Courses

Learn PyTorch. Become a Deep Learning Engineer. Get Hired. What you'll learn: Everything from getting started with using PyTorch to building your own real-world modelsUnderstand how to integrate Deep Learning into tools and applicationsBuild and deploy your own custom trained PyTorch neural network accessible to the publicMaster deep learning and become a top candidate for recruiters seeking Deep Learning EngineersThe skills you need to become a Deep Learning Engineer and get hired with a chance of making US$100,000+ / yearWhy PyTorch is a fantastic way to start working in machine learningCreate and utilize machine learning algorithms just like you would write a Python programHow to take data, build a ML algorithm to find patterns, and then use that algorithm as an AI to enhance your applicationsTo expand your Machine Learning and Deep Learning skills and toolkit 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 scenariosBy the end of it all, you'll have the skillset needed to identify and develop modern deep learning solutions that Big Tech companies encounter. Fair warning: this course is very comprehensive. But don't be intimidated, Daniel will teach you everything from scratch and step-by-step!Here's what you'll learn in this PyTorch course:1. PyTorch Fundamentals — We start with the barebone fundamentals, so even if you're a beginner you'll get up to speed.In machine learning, data gets represented as a tensor (a collection of numbers). Learning how to craft tensors with PyTorch is paramount to building machine learning algorithms. In PyTorch Fundamentals we cover the PyTorch tensor datatype in-depth.2. PyTorch Workflow — Okay, you’ve got the fundamentals down, and you've made some tensors to represent data, but what now?With PyTorch Workflow you’ll learn the steps to go from data -> tensors -> trained neural network model. You’ll see and use these steps wherever you encounter PyTorch code as well as for the rest of the course.3. PyTorch Neural Network Classification — Classification is one of the most common machine learning problems.Is something one thing or another?Is an email spam or not spam?Is credit card transaction fraud or not fraud?With PyTorch Neural Network Classification you’ll learn how to code a neural network classification model using PyTorch so that you can classify things and answer these questions.4. PyTorch Computer Vision — Neural networks have changed the game of computer vision forever. And now PyTorch drives many of the latest advancements in computer vision algorithms.For example, Tesla use PyTorch to build the computer vision algorithms for their self-driving software.With PyTorch Computer Vision you’ll build a PyTorch neural network capable of seeing patterns in images of and classifying them into different categories.5. PyTorch Custom Datasets — The magic of machine learning is building algorithms to find patterns in your own custom data. There are plenty of existing datasets out there, but how do you load your own custom dataset into PyTorch?This is exactly what you'll learn with the PyTorch Custom Datasets section of this course.You’ll learn how to load an image dataset for FoodVision Mini: a PyTorch computer vision model capable of classifying images of pizza, steak and sushi (am I making you hungry to learn yet?!).We’ll be building upon FoodVision Mini for the rest of the course.6. PyTorch Going Modular — The whole point of PyTorch is to be able to write Pythonic machine learning code.There are two main tools for writing machine learning code with Python:A Jupyter/Google Colab notebook (great for experimenting)Python scripts (great for reproducibility and modularity)In the PyTorch Going Modular section of this course, you’ll learn how to take your most useful Jupyter/Google Colab Notebook code and turn it reusable Python scripts. This is often how you’ll find PyTorch code shared in the wild.7. PyTorch Transfer Learning — What if you could take what one model has learned and leverage it for your own problems? That’s what PyTorch Transfer Learning covers.You’ll learn about the power of transfer learning and how it enables you to take a machine learning model trained on millions of images, modify it slightly, and enhance the performance of FoodVision Mini, saving you time and resources.8. PyTorch Experiment Tracking — Now we're going to start cooking with heat by starting Part 1 of our Milestone Project of the course!At this point you’ll have built plenty of PyTorch models. But how do you keep track of which model performs the best?That’s where PyTorch Experiment Tracking comes in.Following the machine learning practitioner’s motto of experiment, experiment, experiment! you’ll setup a system to keep track of various FoodVision Mini experiment results and then compare them to find the best.9. PyTorch Paper Replicating — The field of machine learning advances quickly. New research papers get published every day. Being able to read and understand these papers takes time and practice.So that’s what PyTorch Paper Replicating covers. You’ll learn how to go through a machine learning research paper and replicate it with PyTorch code.At this point you'll also undertake Part 2 of our Milestone Project, where you’ll replicate the groundbreaking Vision Transformer architecture!10. PyTorch Model Deployment — By this stage your FoodVision model will be performing quite well. But up until now, you’ve been the only one with access to it.How do you get your PyTorch models in the hands of others?That’s what PyTorch Model Deployment covers. In Part 3 of your Milestone Project, you’ll learn how to take the best performing FoodVision Mini model and deploy it to the web so other people can access it and try it out with their own food images.What's the bottom line?Machine learning's growth and adoption is exploding, and deep learning is how you take your machine learning knowledge to the next level. More and more job openings are looking for this specialized knowledge.Companies like Tesla, Microsoft, OpenAI, Meta (Facebook + Instagram), Airbnb and many others are currently powered by PyTorch.And this is the most comprehensive online bootcamp to learn PyTorch and kickstart your career as a Deep Learning Engineer.So why wait? Advance your career and earn a higher salary by mastering PyTorch and adding deep learning to your toolkit?

Dive into a captivating podcast episode featuring Sachin Abeywardana, Deep Learning Engineer at Canva AI, as he shares his adventures in building CLIP and other large language models. Explore insights on understanding broader product implications, incorporating AI and machine learning capabilities, and the challenges of balancing work and family life. Learn about AI models for grammar correction and code generation, the fascinating CLIP model, and Sachin's journey from completing a PhD in Bayesian Machine Learning to focusing on NLP problems. Gain valuable perspectives on leading ML engineers and teams, the importance of being practical with math, and the insufficient exploration of Transformers. Discover Sachin's thoughts on vector databases, recommendation systems, and the criticisms of current architecture limitations. This informative and thought-provoking discussion covers a wide range of topics in the field of machine learning and AI, offering valuable insights for both beginners and experienced practitioners.

Learn PyTorch from scratch! This PyTorch course is your step-by-step guide to developing your own deep learning models using PyTorch. You'll learn Deep Learning with PyTorch by building a massive 3-part real-world milestone project. By the end, you'll have the skills and portfolio to get hired as a Deep Learning Engineer. Learn PyTorch. Become a Deep Learning Engineer. Get Hired.Everything from getting started with using PyTorch to building your own real-world modelsWhy PyTorch is a fantastic way to start working in machine learningUnderstand how to integrate Deep Learning into tools and applicationsCreate and utilize machine learning algorithms just like you would write a Python programBuild and deploy your own custom trained PyTorch neural network accessible to the publicHow to take data, build a ML algorithm to find patterns, and then use that algorithm as an AI to enhance your applicationsMaster deep learning and become a top candidate for recruiters seeking Deep Learning EngineersTo expand your Machine Learning and Deep Learning skills and toolkitThe skills you need to become a Deep Learning Engineer and get hired with a chance of making US$100,000+ / year

## Become a Cutting-Edge Machine Learning Engineer Step into the exciting world of machine learning engineering with this comprehensive Track designed for aspiring professionals. You'll learn everything you need to know about model deployment, operations, monitoring, and maintenance to become a well-rounded machine learning engineer. ## Master the Fundamentals of MLOps Gain a deep understanding of the core concepts of MLOps as you: * Explore the modern MLOps framework and lifecycle * Learn to design, train, and deploy end-to-end models * Gain hands-on experience with key technologies like Python, Docker, and MLflow * Understand crucial concepts like CI/CD, deployment strategies, and concept drift ## Gain Practical Skills Through Real-World Projects Apply your knowledge to solve authentic challenges that mirror the day-to-day work of a machine learning engineer. You'll have the opportunity to develop predictive models for agriculture, forecast temperatures in London using advanced techniques, and build reliable data pipelines using ETL and ELT principles. ## Develop a Versatile Machine Learning Engineering Skill Set Throughout this Track, you'll gain expertise in building and deploying machine learning models in production environments, ensuring their performance remains optimal over time. You'll explore methods for monitoring models and addressing issues related to data and concept drift while leveraging data version control for efficient ML data management. Additionally, you'll learn how to implement CI/CD pipelines to streamline model development and deployment, making machine learning workflows more reliable and scalable. ## Prepare for a Junior Machine Learning Engineer Role Upon completing this Track, you'll have the knowledge and practical experience to confidently pursue junior machine learning engineer positions. You'll be equipped to: * Collaborate with data science teams to bring models from concept to production * Optimize model performance and ensure seamless integration with business systems * Continuously monitor and maintain deployed models to deliver reliable results * Contribute to the development of scalable and efficient machine learning infrastructure Note: This Track assumes prior knowledge of data manipulation, training, and evaluating machine learning models using Python. ## Unlock Your Potential in Machine Learning Engineering Start this transformative journey to become a sought-after machine learning engineer. With interactive courses, real-world projects, and expert instruction, you'll gain the skills and confidence to make a lasting impact in this cutting-edge field.

Guided by real-world programming examples in TensorFlow and PyTorch, you’ll master neural network fundamentals, convolutional and recurrent architectures, and cutting-edge topics like transformers, large language models, and multimodal AI. By the end of this specialization, you’ll be equipped to build, train, and deploy deep learning models for image classification, language translation, and more—while understanding the ethical considerations essential for responsible AI innovation.

Dive into the world of deep learning with this comprehensive 1.5-hour course from Kaggle. Explore key concepts including computer vision, convolutional neural networks, TensorFlow and Keras programming, transfer learning, and data augmentation. Gain a deeper understanding of deep learning fundamentals, learn to build models from scratch, and optimize them using techniques like stride length adjustment and dropout. Discover the power of Tensor Processing Units (TPUs) and their applications in cutting-edge AI, from AlphaGo to speech recognition. Get hands-on experience with TPU notebooks and learn to work with TFRecords for efficient data processing.

An introduction to the field of deep learning, including neural networks, convolutional neural networks, recurrent neural networks, transformers, generative models, neural network compression and transfer learning. This course will benefit students’ careers as a machine learning engineer or data scientist.

COURSE OUTLINE : The availability of a huge volume of Image and Video data over the internet has made the problem of data analysis and interpretation a really challenging task. Deep Learning has proved itself to be a possible solution to such Computer Vision tasks. Not only in Computer Vision, but Deep Learning techniques are also widely applied in Natural Language Processing tasks. In this course, we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. On completion of the course, students will acquire the knowledge of applying Deep Learning techniques to solve various real-life problems.

Explore advanced deep learning techniques in ArcGIS Pro through a series of hands-on tutorials. Learn to classify features with multiple labels, extract residential parcels from imagery using BDCN and HED, implement the Pix2Pix model, extract roads from satellite imagery, digitize and georeference scanned paper maps, and classify point cloud datasets. Master these powerful tools to enhance your geospatial analysis and data processing capabilities in just 30 minutes.

Artificial neural networks form the foundation of modern AI systems. “Deep Learning” offers participants a comprehensive introduction to the core principles and fundamental building blocks used in today’s neural networks. The course covers the most important types of neural networks, like MLPs, CNNs, RNNs, and Transformers, as well as practical techniques for efficient training and the reuse large pre-trained models. Throughout the course, students will gain a robust understanding of the general training process and key differences between different network types, as well as practical knowledge through hands-on programming exercises. By the end of the course, students will be equipped with the knowledge and skills to understand, train, and apply deep neural networks to a variety of problems, laying a strong foundation for advanced exploration of the field.

Build foundational skills in deep learning by designing and training neural networks to solve complex real-world problems. You’ll begin with the essentials of neural networks, advancing to specialized architectures like Convolutional and Recurrent Neural Networks, along with Generative Adversarial Networks. Through projects, create models for applications such as image classification, sentiment analysis, and face generation, gaining hands-on experience with PyTorch and advanced training techniques. Ideal for those aiming to harness the potential of deep learning, this experience prepares you to tackle AI challenges across various domains.

AI is revolutionizing the way we live, work and communicate. At the heart of AI is Deep Learning. Once a domain of researchers and PhDs only, Deep Learning has now gone mainstream thanks to its practical applications and availability in terms of consumable technology and affordable hardware. The demand for Data Scientists and Deep Learning professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Deep Learning skills by employers -- and the job salaries of Deep Learning practitioners -- are only bound to increase over time, as AI becomes more pervasive in society. Deep Learning is a future-proof career. Within this series of courses, you’ll be introduced to concepts and applications in Deep Learning, including various kinds of Neural Networks for supervised and unsupervised learning. You’ll then delve deeper and apply Deep Learning by building models and algorithms using libraries like Keras, PyTorch, and Tensorflow. You’ll also master Deep Learning at scale by leveraging GPU accelerated hardware for image and video processing, as well as object recognition in Computer Vision. Throughout this program you will practice your Deep Learning skills through a series of hands-on labs, assignments, and projects inspired by real world problems and data sets from the industry. You’ll also complete the program by preparing a Deep Learning capstone project that will showcase your applied skills to prospective employers. This program is intended to prepare learners and equip them with skills required to become successful AI practitioners and start a career in applied Deep Learning.

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

COURSE OUTLINE: Deep Learning has received a lot of attention over the past few years and has been employed successfully by companies like Google, Microsoft, IBM, Facebook, Twitter etc. to solve a wide range of problems in Computer Vision and Natural Language Processing. In this course, we will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms. We will also look at various optimization algorithms such as Gradient Descent, Nesterov Accelerated Gradient Descent, Adam, AdaGrad and RMSProp which are used for training such deep neural networks. At the end of this course students would have knowledge of deep architectures used for solving various Vision and NLP tasks

AI is revolutionizing the way we live, work and communicate. At the heart of AI is Deep Learning. Once a domain of researchers and PhDs only, Deep Learning has now gone mainstream thanks to its practical applications and availability in terms of consumable technology and affordable hardware. The demand for Data Scientists and Deep Learning professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Deep Learning skills by employers -- and the job salaries of Deep Learning practitioners -- are only bound to increase over time, as AI becomes more pervasive in society. Deep Learning is a future-proof career. Within this series of courses, you’ll be introduced to concepts and applications in Deep Learning, including various kinds of Neural Networks for supervised and unsupervised learning. You’ll then delve deeper and apply Deep Learning by building models and algorithms using libraries like Keras, PyTorch, and Tensorflow. You’ll also master Deep Learning at scale by leveraging GPU accelerated hardware for image and video processing, as well as object recognition in Computer Vision. Throughout this program you will practice your Deep Learning skills through a series of hands-on labs, assignments, and projects inspired by real world problems and data sets from the industry. You’ll also complete the program by preparing a Deep Learning capstone project that will showcase your applied skills to prospective employers. This program is intended to prepare learners and equip them with skills required to become successful AI practitioners and start a career in applied Deep Learning.

Embark on a comprehensive 22-hour journey through the world of deep learning. Explore the historical context, fundamental concepts, and advanced techniques in this extensive lecture series. Begin with an introduction to machine learning basics and progress through computation graphs, deep neural networks, regularization methods, and optimization strategies. Dive into specialized topics such as convolutional neural networks, sequence models, natural language processing, and graph neural networks. Investigate autoencoders, including variational autoencoders, and conclude with an in-depth look at generative adversarial networks. Gain practical insights into debugging strategies, visualization techniques, and cutting-edge research in the field.

This Learning Plan is designed to help DevOps Engineers, Developers, and Operations Engineers who want to become proficient at deploying secure and reliable applications at high velocity on AWS. The digital training included in this Learning Plan will expose you to version control, infrastructure as code, and continuous integration/continuous delivery (CI/CD). This Learning Plan can also help prepare you for the AWS Certified DevOps Engineer - Professional certification exam. If you are interested in additional resources you can explore the Ramp-Up Guide: DevOps Engineering.

Gain end-to-end expertise in Machine Learning DevOps with this Nanodegree program that focuses on deployment, automation, and monitoring. Develop production-ready skills in CI/CD, container orchestration, and model performance tracking.

Dive into the fundamentals of deep learning in this 30-minute lecture from the Full Stack Deep Learning Spring 2021 course. Explore artificial neural networks, the universal approximation theorem, and three major types of learning problems. Understand the empirical risk minimization problem, grasp the concept behind gradient descent, and learn about back-propagation in practice. Examine core neural architectures and the rise of GPUs in deep learning. Cover topics including neural networks, universality, learning problems, loss functions, gradient descent, backpropagation, automatic differentiation, architectural considerations, and CUDA cores. For those needing a refresher, consult the recommended online book at neuralnetworksanddeeplearning.com before watching.

Explore the challenges and techniques of developing AI-based musical instruments and audio tools in this 42-minute conference talk from the Audio Developer Conference. Gain insights into the design process, required knowledge, and learning curve for AI tools from a DSP engineer's perspective. Survey current AI applications in real-time music making, learn about dataset creation and model deployment for real-time audio, and discover how these concepts were applied to PhD projects like the HITar and Bessel's Trick. Ideal for DSP engineers looking to bridge the gap between acoustic instruments and synthesizers using AI for audio analysis and synthetic sound generation.