Deep Learning with TensorFlow Learning Path

Computer Vision with Deep Learning

Learn TensorFlow 2.0 and Keras for deep learning. Build neural networks for computer vision, NLP, and time series prediction.

Machine learning and Deep Learning have been gaining immense traction lately, but until now Java Script developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. Here comes a browser-based Java Script library, TensorFlow.js to your rescue using which you can train and deploy machine learning models entirely in the browser. If you’re a Java Script developer who wants to enter the field ML and DL using TensorFlow.js, then this course is for you.Towards the end of this course, you will be able to implement Machine Learning and Deep Learning for your own projects using Java Script and the TensorFlow.js library.This course is project-based so you will not be learning a bunch of useless coding practices. At the end of this course, you will have real-world apps to use in your portfolio. We feel that project-based training content is the best way to get from A to B. Taking this course means that you learn practical, employable skills immediately.You can use the projects you build in this course to add to your Linked In profile. Give your portfolio fuel to take your career to the next level.Learning how to code is a great way to jump into a new career or enhance your current career. Coding is the new math and learning how to code will propel you forward in any situation. Learn it today and get a head start for tomorrow. People who can master technology will rule the future.

Deep Learning is one of the most popular fields in computer science today. It has applications in many and very varied domains. With the publishing of much more efficient deep learning models in the early 2010s, we have seen a great improvement in the state of the art in domains like Computer Vision, Natural Language Processing, Image Generation, and Signal Processing. The demand for Deep Learning engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using TensorFlow 2 (the world's most popular library for deep learning, and built by Google) and Hugging Face. We shall start by understanding how to build very simple models (like Linear regression models for car price prediction, text classifiers for movie reviews, binary classifiers for malaria prediction) using TensorFlow and Hugging Face transformers, to more advanced models (like object detection models with YOLO, lyrics generator model with GPT2 and Image generation with GANs)After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep-learning solutions that big tech companies encounter.You will learn:The Basics of TensorFlow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision Transformers Evaluation of Cla

If you are interested in Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNNs), and Convolution Neural Networks (CNNs) with an in-depth and clear understanding, then this course is for you.Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.Hand-on examples are available for you to download.Please watch the first two videos to have a better understanding of the course.TOPICS COVERED What is Machine Learning?Linear Regression Steps to Calculate the Parameters Linear Regression-Gradient Descent using Mean Squared Error (MSE) Cost Function Logistic Regression: Classification Decision Boundary Sigmoid Function Non-Linear Decision Boundary Logistic Regression: Gradient Descent Gradient Descent using Mean Squared Error Cost Function Problems with MSE Cost Function for Logistic Regression In Search for an Alternative Cost-Function Entropy and Cross-Entropy Cross-Entropy: Cost Function for Logistic Regression Gradient Descent with Cross Entropy Cost Function Logistic Regression: Multiclass Classification Introduction to Neural Network Logical Operators Modeling Logical Operators using Perceptron(s)Logical Operators using Combination of Perceptron

Welcome to the "Deep Learning Neural Networks with TensorFlow" course! This comprehensive program is designed to equip you with the essential knowledge and hands-on skills required to navigate the exciting field of deep learning using TensorFlow.Overview: In this course, you will embark on a journey through the fundamentals and advanced concepts of deep learning neural networks. We'll start by providing you with a solid foundation, introducing the core principles of neural networks, including the scenario of Perceptron and the creation of neural networks using TensorFlow.Hands-on Projects: To enhance your learning experience, we have incorporated practical projects that allow you to apply your theoretical knowledge to real-world scenarios. The "Face Mask Detection Application" project in Section 2 and the "Implementing Linear Model with Python" project in Section 3 will provide you with valuable hands-on experience, reinforcing your understanding of TensorFlow.Advanced Applications: Our course goes beyond the basics, delving into advanced applications of deep learning. Section 4 explores the fascinating realm of automatic image captioning for social media using TensorFlow. You will learn to preprocess data, define complex models, and deploy applications, gaining practical insights into the cutting-edge capabilities of deep learning.Why TensorFlow? TensorFlow is a leading open-source deep learning framework, widely adopted for its flexibility, scalability, and extensive community support. Whether you're a beginner or an experienced professional, this course caters to learners of all levels, guiding you through the intricacies of deep learning with TensorFlow.Get ready to unravel the mysteries of neural networks, develop practical skills, and unleash the power of TensorFlow in the dynamic field of deep learning. Join us on this exciting learning journey, and let's dive deep into the

Das sagen Teilnehmer über diesen Kurs:"Sehr aktiver Dozent der sich um die Kursteilnehmer und den Kurs kümmert. Der TensorFlow Kurs hat viele beispiele was mir geholfen hat TensorFlow und Keras besser zu verstehen. Ebenfalls sehr gut waren auch die Begriff erklärungen die einem sehr helfen ML als beginner zu lernen."  - Ibrahim Akkulak"Ich würde den Kurs auf jeden Fall weiter empfehlen. Mehr Content als gedacht und sehr viele Erklärungen. Top!"  - Erik Andrè Thürsam"Der Kurs gefällt mir ganz gut und bringt viele Beispiele ein. Der Saif beantwortet Fragen super schnell und ist sehr hilfsbereit. Empfehle den Kurs sehr für alle die Deep Learning mit vielen Praxisbeispielen lernen möchten."  - Simon Behrens Deep Learning ist eines der angesagtesten Themen weit und breit. Insbesondere wird Deep Learning und Künstliche Neuronale Netze in vielen Technologien in deinem Umfeld eingesetzt, um dir ein noch angenehmeres Leben zu ermöglichen. Mithilfe diesen Praxis-Kurs bringe ich dir bei wie man Deep Learning mithilfe von Keras, TensorFlow und Python einsetzt. Du wirst eine gute Mischung von Theorie und Praxis in diesen Kurs erhalten. Viele der Techniken werden anhand von echten Praxis Projekte dir vermittelt. Warum solltest du Keras lernen? Keras wird von den "Big Five" Unternehmen wie Apple, Google, Facebook, Amazon und Microsoft in vielen ihrer Produkte eingesetzt, um Machine Learning noch effizienter zu nutzen! Ebenfalls werde ich ihn auch immer auf dem neusten Stand der Technik und Wissenschaft halten.  Lerne wie du Keras meisterst und schreibe dich JETZT ein!

You want to start developing deep learning solutions, but you do not want to lose time in mathematics and theory?You want to conduct deep learning projects, but do not like the hassle of tedious programming tasks?Do you want an automated process for developing deep learning solutions?This course is then designed for you! Welcome to Deep Learning in Practice, with NO PAIN!This course is the first course on a series of Deep Learning in Practice Courses of Anis Koubaa, namely Deep Learning in Practice I: TensorFlow 2 Basics and Dataset Design (this course): the student will learn the basics of conducting a classification project using deep neural networks, then he learns about how to design a dataset for industrial-level professional deep learning projects. Deep Learning in Practice II: Transfer Learning and Models Evaluation: the student will learn how to manage complex deep learning projects and develop models using transfer learning using several state-of-the-art CNNs algorithms. He will learn how to develop reusable projects and how to compare the results of different deep learning models in an automated manner. Deep Learning in Practice III: Face Recognition. The student will learn how to build a face recognition app in TensorFlow and Keras.Deep Learning in Practice I: Basics and Dataset Design There are plenty of courses and tutorials on deep learning. However, some practical skills are challenging to find in this massive bunch of deep learning resources, and that someone would spend a lot of time to get these practical skills.This course fills this gap and provides a series of practical lectures with hands-on projects through which I introduce the best practices that deep learning practitioners have to know to conduct deep learning projects.I have

Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice.AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:(1) Train Feed Forward Artificial Neural Networks to perform regression tasks such as sales/revenue predictions and house price predictions(2) Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection.(3) Train Deep Learning models to perform image classification tasks such as face detection, Fashion classification and traffic sign classification.(4) Develop AI models to perform sentiment analysis and analyze customer reviews.(5) Perform AI models visualization and assess their performance using Tensorboard(6) Deploy AI models in practice using TensorFlow 2.0 Serving The course is targeted towards students wanting to gain a fundamental understanding of how to build and deploy models in TensorFlow 2.0. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master AI and Deep Learning techn

Interested in using Machine Learning in Java Script applications and websites? Then this course is for you!This is the tutorial you've been looking for to become a modern Java Script machine learning master in 2024. It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume. From absolute zero knowledge to master - join the TensorFlow.js revolution.This course has been designed by a specialist team of software developers who are passionate about using Java Script with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes.Throughout the course we use house price data to ask ever more complicated questions; “can you predict the value of this house?”, “can you tell me if this house has a waterfront?”, “can you classify it as having 1, 2 or 3+ bedrooms?”. Each example builds on the one before it, to reinforce learning in easy and steady steps.Machine Learning in TensorFlow.js provides you with all the benefits of TensorFlow, but without the need for Python. This is demonstrated using web based examples, stunning visualisations and custom website components.This course is fun and engaging, with Machine Learning learning outcomes provided in bitesize topics:Part 1 - Introduction to TensorFlow.js Part 2 - Installing and running TensorFlow.js Part 3 - TensorFlow.js Core Concepts Part 4 - Data Preparation with TensorFlow.js Part 5 - Defining a model Part 6 - Training and Testing in TensorFlow.js Part 7 - TensorFlow.js Prediction Part 8 - Binary Classification Part 9 - Multi-class Classification Part 10 - Conclusion & Next Steps As a bonus, for every student, we provide you wit

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

This course provides you to be able to build Deep Neural Networks models for different business domains with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras. We will also focus on the advanced topics in this lecture such as transfer learning, autoencoders, face recognition (including those models: VGG-Face, Google Face Net, Open Face and Facebook Deep Face).This course appeals to ones who interested in Machine Learning, Data Science and AI. Also, you don't have to be attend any ML course before.

Just launched with all modern best practices for building neural networks with TensorFlow and becoming a TensorFlow & Deep Learning Expert!Join a live online community of over 900,000+ students and a course taught by a TensorFlow expert. This course will take you from absolute beginner with TensorFlow, to creating state-of-the-art deep learning neural networks.TensorFlow experts earn up to $204,000 USD a year, with the average salary hovering around $148,000 USD. By taking this course you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow Developer!Here is a full course breakdown of everything we will teach (yes, it's very comprehensive, but don't be intimidated, as we will teach you everything from scratch!):The goal of this course is to teach you all the skills necessary for you to become a top 10% TensorFlow Developer.This course will be very hands on and project based. You won't just be staring at us teach, but you will actually get to experiment, do exercises, and build machine learning models and projects to mimic real life scenarios. By the end of it all, you will develop skillsets needed to develop modern deep learning solutions that big tech companies encounter. 0 — TensorFlow Fundamentals Introduction to tensors (creating tensors)Getting information from tensors (tensor attributes)Manipulating tensors (tensor operations)Tensors and Num PyUsing @tf.function (a way to speed up your regular Python functions)Using GP Us with Tensor Flow1 — Neural Network Regression with TensorFlow Build TensorFlow sequential models with multiple layers Prepare data for use with a machine learning model

Welcome to the Complete Deep Learning Course 2021 With 7+ Real Projects This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure TensorFlow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!This course is designed to balance theory and practical implementation, with complete google colab and Jupiter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!This course covers a variety of topics, including Deep Learning.Google Colab Anaconda Jupiter Notebook Activation Function.Keras.Pandas.Seaborn.Feature scaling.Matplotlib.Scikit-Learn Sigmoid Function.Tanh Function.ReLU Function.Leaky Relu Function.Exponential Linear Unit Function.Swish function.Corpora.NLTK.TensorFlow 2.0Tokenization.Spacy.PoS tagging.NER.Stemming and lemmatization.Semantics and topic modelling.Sentiment analysis techniques.Lexicon-based methods.Rule-based methods.Statistical methods.Machine learning methods.</

Deep learning is not like any other technology, but it is in many cases the only technology that can solve certain problems. We need to ensure that all people involved in the project have a common understanding of what is required, how the process works, and that we have a realistic view of what is possible with the tools at hand. To boil down all this to its core components we could consider a few important rules:create a common ground of understanding, this will ensure the right mindsetstate early how progress should be measuredcommunicate clearly how different machine learning concepts worksacknowledge and consider the inherited uncertainty, it is part of the process In order to define AI, we must first define the concept of intelligence in general. A paraphrased definition based on Wikipedia is:Intelligence can be generally described as the ability to perceive information and retain it as knowledge to be applied towards adaptive behaviors within an environment or context.While there are many different definitions of intelligence, they all essentially involve learning, understanding, and the application of the knowledge learned to achieve one or more goals.Is this course for me?By taking this course, you will gain the tools you need to continue improving yourself in the field of app development. You will be able to apply what you learned to further experience in making your own apps able to perform more.No experience necessary. ​Even if you’ve never coded before, you can take this course. ​​One of the best features is that you can watch the tutorials at any speed you want. This means you can speed up or slow down the video if you want to!When your learning to code, you often find yourself following along with a tutor without really knowing why you're doing certain things. In this course, I will demonstrate correct coding as well as mistakes I often see an

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure TensorFlow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! This course covers a variety of topics, including Neural Network Basics TensorFlow Basics Artificial Neural Networks Densely Connected Networks Convolutional Neural Networks Recurrent Neural Networks Auto Encoders Reinforcement Learning OpenAI Gymand much more! There are many Deep Learning Frameworks out there, so why use TensorFlow? TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CP Us or GP Us in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system

USED BY SOFTWARE STUDENTS AT CAMBRIDGE UNIVERSITY - WORLD CLASS DEEP LEARNING COURSE - UPDATED CONTENT January 2018 Master practical deep learning and neural network concepts and fundamentals My course does exactly what the title describes in a simple, relatable way. I help you to grasp the complete start to end concepts of fundamental deep learning. Why you need this course Coming to grips with python isn't always easy. On your own it can be quite confusing, difficult and frustrating. I've been through the process myself, and with the help of lifelong ... I want to share this with my fellow beginners, developers, AI aspirers, with you. What you will get out of this course I will give you straightforward examples, instructions, advice, insights and resources for you to take simple steps to create your own neural networks from scratch. By the end of the course you will be able to create neural networks to create your very own image classifier, able to work on your own images. I personally provide support within the course, answering questions and giving feedback on what you're discovering/creating along the way. I don't just throw you in at the deep end - I provide you with the resources to learn and develop what you need at a pace to work for you and then help you stroll through to the finish line. Studies have shown that to learn effectively from online courses tutorials should last around ten minutes each. Therefore to maximise your learning experience all of the lectures in this course have been created around this amount of time. My course integrates all of the aspects required to get you on the road becoming a successful deep learning developer. I teach and I preach, with live, practical exercises and walkthroughs at the end of each section!

This Course simplifies the advanced Deep Learning concepts like Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short Term Memory (LSTMs), Gated Recurrent Units(GRUs), etc. TensorFlow, Keras, Google Colab, Real World Projects and Case Studies on topics like Regression and Classification have been described in great detail. Advanced Case studies like Self Driving Cars will be discussed in great detail. Currently the course has few case studies.The objective is to include at least 20 real world projects soon.&nbsp; Case studies on topics like Object detection will also be included. TensorFlow and Keras basics and advanced concepts have been discussed in great detail.&nbsp; The ultimate goal of this course is to make the learner able to solve real world problems using deep learning. After completion of this course the Learner shall also be able to pass the Google TensorFlow Certification Examination which is one of the prestigious Certification. Learner will also get the certificate of completion from Udemy after completing the Course. After taking this course the learner will be expert in following topics.&nbsp;a) Theoretical Deep Learning Concepts.b) Convolutional Neural Networksc) Long-short term memoryd) Generative Adversarial Networkse) Encoder- Decoder Modelsf) Attention Modelsg) Object detectionh) Image Segmentationi) Transfer Learningj) OpenCV using Pythonk) Building and deploying Deep Neural Networks&nbsp;l) Professional Google TensorFlow developer&nbsp;m) Using Google Colab for writing Deep Learning coden) Python programming for Deep Neural Networks The Learners are advised to practice the TensorFlow code as they watch the videos on Programming from this course.&nbsp;First Few sections have been uploaded, The course is in updation phase and the remaining sections will be added soon.

Welcome to our Python &amp; TensorFlow for Machine Learning complete course. This intensive program is designed for both beginners eager to dive into the world of data science and seasoned professionals looking to deepen their understanding of machine learning, deep learning, and TensorFlow's capabilities.Starting with Python—a cornerstone of modern AI development—we'll guide you through its essential features and libraries that make data manipulation and analysis a breeze. As we delve into machine learning, you'll learn the foundational algorithms and techniques, moving seamlessly from supervised to unsupervised learning, paving the way for the magic of deep learning.With TensorFlow, one of the most dynamic and widely-used deep learning frameworks, we'll uncover how to craft sophisticated neural network architectures, optimize models, and deploy AI-powered solutions. We don't just want you to learn—we aim for you to master. By the course's end, you'll not only grasp the theories but also gain hands-on experience, ensuring that you're industry-ready.Whether you aspire to innovate in AI research or implement solutions in business settings, this comprehensive course promises a profound understanding, equipping you with the tools and knowledge to harness the power of Python, Machine Learning, and TensorFlow.We're excited about this journey, and we hope to see you inside!

Do you want to learn about Web Development and Machine learning at the same time? With this course you can do exactly that and more!This course was funded by a wildly successful Kickstarter With the Deep Learning of Angular 2 and TensorFlow, You will learn about Javascript frameworks for creating websites and create Apps driven by Machine Learning by learning TensorFlow as well as Py Charm, Python, Android Studio and more!About TensorFlow: ​​We use frameworks like TensorFlow that make it easy to build, train, test, and use machine learning models. TensorFlow makes machine learning so much more accessible to programmers everywhere You can expect a complete and comprehensive course that guides you first through the basics, then through some simple models. You will end up with a ​portfolio of apps driven by machine learning, as well as the know-how to create more and expand upon what we build together.About Angular 2: ​​Java Script is one of the fundamental languages of the web. Java Script is easy to program in but some tasks are difficult. Java Script frameworks are built to make these difficult tasks easier. In this course you will learn how to code with ​Angular.js 2,​​ ​a powerful framework that makes building web apps a breeze​​. In this course you will learn web programming fundamentals and other valuable skill boosting career knowledge.This course is ​project based​​ so you will not be learning a bunch of useless coding practices. At the end of this course you will have real world apps to use in your portfolio. We feel that project based training content is the best way to get from A to B. Taking this course means that you learn practical, employable ​skills immediately.Also, now included in this course are bonus courses of other related topics, such as Cand Java! You get more content at a great price!En