the latest machine learning research is done and published using PyTorch code so knowing how it works means you’ll be at the cutting edge of this highly in-demand field.And you'll be learning PyTorch in good company.Graduates of Zero To Mastery are now working at Google, Tesla, Amazon, Apple, IBM, Uber, Meta, Shopify + other top tech companies at the forefront of machine learning and deep learning.This can be you.By enrolling today, you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.Most importantly, you will be learning PyTorch from a professional machine learning engineer, with real-world experience, and who is one of the best teachers around!What will this PyTorch course be like?This PyTorch course is very hands-on and project based. You won't just be staring at your screen. We'll leave that for other PyTorch tutorials and courses.In this course you'll actually be:Running experimentsCompleting exercises to test your skillsBuilding real-world deep learning models and projects to mimic real life scenarios<
This course is complete guide of AWS SageMaker wherein student will learn how to build, deploy SageMaker models by brining on-premises docker container and integrate it to SageMaker. Course will also do deep drive on how to bring your own algorithms in AWS SageMaker Environment. Course will also explain how to use pre-built optimized SageMaker Algorithm.Course will also do deep drive how to create pipeline and workflow so model could be retrained and scheduled automatically.This course covers all aspect of AWS Certified Machine Learning Specialty (MLS-C01)This course will give you fair ideas of how to build Transformer framework in Keras for multi class classification use cases. Another way of solving multi class classification by using pre-trained model like Bert .Both the Deep learning model later encapsulated in Docker in local machine and then later push back to AWS ECR repository.This course offers:AWS Certified Machine Learning Specialty (MLS-C01)What is SageMaker and why it is requiredSageMaker Machine Learning lifecycleSageMaker ArchitectureSageMaker training techniques:Bring your own docker container from on premise to SageMakerBring your own algorithms from local machine to SageMakerSageMaker Pre built AlgorithmSageMaker Pipeline developmentSchedule the SageMaker Training notebookMore than 5 hour course are provided which helps beginners to excel in SageMaker and will be well versed with build, train and deploy the models in SageMaker
Pytorch&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 huggingface yourself by colab. Each Section will have one assignment for you to think and code yourself. The Agenda is below. Agenda:IntroductionGoogle ColaboratoryNeuronPerceptronMake Your Perceptron TrainableNormalize DataActivation FunctionLoss FunctionGradient DescentElegant Pytorch Gradient DescentFinal ProjectFinal Project ExplainedMulti Layer Perceptron(MLP)One Hot EncodingPrepare data for MLPDefine MLPTrain & Evaluate MLPFinal Project for MLPFCNN ExplainedFCNN LOVE Letters Classification using MLPFinal Project For FCNNCNN ExplainedCNN Prepare data(Fashion MNIST) CNN Define Model CNN Train&Evaluate ModelCNNInferenceFinal Project For CNNRNN ExplainedRNN Prepare dataRNN Define ModelRNN Train ModelRNN InferenceBERT Sesame StreetBERT Prepare Data IMDBBERT Model definitionBERT Model TrainingBERT Model Evaluation<p
A 2-hour project-based course where you will learn to understand and write a custom dataset class for an Image-mask dataset. You will also apply segmentation augmentation and load a pretrained state-of-the-art convolutional neural network for segmentation.
Deep Learning with TensorFlow focuses on building and deploying advanced neural network models that mimic the human brain’s learning capabilities to solve complex problems. This topic explains the architecture of deep neural networks, including layers, neurons, activation functions, loss functions, backpropagation, and optimization techniques. Learners explore how TensorFlow, a leading open-source framework, enables the design, training, and deployment of deep learning models efficiently, handling large datasets and computational requirements. Practical applications such as image classification, object detection, natural language processing, speech recognition, and recommendation systems are highlighted to show real-world relevance. The topic also covers hyperparameter tuning, model evaluation, performance optimization, and techniques to prevent overfitting or underfitting. Learners gain a comprehensive understanding of how to preprocess data, structure neural networks, and apply advanced algorithms to achieve accurate and reliable results. This topic is ideal for students, AI enthusiasts, developers, and data scientists seeking practical deep learning expertise. By mastering Deep Learning with TensorFlow, learners develop the skills necessary to build intelligent systems that solve complex problems, contribute to innovation in AI-driven industries, and prepare for advanced roles in artificial intelligence, data science, and machine learning engineering. The knowledge gained empowers learners to create scalable, high-performing AI solutions that can be applied across multiple sectors, from technology to business intelligence.
This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Written in Python, PyTorch is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs.In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks.By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You'll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems.This course uses Python 3.6, and PyTorch 0.3, while not the latest version available, it provides relevant and informative content for legacy users of Python, and PyTorch.About the AuthorAnand Saha is a software professional with 15 years' experience in developing enterprise products and services. Back in 2007, he worked with machine learning to predict call patterns at TATA Communications. At Symantec and Veritas, he worked on various features of an enterprise backup product used by Fortune 500 companies. Along the way he nurtured his interests in Deep Learning by attending Coursera and Udacity MOOCs.He is passionate about Deep Learning and its applications; so much so that he quit Veritas at the beginning of 2017 to focus full time on Deep Learning practices. Anand built pipelines to detect and count endangered species from aerial images, trained a robotic arm to pick and place objects, and imp
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' Fastai course), you’ll be pleasantly surprised at how much more you can understand.You'll be able to understand
Course DescriptionThis tutorial course is a practical, project driven introduction to Machine Learning and Deep Learning using PyTorch. Each concept is taught through real world examples, allowing professionals to quickly understand, how models work and how they are used in real applications. You will build complete end to end projects such as LSTM based sentiment analysis, RNN based spam detection, CNN models for image classification, MLP networks for video quality prediction, and regression models using real datasets from sales, finance, and home loan scenarios. This tutorial course also covers how to convert Jupyter Notebook experiments into a clean, modular Python project structure suitable for production use.By combining NLP, computer vision, and predictive analytics use cases, this tutorial course helps you gain solid practical experience in PyTorch while learning how to preprocess data, design model architectures, train models, evaluate results, and prepare solutions for real-world implementation.This Tutorial Course Primarily Focuses On:Building ML & DL models end to end in PyTorchPerforming data preprocessing and feature engineeringTraining, evaluating, and deploying models with real datasetsUnderstanding architectures like LSTM, CNN, DNN, Decision Trees, Random Forest & MLPConverting research notebooks into production ready Python modulesBy the end of this course, You will be able toBuild machine learning regression & classification modelsDevelop CNN, RNN, MLP, and LSTM architectures in PyTorchPerform NLP tasks like sentiment analysis & spam detectionImplement image classification models for handwritten alphabets & traffic signsConvert notebooks into modular Python project structuresWork with real time data for prediction and quality assessmentYou will learn in this tutorial courseDec
Unlock the power of interactive data science with Interactive Data Science in Python — a comprehensive, beginner-friendly course designed to take you from novice to confident practitioner. We begin by exploring Shiny, the dynamic and popular web app framework for Python, where you'll learn how to build interactive dashboards, responsive data visualizations, and user-friendly interfaces using the classic Shiny library. Once you’ve gained solid skills, you’ll transition smoothly to Shiny Express, a modern, more streamlined toolkit that accelerates app development while maintaining full flexibility.Alongside Shiny, you’ll dive deep into essential Python data science libraries like Pandas, Seaborn, and Matplotlib. You’ll master how to clean, analyze, visualize, and explore complex datasets with clarity and precision, empowering you to uncover patterns and tell compelling stories with data.This course also introduces PyTorch basics from scratch — perfect for beginners eager to explore deep learning and neural networks. You’ll grasp fundamental machine learning concepts and get hands-on experience building your own models, preparing you to confidently tackle more advanced AI projects.Throughout the course, you’ll engage with practical coding exercises, real-world datasets, and projects focused on creating interactive applications that captivate users and dynamically reveal insights. Whether you aspire to be a data scientist, analyst, or developer, this course will equip you with the skills and confidence to build powerful data-driven applications and understand foundational deep learning techniques in Python.Jump in today and bring your data to life with interactive, intelligent applications!
Learn Artificial Intelligence A-Z: Learn How To Build An AI
Did you ever want to apply Deep Neural Networks to more than MNIST, CIFAR10 or cats vs dogs?Do you want to learn about state of the art Machine Learning frameworks while segmenting cancer in CT-images?Then this is the right course for you!Welcome to one of the most comprehensive courses on Deep Learning in medical imaging!This course focuses on the application of state of the art Deep Learning architectures to various medical imaging challenges.You will tackle several different tasks, including cancer segmentation, pneumonia classification, cardiac detection, Interpretability and many more.The following topics are covered:NumPyMachine Learning TheoryTest/Train/Validation Data SplitsModel Evaluation - Regression and Classification TasksTensors with PyTorchConvolutional Neural NetworksMedical ImagingInterpretability of a network's decision - Why does the network do what it does?A state of the art high level pytorch library: pytorch-lightningTumor SegmentationThree-dimensional dataand many moreWhy choose this specific Deep Learning with PyTorch for Medical Image Analysis course ?This course provides unique knowledge on the application of deep learning to highly complex and non-standard (medical) problems (in 2D and 3D) All lessons include clearly summarized theory and code-along examples, so that you can understand and follow every step. Powerful online community with our QA Forums with thousands of students and dedicated Teaching Assistants, as well as student interaction on our Discord Server.You will learn skills and techniques that the vast majority of AI engineers do not have!</ul
A book that provides a comprehensive guide to machine learning using two popular Python libraries, covering a wide range of supervised learning models.
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Welcome to PyTorch: Deep Learning and Artificial Intelligence!Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence.Is it possible that Tensorflow is popular only because Google is popular and used effective marketing?Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems?It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab - FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can't go wrong with PyTorch. And maybe it's a bonus that the library won't completely ruin all your old code when it advances to the next version. ;)On the flip side, it is very well-known that all the top AI shops (ex. OpenAI, Apple, and JPMorgan Chase) use PyTorch. OpenAI just recently switched to PyTorch in 2020, a strong sign that PyTorch is picking up steam.If you are a professional, you will quickly recognize that building and testing new ideas is extremely easy with PyTorch, while it can be pretty hard in other libraries that try to do everything for you. Oh, and it's faster.Deep Learning has been responsible for some amazing achievements recently, such as:<ul
Máster Especialista de Deep Learning en Python con PyTorch.Redes Neuronales Profundas con PyTorch: Diseño, Implementación y Evaluación de Modelos Neuronales desde 0 a experto.Instructor: PhD. Manuel Castillo-CaraRequisitos previos: Se recomienda tener conocimientos sobre Machine Learning. Se recomienda realizar previamente siguiente curso de Udemy:Machine Learning con Python. Aprendizaje Automático Avanzado - Aprendizaje Automático Scikit-Learn en Python. Modelos Predictivos. Data Science. De básico a Experto.Descripción del Curso:Bienvenido al curso de Deep Learning con Python y PyTorch. En este curso exploraremos a fondo la librería PyTorch de Python para Deep Learning, aprendiendo cómo utilizarla para desarrollar y evaluar modelos de Deep Learning avanzados. Nuestro objetivo es proporcionarte las técnicas, el código y las habilidades necesarias para que puedas aplicar el Deep Learning en tus propios proyectos innovadores.PyTorch se ha convertido en una de las herramientas más potentes y flexibles en el campo del aprendizaje profundo. A diferencia de otras librerías, PyTorch ofrece un enfoque dinámico y intuitivo para la construcción de redes neuronales, permitiéndote definir y modificar tus modelos con gran facilidad.En este curso, nos centraremos en el desarrollo práctico de modelos de Deep Learning utilizando PyTorch. Comenzaremos con los fundamentos y avanzaremos hacia técnicas más sofisticadas, permitiéndote construir una base sólida que podrás expandir en el futuro según tus necesidades y proyectos específicos.Hemos elegido PyTorch como nuestra plataforma principal debido a su capacidad para desarrollar rápidamente modelos de Deep Learning potentes y eficientes. PyTorch combina la potencia de la computación GPU con una API intuitiva, lo que nos permitir
A área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina).A área de Deep Learning é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo que o mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação!E para levar você até essa área, neste curso você terá uma visão teórica e principalmente prática sobre as principais e mais modernas técnicas de Deep Learning utilizando a biblioteca PyTorch o Python! Este curso apresenta desde os conceitos mais básicos sobre as redes neurais até técnicas mais modernas e avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Para isso, o conteúdo está dividido em sete partes: redes neurais artificiais, redes neurais convolucionais, autoencoders, redes adversariais generativas (GANs)</strong
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 GAN 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
This course equips learners with the skills to build and train powerful deep-learning models using PyTorch. It includes an in-depth exploration of convolutional neural networks for image recognition and covers advanced training techniques like dropout and batch normalization, which are crucial for avoiding common pitfalls.
This course provides a gentle introduction to deep learning using PyTorch. It covers the fundamentals of neural networks and how to build and train them for tasks like image classification.
This professional certificate teaches how to build and train deep learning models using PyTorch. It covers applying transfer learning and fine-tuning to pretrained models for computer vision and natural language processing.
Learn Deep Learning A-Z: Hands-On Neural Networks
Are you ready to dive into the fascinating world of object detection using deep learning? In our comprehensive course "Deep Learning for Object Detection with Python and PyTorch", we will guide you through the essential concepts and techniques required to detect, classify, and locate objects in images. Object Detection has wide range of potential real life application in many fields. Object detection is used for autonomous vehicles to perceive and understand their surroundings. It helps in detecting and tracking pedestrians, vehicles, traffic signs, traffic lights, and other objects on the road. Object Detection is used for surveillance and security using drones to identify and track suspicious activities, intruders, and objects of interest. Object Detection is used for traffic monitoring, helmet and license plate detection, player tracking, defect detection, industrial usage and much more.With the powerful combination of Python programming and the PyTorch deep learning framework, you'll explore state-of-the-art algorithms and architectures like R-CNN, Fast RCNN and Faster R-CNN. Throughout the course, you'll gain a solid understanding of Convolutional Neural Networks (CNNs) and their role in Object Detection. You'll learn how to leverage pre-trained models, fine-tune them for Object Detection using Detectron2 Library developed by by Facebook AI Research (FAIR).The course covers the complete pipeline with hands-on experience of Object Detection using Deep Learning with Python and PyTorch as follows:Course BreakDown:Learn Object Detection with Python and Pytorch CodingLearn Object Detection using Deep Learning ModelsIntroduction to Convolutional Neural Networks (CNN)Learn RCNN, Fast RCNN, Faster RCNN, Mask RCNN and YOLO8, YOLO11 ArchitecturesPerform Object Detection with Fast RCNN and Faster RCNNPerform Real-time Video Object Detection with YOLOv8 and YOLO11</
This course is outdated because it is based on pytorch lightning and alot of thing has been changed since the release of this course. Further some of datasets in this course are no more available for public anymore. So I am not providing support for this course. I want to make this course free, but udemy is not allowing to do so because of content length. The reason why I am not archiving this course, because its still relevant if you want to gain concept of medical imaging competition. Greetings. This course is not intended for beginners, and it is more practically oriented. Though I tried my best to explain why I performed a particular step, I put little to no effort into explaining basic concepts such as Convolution neural networks, how the optimizer works, how ResNet, DenseNet model was created etc. This course is for those who have worked on CIFAR, MNIST data and want to work in real-life scenariosMy focus was mainly on how to participate in a competition, get data and train a model on that data, and make a submission. In this course PyTorch lightning is usedThe course covers the following topicsBinary ClassificationGet the dataRead dataApply augmentationHow data flows from folders to GPUTrain a modelGet accuracy metric and lossMulti-class classification (CXR-covid19 competition)Albumentations augmentationsWrite a custom data loaderUse publicly pre-trained model on XRayUse learning rate schedulerUse different callback functionsDo five fold cross-validations when images are in a folderTrain, save and load modelGet test predictions via ensemble learningSubmit predictions to the competition pageMulti-label classification (ODIR competition)
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 AutoGrad, 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 - AutoGrad: PyTorch's dynamic computational graph through AutoGrad 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
Master the Latest and Hottest of Deep Learning Frameworks (PyTorch) for Python Data ScienceTHIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH PYTORCH IN PYTHON!It is a full 5-Hour+ PyTorch Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Python Deep Learning frameworks- PyTorch. HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical machine & deep learning using the PyTorch framework in Python.. This means, this course covers the important aspects of PyTorch and if you take this course, you can do away with taking other courses or buying books on PyTorch. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of frameworks such as PyTorch is revolutionizing Deep Learning... By gaining proficiency in PyTorch, you can give your company a competitive edge and boost your career to the next level.THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTORCH BASED DATA SCIENCE!But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the Python data science courses
Welcome to Modern Computer Vision Tensorflow, Keras & PyTorch! 2025AI and Deep Learning are transforming industries and one of the most intriguing parts of this AI revolution is in Computer Vision!Update for 2025: Modern Computer Vision CourseWe're excited to bring you the latest updates for our 2024 modern computer vision course. Dive into an enriched curriculum covering the most advanced and relevant topics in the field:YOLOv8: Cutting-edge Object RecognitionDINO-GPT4V: Next-Gen Vision ModelsMeta CLIP for Enhanced Image AnalysisDetectron2 for Object DetectionSegment AnythingFace Recognition TechnologiesGenerative AI Networks for Creative ImagingTransformers in Computer VisionDeploying & Productionizing Vision ModelsDiffusion Models for Image ProcessingImage Generation and Its ApplicationsAnnotation Strategy for Efficient LearningRetrieval Augmented Generation (RAG)Zero-Shot Classifiers for Versatile ApplicationsUsing Roboflow: Streamlining Vision WorkflowsWhat is Computer Vision?But what exactly is Computer Vision and why is it so exciting? Well, what if Computers could understand what they’re seeing through cameras or in images? The applications for such technology are endless from medical imaging, military, self-driving cars, security monitoring, analysis, safety, farming, industry, and manufacturing! The list is endless. Job demand for Computer Vision workers are skyrocketing
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