Advanced TensorFlow

Complete natural language processing specialization covering transformers, attention mechanisms, and modern NLP techniques.

Master TensorFlow 2 and Keras. ANNs, CNNs, RNNs, GANs, deployment.

This course provides a comprehensive, hands-on introduction to machine learning on the Google Cloud Platform, with a specific focus on Vertex AI. Students will learn about various GCP services, including compute, storage, and databases, before diving into machine learning workflows. The curriculum covers building and deploying models using GCP's AutoML for tabular, image, and text data, as well as custom model training and deployment on the AI Platform and Vertex AI. The course is designed to equip learners with the practical skills needed to create and manage machine learning pipelines on Google Cloud.

This course covers everything from Large Language Models (LL Ms) and prompt engineering to  fine-tuning , as well as advanced concepts like Direct Preference Optimization (DPO). You'll also dive deep into Retrieval-Augmented Generation (RAG), which enhances your LL Ms' capabilities by integrating retrieval systems for more accurate and superior responses.By the end of this course, you'll be equipped to create AI solutions that align perfectly with human intent and outperform standard models.What You Will Get In addition to the core topics, our course features in-depth, real-world case studies on fine-tuning, prompt engineering, and Retrieval-Augmented Generation (RAG). These case studies not only highlight cutting-edge techniques but also offer practical, hands-on insights into their application in real-world AI projects. By exploring actual scenarios and projects, learners will gain a deep understanding of how to effectively utilize these methods to solve complex challenges. The case studies are designed to bridge the gap between theory and practice, enabling participants to see how these advanced techniques are deployed in industry settings.Moreover, these examples provide a step-by-step framework for applying theoretical concepts to real-world applications. Whether it's fine-tuning models for enhanced performance, engineering prompts for improved outputs, or leveraging retrieval systems to augment generation, learners will be able to confidently implement these strategies in their own projects. This ensures that by the end of the course, participants will not only have a solid foundation in generative AI concepts but also the ability to apply them in practical, impactful ways.  

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.

You will learn ChatGPT and Generative AI in this course. In recent years, the field of artificial intelligence (AI) has experienced a transformative shift with the emergence of generative AI technologies. Among the most prominent examples is ChatGPT, a language model developed by OpenAI. Built on the GPT (Generative Pre-trained Transformer) architecture, ChatGPT exemplifies how AI can generate human-like text, enabling new possibilities in communication, creativity, and productivity.Generative AI refers to algorithms capable of creating new content—text, images, music, code, and more—by learning from existing data. Unlike traditional AI, which primarily classifies or analyzes information, generative AI can produce original outputs that mimic human expression. ChatGPT, specifically, has been trained on vast datasets of text from the internet, allowing it to understand context, respond to prompts, and carry on conversations in a coherent and often insightful manner. This ability to generate language that feels natural makes it useful in a wide range of applications, from writing assistance and customer support to education and software development.To unlock the power of generative AI effectively, users should learn prompt engineering—crafting questions or commands to guide the AI toward more accurate and relevant results. With proper guidance, ChatGPT can simulate conversations, summarize documents, draft content, translate languages, write code, and much more. As this technology evolves, its integration with other AI models, including image and video generators, will only increase its impact.ChatGPT and generative AI are reshaping the digital landscape. By understanding how these tools work and using them responsibly, individuals and organizations can unlock unprecedented capabilities, drive innovation, and redefine what is possible in human-AI collaboration.

If you're a budding data enthusiast, developer, or even an experienced professional wanting to make the leap into the ever-growing world of machine learning, have you often wondered how to integrate the power of TensorFlow with the vast scalability of Google Cloud? Do you dream of deploying robust ML models seamlessly without the fuss of infrastructure management?Delve deep into the realms of machine learning with our structured guide on "Machine Learning with TensorFlow on Google Cloud." This course isn't just about theory; it's a hands-on journey, uniquely tailored to help you utilize TensorFlow's prowess on the expansive infrastructure that Google Cloud offers.In this course, you will:Develop foundational models such as Linear and Logistic Regression using TensorFlow.Master advanced architectures like Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for intricate tasks.Harness the power and convenience of Google Cloud's Colab to run Python code effortlessly.Construct sophisticated Jupyter notebooks with real-world datasets on Google Colab and Vertex.But why dive into TensorFlow on Google Cloud? As machine learning solutions become increasingly critical in decision-making, predicting trends, and understanding vast datasets, TensorFlow's integration with Google Cloud is the key to rapid prototyping, scalable computations, and cost-effective solutions.Throughout your learning journey, you'll immerse yourself in a series of projects and exercises, from constructing your very first ML model to deploying intricate deep learning networks on the cloud.This course stands apart because it bridges the gap between theory and practical deployment, ensuring that once you've completed it, you're not just knowledgeable but are genuinely ready to apply these skills in real-world scenarios.Take the next step in your machi

Learn how to use Numpy and Pandas for Data Analysis. This will cover all basic concepts of Numpy and Pandas that are useful in data analysis.Learn to create impactful visualizations using Matplotlib and Seaborn. Creating impactful visualizations is a crucial step in developing a better understanding about your data.This course covers all Data Preprocessing steps like working with missing values, Feature Encoding and Feature Scaling.Learn about different Machine Learning Models like Random Forest, Decision Trees, KNN, SVM, Linear Regression, Logistic regression etc... All the video sessions will first discuss the basic theory concept behind these algorithms followed by the practical implementation.Learn to how to choose the best hyper parameters for your Machine Learning Model using Grid Search CV. Choosing the best hyper parameters is an important step in increasing the accuracy of your Machine Learning Model.You will learn to build a complete Machine Learning Pipeline from Data collection to Data Preprocessing to Model Building. ML Pipeline is an important concept that is extensively used while building large scale ML projects.This course has two projects at the end that will be built using all concepts taught in this course. The first project is about Diabetes Prediction using a classification machine learning algorithm and second is about prediciting the insurance premium using a regression machine learning algorithm.

Discover how Generative Artificial Intelligence is transforming businesses by driving innovation and efficiency across multiple industries! This hands-on course offers an immersive dive into the world of LL Ms (Large Language Models) and AI Agents, equipping you to create intelligent, automated solutions for real-world business challenges.With the rapid advancement of NLP (Natural Language Processing) and language models, companies and professionals are adopting these technologies to boost productivity and make decisions with greater precision. With this in mind, this course was designed to provide you with a practical, direct, and applicable learning experience. You will learn to implement solutions in Python, with a focus on applying LL Ms to business contexts, but with skills that are versatile enough to be adapted to any real-world challenge.Throughout the course, you will master the key tools and frameworks in the generative AI ecosystem, including LangChain, Lang Graph, Llama Index, CrewAI, Agno, and other open-source solutions. Learn to implement LL Ms via Python AP Is (both free and paid) or locally, exploring models such as Llama, Deep Seek, ChatGPT, Gemini, and more — always with an emphasis on real-world, scalable applications.You will act as a professional tasked with addressing diverse business needs. Through 8 practical case studies, you will be challenged to develop useful, customized applications applying generative AI in:Marketing: Create an AI marketing assistant to scale content creation. Effortlessly adapt text for diverse audiences, platforms, and goals.Customer Service & Support: Develop intelligent chatbots with RAG. Use real documents (e.g., manuals, PD Fs) to provide accurate answers to customer questions.Human Resources: Automatically screen resumes and classify candidate

Vom Nutzer zum Strategen: Meistere ChatGPT & Prompt Engineering für echte Resultate – in Marketing, Business & Tech.ChatGPT ist längst mehr als ein nettes Tool. Wer es strategisch einsetzt, gewinnt Tempo, Qualität und Reichweite. In dieser Masterclass machst du den Sprung vom Gelegenheitsnutzer zum Architekten wirksamer KI-Workflows – inkl. praxiserprobter Prompts, Templates und Automatisierung.Was dich erwartet Fundament verstehen: Wie LL Ms funktionieren (Chancen, Grenzen, Halluzinationen), warum Datensicherheit zählt und wie du sauber arbeitest.C.R.A.F.T.-System: Kontext, Rolle, Aufgabe, Format, Ton – dein roter Faden für Prompts, die konsistent Top-Ergebnisse liefern.Fortgeschrittene Techniken: Few-Shot, Negativ-Prompts, Chain/Tree of Thought, Self-Correction, Experten-Komitee, Persona-Design.Iteratives Vorgehen: Follow-ups, Eingrenzung, Schritt-für-Schritt-Anleitungen & Prompt-Ketten für komplexe Aufgaben.Praxisprojekt: Entwicklung einer Content-Strategie für ein fiktives Startup (Zielgruppen/Keywords → Content-Plan → Pillar-Page → Distribution).Marketing mit KI: SEO (Keyword-Cluster, Strukturen, FA Qs, Schema Markup), Social Media (Kalender, virale Hooks, Community-Management),E-Mail-Marketing (Kampagnen, Betreff-A/B-Tests), Ads (Google & Facebook: Texte, Kampagnenstrukturen).Business & Productivity: Recherche, Präsentationen, Entscheidungsfindung (SWOT, Pro/Contra), Meeting-Vor/Nachbereitung, Vorlagen & Workflows.Dev & Data optional: C

The demand for Big Data Hadoop Developers, Architects, Data Scientists, Machine Learning Engineers is increasing day by day and one of the main reason is that companies are more keen these days to get more accurate predictions & forecasting result using data. They want to make sense of data and wants to provide 360 view of customers thereby providing better customer experience. This course is designed in such a way that you will get an understanding of best of both worlds i.e. both Hadoop as well as Data Science. You will not only be able to perform Hadoop related operations to gather data from the source directly but also they can perform Data Science specific tasks and build model on the data collected. Also, you will be able to do transformations using Hadoop Ecosystem tools. So in a nutshell, this course will help the students to learn both Hadoop and Data Science Natural Language Processing in one course. Companies like Google, Amazon, Facebook, Ebay, Linked In, Twitter, and Yahoo! are using Hadoop on a larger scale these days and more and more companies have already started adopting these digital technologies. If we talk about Text Analytics, there are several applications of Text Analytics (given below) and hence companies prefer to have both of these skillset in the professionals. One of the application of text classification is a faster emergency response system can be developed by classifying panic conversation on social media.Another application is automating the classification of users into cohorts so that marketers can monitor and classify users based on how they are talking about products, services or brands online.Content or product tagging using categories as a way to improve browsing experience or to identify related content on the website. Platforms such as news agencies, directories, E-commerce, blogs, content curators, and likes can use automated technologies to classify and tag content a

Master Data Science Workflows with H2O: From Prep to Deployment & Generative AI with Michelle Tanco and Jon Farland!This course equips you with H2O's suite of cutting-edge tools, such as Driverless AI, H2O Actions, the Wave App, Gen AI App Store, LLM Data Studio, H2O LLM Studio, Enterprise GP Te, h2oGPT, and Eval Studio. In this comprehensive course, you will develop a thorough understanding of data preparation and visualization using H2O's intuitive tools, enabling you to efficiently clean, transform, and explore data to uncover actionable insights without the traditional complexities of data wrangling. Dive deep into automated machine learning mastery with Driverless AI, leveraging its automation capabilities to streamline model building processes, allowing you to focus on strategic analysis and solving complex problems effectively. Gain expertise in seamless model deployment techniques, ensuring that your models translate into impactful business outcomes with ease and efficiency. Explore the best of what generative AI has to offer with Enterprise GP Te and H2OGPT, where you will delve into advanced tasks such as text generation, language translation, and creative content development, empowering you to innovate and excel in data science and business decision-making. Join us on this transformative journey to elevate your skills and harness the full potential of H2O's tools for driving data-driven insights and strategic business success.Come aboard our dynamic course and elevate your data science skills!

Der Bedarf an Data-Experten wächst wesentlich schneller als das Angebot an Fachkräften. 2022 fehlten laut einer repräsentativen Bitkom-Umfrage rund 137.000 IT-Fachkräfte in Deutschland. Damit liegt der Mangel sogar noch höher als vor der Pandemie.Die Karriere im Bereich Data Science bietet nicht nur finanzielle Vorteile, sondern auch die Möglichkeit, an den herausforderndsten und faszinierendsten Aufgaben der Welt zu arbeiten. Bist du bereit, den Weg als Data Scientist einzuschlagen?   "Perfektes Niveau, motivierend und verständlich/gründlich erklärt." (★★★★★ P. Fuchs)Dieser Grundlagenkurs richtet sich sowohl an Anfänger, die zum ersten Mal mit Data Science in Berührung kommen, als auch an Entwickler, die ihr Portfolio um Fähigkeiten in Richtung Data Science und Machine Learning ausbauen wollen!Wichtig: Unser Data Science-Kurs erfordert Grundkenntnisse der Programmierung mit Python! Falls du die Grundlagen von Python bisher noch nicht erlernt hast, solltest du zuerst einen unserer Python-Kurse durcharbeiten!Dieser umfassende Kurs ist inhaltlich vergleichbar mit anderen Data Science Bootcamps, die sonst mehrere tausend Euro kosten. Nun kannst Du all das zu einem Bruchteil der Kosten lernen. Und dank der Plattform Udemy lernst Du wann und wo Du möchtest. Mit über 100 HD Video Lektionen und den detaillierten Code Notebooks zu jeder Lektion ist dies einer der umfangreichsten deutschsprachigen Kurse für Data Science und Maschinelles Lernen (Machine Learning) auf Udemy!Wir bringen dir bei, wie man Python zur Analyse von Daten einsetzt, wie man Daten visualisiert und wie Python zum Maschinellen Lernen (Machine Learning) genutzt werden kann! Hier sind einige der Punkte die wir behandeln werd

April 2024 update: This course comes with a certificate of completion that you can share on Linkedin or other social media platforms, and/or add to your resume/CV.Technology is going fast, if you stop moving, you will lose the race.Welcome to "The ChatGPT and Prompt Engineering Masterclass" an online course designed to help you harness the power of ChatGPT, a state-of-the-art language model. Whether you're a professional, content creator, or simply curious about AI, this course equips you with practical knowledge and techniques to make the most of ChatGPT's capabilities.This course will help you understand how ChatGPT works, how to fix common issues, how to write the perfect prompt and best prompt engineering practices, ethics, AI misuse, and all the ways GPT can help you save time and money (and make money!). We will also very briefly talk about how to generate an AI book to sell on Amazon.What are you getting with this course:Over an hour of on-demand video, updated in August 2023.Additional resources (like written articles)The script and PPTX of the course (for free!)My prompt listA possibility to reach out to me if you need anything!Enroll now in "The ChatGPT and Prompt Engineering Masterclass" and unlock the full potential of ChatGPT to revolutionize your personal and professional life. ChatGPT is more than a conversational AI or a writing assistant,  join us and discover the power of ChatGPT!

Imagina crear, en pocos días, una inteligencia artificial que detecte tumores o enseñe a una consola Atari a batir récords, sin ser experto en matemáticas. El secreto está en proyectos guiados paso a paso, esto disparará tu motivación y retención.¿Qué vas a conseguir?Dominar Deep Learning e IA con TensorFlow desde cero, usando explicaciones que cualquier principiante puede entender a la primera.Construir 10 proyectos reales: detector de tumores, diagnóstico Covid con Transfer Learning, agente Atari autónomo, detector de violencia en vídeo y más, para impresionar a reclutadores con tu portafolio de proyectos de Inteligencia Artificial.Aprender con metodología 100 % práctica, probada para multiplicar la retención hasta 15 veces frente a clases teóricas con presentaciones aburridas.¿Por qué te importa?Empresas  buscan talento en IA más que nunca: las vacantes que piden TensorFlow crecieron un 34 % en el último año y pagan hasta un 25 % más que la media STEM. Además, la tecnología de redes neuronales ya supera a radiólogos en ciertas tareas de diagnóstico, de modo que estas habilidades abren puertas que transforman carreras y cambian vidas.Requisitos Solo Python básico y ganas de experimentar—el resto (instalación de librerías, datasets y scripts) lo instalamos juntos en el curso

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 (CNNs) for image recognition. Also, predict share prices with Recurrent Neural Network and Long Short-Term Memory Network (LSTMs). 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 Overview This 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

Complete TensorFlow Mastery For Machine Learning & Deep Learning in PythonTHIS IS A COMPLETE DATA SCIENCE TRAINING WITH TensorFlow IN PYTHON!It is a full 7-Hour Python TensorFlow Data Science Boot Camp that will help you learn statistical modelling, data visualization, machine learning and basic deep learning  using the TensorFlow framework in Python..                         HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical data science using the TensorFlow framework in Python.. This means, this course covers all the aspects of practical data science with TensorFlow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python TensorFlow based data science.  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 TensorFlow is revolutionizing Deep Learning... By storing, filtering, managing, and manipulating data in Python and TensorFlow, 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 PYTHON TensorFlow 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 journa

Avec l'avènement des intelligences artificielles comme ChatGPT et Midjourney, nous vivons une véritable révolution dans le monde de la technologie. Et il est devenu indispensable de posséder des compétences en intelligence artificielle pour rester compétitif sur le marché de l'emploi. Si vous cherchez à développer vos compétences en IA, ce cours est exactement ce dont vous avez besoin pour acquérir les bases nécessaires et vous positionner comme un expert dans ce domaine en pleine croissance.Pourquoi Le deep learning avec TensorFlow et non PyTorch ?Parce que :TensorFlow a été créé par Google en 2015, tandis que PyTorch est apparu en 2017. TensorFlow a donc été utilisé et testé plus longtemps dans des applications de production.TensorFlow est plus adapté aux projets de grande envergure. TensorFlow a été conçu pour être utilisé sur des clusters de machines, ce qui en fait un choix plus approprié pour les projets de grande envergure.TensorFlow offre une grande flexibilité en termes de déploiement. TensorFlow peut être utilisé pour déployer des modèles sur différents types d'appareils, y compris les ordinateurs, les serveurs, les téléphones mobiles et les dispositifs de l'internet des objets.TensorFlow dispose d'un écosystème plus large et est utilisé dans un large éventail d'applications, allant de la reconnaissance d'image et de la vision par ordinateur à la prédiction de séries temporelles et à la modélisation du langage naturel.Les bases mathématiques du Deep Learning : Pas besoin d’être un matheux Cependant, TensorFlow encapsule plusieurs concepts mathématiques de base dont la compréhension est indispensable pour bien entrainer des réseaux de neurones.C’est pourquoi nous débutons cette formation par les bases mathématiques du Deep Learning, mais de façon pratique avec du code et non des formules mathématiques.Si vous avez le niveau Lycée en Mathématique mais pense

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

Machine learning allows you to build more powerful, more accurate and more user friendly software that can better respond and adapt.Many companies are integrating machine learning or have already done so, including the biggest Google, Facebook, Netflix, and Amazon.There are many high paying machine learning jobs.Jump into this fun and exciting course to land your next interesting and high paying job with the projects you’ll build and problems you’ll learn how to solve.In just a matter of hours you'll have new skills with projects to back them up:  Deep dive into machine learning Problems that machine learning solves Types of machine learning Common machine learning structures Steps to building a machine learning model Build a linear regression machine learning model with TensorFlow Test and train the model Python variables and operators Collection types Conditionals and loops Functions Classes and objects Install and import Num PyBuild Num Py arrays Multidimensional Num Py arrays Array indexes and properties Num Py functions Num Py operations And much more!Add new skills to your resume in this project based course: Graph data with Py Plot Customize graphs Build 3D graphs with Py Plot Use TensorFlow to build a program to categorize irises into different species.Build a classification model Track data Implement logic Implement responsiveness Build data structures Replace Python lists with Num Py arrays Build and use Num Py arrays Use common array

TensorFlow is the world’s most widely adopted framework for Machine Learning and Deep Learning. TensorFlow 2.0 is a major milestone due to its inclusion of some major changes making TensorFlow easier to learn and use such as “Eager Execution”. It will support more platforms and languages, improved compatibility and remove deprecated AP Is.This course will guide you to upgrade your skills in Machine Learning by practically applying them by building real-world Machine Learning projects.Each section should cover a specific project on a Machine Learning task and you will learn how to implement it into your system using TensorFlow 2. You will implement various Machine Learning techniques and algorithms using the TensorFlow 2 library. Each project will put your skills to test, help you understand and overcome the challenges you can face in a real-world scenario and provide some tips and tricks to help you become more efficient. Throughout the course, you will cover the new features of TensorFlow 2 such as Eager Execution. You will cover at least 3-4 projects. You will also cover some tasks such as Reinforcement Learning and Transfer Learning.By the end of the course, you will be confident to build your own Machine Learning Systems with TensorFlow 2 and will be able to add this valuable skill to your CV.About the Author Vlad Ionescu is a lecturer at Babes-Bolyai University. He has a PhD in machine learning, a field he is continuously researching and exploring every day with technologies such as Python, Keras, and TensorFlow.His philosophy is “If I can't explain something well enough for most people to understand it, I need to go back and understand it better myself before trying again”. This philosophy helps him to give of his best in his lectures and tutorials.He started as a high school computer science teacher while he was doing his Masters over 5 years ago. Right now, he teaches various university-level courses and tutorials, coverin

You're looking for a complete Convolutional Neural Network (CNNs) course that teaches you everything you need to create a Image Recognition model in Python, right?You've found the right Convolutional Neural Networks course!After completing this course you will be able to:Identify the Image Recognition problems which can be solved using CNNs Models.Create CNNs models in Python using Keras and TensorFlow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning concepts Have a clear understanding of Advanced Image Recognition models such as Le Net, Google Net, VGG16 etc.How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep

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:&nbsp;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?&nbsp;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&nbsp;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 JP Morgan 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

Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common?They are all masters of deep learning. We often hear about AI, or self-driving cars, or the ‘algorithmic magic’ at Google, Facebook, and Amazon. But it is not magic - it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all.Cool, that sounds like a really important skill; how do I become a Master of Deep Learning?There are two routes you can take: The unguided route – This route will get you where you want to go, eventually, but expect to get lost a few times. If you are looking at this course you’ve maybe been there. The 365 route – Consider our route as the guided tour. We will take you to all the places you need, using the paths only the most experienced tour guides know about. We have extra knowledge you won’t get from reading those information boards and we give you this knowledge in fun and easy-to-digest methods to make sure it really sticks.Clearly, you can talk the talk, but can you walk the walk? – What exactly will I get out of this course that I can’t get anywhere else?Good question! We know how interesting Deep Learning is and we love it! However, we know that the goal here is career progression, that’s why our course is business focused and gives you real world practice on how to use Deep Learning to optimize business performance.We don’t just scratch the surface either – It’s not called ‘Skin-Deep’ Learning after all. We fully explain the theory from the mathematics behind the algorithms to the state-of-the-art initialization methods, plus so much more.Theory is no good without putting it into practice, is it? That’s why we give you plenty of opportunities to put this theory to use. Implement cutting edge

This comprehensive course will take you on a journey from the foundational concepts of machine learning and TensorFlow to the creation of advanced, real world deep learning models. I'll start with the basics, giving you a solid understanding of how neural networks work, and progressively build up your skills to tackle complex problems in computer vision, natural language processing (NLP), and more. Through a series of hands-on labs, projects, and practical examples, you'll learn to not only build and train models but also to understand the "why" behind the code, enabling you to confidently solve new and challenging problems.This course is designed for anyone with a basic understanding of Python programming who wants to build a career in machine learning and artificial intelligence. Whether you're a student, a software developer, or a data analyst, this course will provide you with the practical skills and foundational knowledge to become a proficient TensorFlow practitioner.Why Take This Course?Artificial Intelligence is transforming industries worldwide, and deep learning lies at its core. TensorFlow, developed by Google, has become the industry standard library for building and deploying AI applications at scale. This course provides a step by step learning journey, blending theory with hands-on coding so you not only understand concepts but can also implement them in real world projects.By the end of this course, you’ll have the knowledge and confidence to:Understand the foundations of deep learning and TensorFlow.Build simple and complex neural networks from scratch.Train, evaluate, and optimize models using modern techniques.Work with Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and advanced architectures.Deploy machine learning models in real-world scenarios.What You’ll L