Deep Learning Fundamentals

Learn the fundamentals of deep learning including neural networks, CNNs, RNNs, and hands-on with TensorFlow and Keras.

AI is omnipresent in our modern world. It is in your phone, in your laptop, in your car, in your fridge and other devices you would not dare to think of. After thousands of years of evolution, humanity has managed to create machines that can conduct specific intelligent tasks when trained properly. How? Through a process called machine learning or deep learning, by mimicking the behaviour of biological neurons through electronics and computer science. Even more than it is our present, it is our future, the key to unlocking exponential technological development and leading our societies through wonderful advancements. As amazing as it sounds, it is not off limits to you, to the contrary!We are both engineers, currently designing and marketing advanced ultra light electric vehicles. Albert is a Mechanical engineer specializing in advanced robotics and Eliott is an Aerospace Engineer specializing in advanced space systems with past projects completed in partnership with the European Space Agency. The aim of this course is to teach you how to fully, and intuitively understand neural networks, from their very fundamentals. We will start from their biological inspiration through their mathematics to go all the way to creating, training and testing your own neural network on the famous MNIST database.It is important to note that this course aims at giving you a complete and rich understanding of neural networks and AI, in order to give you the tools to create your own neural networks, whatever the project or application. We do this by taking you through the theory to then apply it on a very hands-on MATLAB project, the goal being for you to beat our own neural network's performance!This course will give you the opportunity to understand, use and create:How to emulate real brains with neural networks.How to represent and annotate neural networks.How to build and compute neural ne

Learn Deep Learning from scratch. It is the extension of a Machine Learning, this course is for beginner who wants to learn the fundamental of deep learning and artificial intelligence. The course includes video explanation with introductions (basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way. It's highly recommended for the students who don’t know the fundamental of machine learning studying at college and university level.The main goal of publishing this course is to explain the deep learning and artificial intelligence in a very simple and easy way. All the codes have been conducted through colab which is an online editor. Python remains a popular choice among numerous companies and organization. Python has a reputation as a beginner-friendly language, replacing Java as the most widely used introductory language because it handles much of the complexity for the user, allowing beginners to focus on fully grasping programming concepts rather than minute details. Below is the list of different topics covered in Deep Learning:Introduction to Deep Learning Artificial Neural Network vs Biological Neural Network Activation Functions Types of Activation functions Artificial Neural Network (ANNs) model Complex ANNs model Forward ANNs model Backward ANNs model Python project of ANNs model<strong

Hello there,Welcome to the “Artificial Intelligence with Machine Learning, Deep Learning ” course Artificial intelligence, Machine learning python, python, machine learning, Django, ethical hacking, python Bootcamp, data analysis, machine learning python, python for beginners, data science, machine learning, Django Artificial Intelligence (AI) with Python Machine Learning and Python Deep Learning, Transfer Learning, TensorFlow It’s hard to imagine our lives without machine learning Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the i Phone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models Ai, TensorFlow, PyTorch, Scikit-Learn, reinforcement learning, supervised learning, teachable machine, python machine learning, TensorFlow python, ai technology, azure machine learning, semi-supervised learning, deep neural network, artificial general intelligence Machine learning isn’t just useful for predictive texting or smartphone voice recognition Machine learning is constantly being applied to new industries and new problems Whether you’re a marketer, video game designer, or programmer, my course on Udemy is here to help you apply machine learning to your work Data Science Careers Are Shaping The Future Data science experts are needed in almost every field, from government security to dating apps Millions of businesses and government departments rely on big data to succeed and better serve their customers So data science careers are in high demand Udemy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for you If you want to learn one of the

Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python’s best application is in deep learning and artificial intelligence tasks.While Python makes deep learning easy, it will still be quite frustrating for someone with no knowledge of how machine learning works in the first place.If you know the basics of Python and you have a drive for deep learning, this course is designed for you. This course will help you learn how to create programs that take data input and automate feature extraction, simplifying real-world tasks for humans.There are hundreds of machine learning resources available on the internet. However, you're at risk of learning unnecessary lessons if you don't filter what you learn. While creating this course, we've helped with filtering to isolate the essential basics you'll need in your deep learning journey.It is a fundamentals course that’s great for both beginners and experts alike. If you’re on the lookout for a course that starts from the basics and works up to the advanced topics, this is the best course for you.It only teaches what you need to get started in deep learning with no fluff. While this helps to keep the course pretty concise, it’s about everything you need to get started with the topic.

YOUR COMPLETE GUIDE TO PRACTICAL NEURAL NETWORKS &amp; DEEP LEARNING IN R: This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level!LEARN FROM AN EXPERT DATA SCIENTIST:My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic . This course will give you a robust grounding in the main aspects of practical neural networks and deep learning. Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science... You will go all the way from carrying out data reading &amp; cleaning to to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R.Among other things:You will be introduced to powerful R-based deep learning packages such as h2o and MXNET. You will be introduced to deep neural networks (DNNs), convolution neural networks (CNNs) and recurrent neural networks (RNNs). You will learn to apply these frameworks to real life data including credit card fraud data, tumor data, images among others for c

Welcome to our comprehensive course on Deep Learning with R! This course is designed to provide you with a thorough understanding of deep learning principles and their practical implementation using the R programming language.In this course, you will embark on a journey into the fascinating world of neural networks and heuristics, gaining the skills and knowledge necessary to leverage the power of deep learning for various applications. Whether you're a beginner or an experienced data scientist, this course offers something for everyone.Section 1: Deep Learning: Neural Networks With RIn the first section, you will dive into the fundamentals of deep learning using neural networks. Starting with dataset review and dataframe creation, you'll learn how to manipulate data effectively for analysis. Through practical exercises, you'll gain hands-on experience in running neural network code and generating outputs from datasets. By the end of this section, you'll be equipped with the foundational skills needed to build and train neural networks using R.Section 2: Deep Learning: Heuristics Using RIn the second section, you'll explore advanced techniques in deep learning, focusing on the application of heuristics using R. From descriptive statistics generation to linear regression modeling, you'll learn how to analyze datasets related to cryptocurrencies, energy sectors, and financial markets. Through a series of practical examples, you'll master the art of data manipulation and visualization, gaining insights into complex relationships between variables.By the end of this course, you'll have a solid understanding of deep learning principles and the ability to apply them confidently in real-world scenarios using R. Whether you're interested in predictive modeling, pattern recognition, or data analysis, this course will empower you to unlock the full potential of deep learning with R. Let's dive in and explore the exciting world of neural networks

Welcome to the course on Data Science &amp; Deep Learning for Business™ 20 Case Studies!This course teaches you how Data Science &amp;&nbsp;Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies.&nbsp; Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade!What student reviews of this course are saying, "I'm only half way through this course, but i have to say WOW. It's so far, a lot better than my Business Analytics MSc I took at UCL. The content is explained better, it's broken down so simply. Some of the Statistical Theory and ML theory lessons are perhaps the best on the internet! 6 stars out of 5!""It is pretty different in format, from others. The appraoch taken here is an end-to-end hands-on project execution, while introducing the concepts. A learner with some prior knowledge will definitely feel at home and get to witness the thought process that happens, while executing a real-time project. The case studies cover most of the domains, that are frequently asked by companies. So it's pretty good and unique, from what i have seen so far. Overall Great learning and great content."--"Data Scientist has become the top job in the US for the last 4 years running!" according to Harvard Business Review &amp;&nbsp;Glassdoor.However, Data Science has a difficult learning curve - How does one even get started in this industry awash with mystique, confusion, impossible-looking mathematics, and code? Even if you get your feet wet, applying your newfound Data Science knowledge to a real-world problem is even more confusing.This course seeks to fill all those gaps in knowledge that scare off

Are you looking for a Machine Learning and Deep Learning course explained in Tamil?This course is designed for Tamil-speaking learners who want to master AI, ML, and DL concepts from the basics to advanced with clear explanations and practical examples.Machine Learning and Deep Learning are at the core of Artificial Intelligence (AI) and are widely used in real-world applications such as speech recognition, computer vision, chatbots, healthcare, recommendation systems, and much more.In this A to Z Tamil course, we’ll cover everything step by step in simple Tamil explanations so that even beginners can understand complex concepts easily.What You’ll Learn in This Course Introduction to Machine Learning (ML) &amp; Artificial Intelligence (AI)Types of Machine Learning:Supervised Learning Unsupervised Learning Reinforcement LearningML Algorithms explained in Tamil:Linear &amp; Logistic Regression Decision Trees &amp; Random ForestsKNN &amp; Naive Bayes Clustering (K-Means, Hierarchical)Deep Learning Concepts Artificial Neural Networks (ANNs)Convolutional Neural Networks (CNNs)Recurrent Neural Networks (RNNs, LSTMs, GRUs)Transfer Learning &amp; Pretrained Models Why Take This Course?Explained 100% in Tamil – No confusion, easy to follow Covers both theory and practical insightsA to Z coverage of Machine Learning and Deep Learning Beginner-friendly with real-world exampl

Python est reconnu comme l'un des meilleurs langages de programmation pour sa flexibilité. Il fonctionne dans presque tous les domaines, du développement Web au développement d'applications financières. Cependant, ce n'est un secret pour personne que la meilleure application de Python est dans les tâches d'apprentissage automatique, d'apprentissage en profondeur et d'intelligence artificielle.Bien que Python facilite l'utilisation du Machine Learning et du Deep Learning, il sera toujours assez frustrant pour quelqu'un qui n'a aucune connaissance du fonctionnement de l'apprentissage automatique.Si vous connaissez les bases de Python et que vous avez envie d'apprendre le Deep Learning, ce cours est fait pour vous. Ce cours vous aidera à apprendre à créer des programmes qui acceptent la saisie de données et automatisent l'extraction de fonctionnalités, simplifiant ainsi les tâches du monde réel pour les humains.Il existe des centaines de ressources d'apprentissage automatique disponibles sur Internet. Cependant, vous risquez d'apprendre des leçons inutiles si vous ne filtrez pas ce que vous apprenez. Lors de la création de ce cours, nous avons tout filtré pour isoler les bases essentielles dont vous aurez besoin dans votre parcours d'apprentissage en profondeur.C'est un cours de base qui convient aussi bien aux débutants qu'aux experts. Si vous êtes à la recherche d'un cours qui commence par les bases et passe aux sujets avancés, c'est le meilleur cours pour vous.Il enseigne uniquement ce dont vous avez besoin pour vous lancer dans l'apprentissage automatique et l'apprentissage en profondeur sans fioritures. Bien que cela aide à garder le cours assez concis, il s'agit de tout ce dont vous avez besoin pour commencer avec le sujet.

Why this Course?Lot of us might have experienced difficulty when relating Machine Learning and Deep Learning models. This course aims to answer usual doubts such as,Why Deep Learning?Why Neural Network performs better than Machine Learning models?Deep Learning and Machine Learning are totally different technologies or they are much related?How Deep Learning evolved from Machine Learning?What it Covers?The course covers Machine Learning models such as Linear Regression, Perceptron, Logistic Regression and a Deep Learning model Dense Neural Network. The four chapters (videos) of the course deal with the adult life of a Legend named Mr. S and show how he used the Machine Learning and Deep Learning models to solve interesting problems such as partying, dating, searching for soulmate and eventually marrying the suitable girl in his life. Through the journey of Mr.&nbsp;S, you will finally get to know why Neural Network performs better &amp;&nbsp;how Machine Learning and Deep Learning are related. Videos contain interesting scenarios with simple numerical examples and explanations.Who can opt for this Course?This course will be highly useful for those individuals,Who does/doesn't have CS background and wants to understand Deep Learning technically without coding &amp;&nbsp;too much mathematics.Who are getting started with Machine Learning or Deep Learning.Who seeks the answer: Why Neural Network perform better than Machine Learning models and how Deep Learning evolved from Machine Learning.Who does research AI and have fundamental doubts about functionality of Neural Networks.

As aplicações de Inteligência Artificial (IA) com Python têm desempenhado um papel significativo no setor financeiro, trazendo uma série de benefícios e transformando a forma como as instituições lidam com dados e tomam decisões. Aqui está um resumo da importância dessas aplicações em finanças:1. Tomada de Decisão Baseada em Dados:&nbsp; &nbsp;- A IA com Python capacita as instituições financeiras a tomar decisões mais informadas e precisas, utilizando algoritmos avançados para analisar grandes conjuntos de dados. Isso resulta em estratégias mais eficazes de investimento, gestão de riscos aprimorada e decisões mais fundamentadas.2. Previsão de Mercado e Tendências:&nbsp; &nbsp;- Algoritmos de machine learning e modelos de IA são utilizados para prever movimentos de mercado, identificar tendências e realizar análises preditivas. Isso auxilia investidores, traders e gestores de ativos na identificação de oportunidades e na mitigação de riscos.3. Detecção de Fraudes e Segurança:&nbsp; &nbsp;- Sistemas de IA são empregados para detectar padrões suspeitos e atividades fraudulentas em transações financeiras. Essa capacidade de análise em tempo real contribui para a segurança das transações e a proteção contra atividades fraudulentas.4. Gestão de Portfólio Automatizada:&nbsp; &nbsp;- Algoritmos de IA e aprendizado de máquina são usados para criar e otimizar automaticamente portfólios de investimento. Esses sistemas automatizados podem ajustar dinamicamente as alocações de ativos com base em condições de mercado em constante mudança.5. Atendimento ao Cliente e Chatbots:&nbsp; &nbsp;- A IA é aplicada em chatbots e assistentes virtuais para melhorar o atendimento ao cliente. Essas soluções são capazes de responder a consultas, fornecer informações sobre contas e até mesmo realizar transações simples, melhorando a eficiência e a experiência do cliente.6. Análise de Sentimento e Mí

Are you looking to gain in-depth knowledge of machine learning and deep learning? If yes, then this Learning Path just right for you. Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. R is one of the leading technologies in the field of data science. Starting out at a basic level, this Learning Path will teach you how to develop and implement machine learning and deep learning algorithms using R in real-world scenarios. The Learning Path begins with covering some basic concepts of R to refresh your knowledge of R before we deep-dive into the advanced techniques. You will start with setting up the environment and then perform data ETL in R. You will then learn important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Next, you will understand the basics of deep learning and artificial neural networks and then move on to exploring topics such as ANNs, RNNs, and CNNs. Finally, you will learn about the applications of deep learning in various fields and understand the practical implementations of scalability, HPC, and feature engineering. By the end of the Learning Path, you will have a solid knowledge of all these algorithms and techniques and be able to implement them efficiently in your data science projects. Do not worry if this seems too far-fetched right now; we have combined the best works of the following esteemed authors to ensure that your learning journey is smooth: About the Authors Selva Prabhakaran is a data scientist with a large e-commerce organization. In his 7 years of experience in data science, he has tackled comp

Welcome to the gateway to your journey into Python for Machine Learning &amp; Deep Learning!Unlock the power of Python and delve into the realms of Machine Learning and Deep Learning with our comprehensive course. Whether you're a beginner eager to step into the world of artificial intelligence or a seasoned professional looking to enhance your skills, this course is designed to cater to all levels of expertise.What sets this course apart?Comprehensive Curriculum: Our meticulously crafted curriculum covers all the essential concepts of Python programming, machine learning algorithms, and deep learning architectures. From the basics to advanced techniques, we've got you covered.Hands-On Projects: Theory is important, but practical experience is paramount. Dive into real-world projects that challenge you to apply what you've learned and reinforce your understanding.Expert Guidance: Learn from industry expert who has years of experience in the field. Benefit from his insights, tips, and best practices to accelerate your learning journey.Interactive Learning: Engage in interactive lessons, quizzes, and exercises designed to keep you motivated and actively involved throughout the course.Flexibility: Life is busy, and we understand that. Our course offers flexible scheduling options, allowing you to learn at your own pace and convenience.Career Opportunities: Machine Learning and Deep Learning are in high demand across various industries. By mastering these skills, you'll open doors to exciting career opportunities and potentially higher earning potential.Are you ready to embark on an exhilarating journey into the world of Python for Machine Learning &amp; Deep Learning? Enroll now and take the first step towards becoming a proficient AI practitioner!

Machine learning is an extremely hot area in Artificial Intelligence and Data Science. There is no doubt that Neural Networks are the most well-regarded and widely used machine learning techniques. A lot of Data Scientists use Neural Networks without understanding their internal structure. However, understanding the internal structure and mechanism of such machine learning techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new Neural Networks for different projects. This course is the easiest way to understand how Neural Networks work in detail. It also puts you ahead of a lot of data scientists. You will potentially have a higher chance of joining a small pool of well-paid data scientists. Why learn Neural Networks as a Data Scientist? Machine learning is getting popular in all industries every single month with the main purpose of improving revenue and decreasing costs. Neural Networks are extremely practical machine learning techniques in different projects. You can use them to automate and optimize the process of solving challenging tasks. &nbsp;What does a data scientist need to learn about Neural Networks? &nbsp;The first thing you need to learn is the mathematical models behind them. You cannot believe how easy and intuitive the mathematical models and equations are. This course starts with intuitive examples to take you through the most fundamental mathematical models of all Neural Networks. There is no equation in this course without an in-depth explanation and visual examples. If you hate math, then sit back, relax, and enjoy the videos to learn the math behind Neural Networks with minimum efforts. It is also important to know what types of problems can be solved with Neural Networks. This course shows different types of problems to solve using Neural Networks including clas

Want to dive into Deep Learning and can't find a simple yet comprehensive course?Don't worry you have come to the right place.We provide easily digestible lessons with plenty of programming question to fill your coding appetite. All topic are thoroughly explained and NO MATH BACKGROUND IS NEEDED. This class will give you a head start among your peers.This class contains fundamentals of Image Classification with TensorFlow.This course will teach you everything you need to get started.

Machine Learning Para Todos: Fundamentos Básicos de la IA¿Sientes curiosidad por la Inteligencia Artificial pero te parece un mundo complejo? Este curso te desmitifica el Machine Learning, brindándote una base sólida y accesible, ¡sin necesidad de experiencia previa en programación o matemáticas avanzadas!"Machine Learning Para Todos" está diseñado para cualquier persona con curiosidad por la IA y el deseo de comprender cómo funciona el aprendizaje automático. No se requieren conocimientos previos especializados; solo una mente abierta y ganas de aprender. Ya seas un profesional buscando nuevas habilidades, un estudiante explorando campos emergentes o simplemente alguien interesado en la tecnología del futuro, este curso te proporcionará una base sólida para comprender y aplicar los fundamentos del Machine Learning.A través de explicaciones claras, ejemplos prácticos y ejercicios sencillos, descubrirás los conceptos fundamentales detrás de la IA que está transformando nuestro mundo. Aprenderás qué es el Machine Learning, cómo funciona, los diferentes tipos de algoritmos (como regresión y clasificación), y cómo se aplican en situaciones reales, desde recomendaciones personalizadas hasta detección de fraudes.Al finalizar este curso, tendrás una comprensión clara de los conceptos fundamentales del Machine Learning, la capacidad de identificar problemas que pueden resolverse con estas técnicas y el conocimiento básico para seguir explorando este campo en temas como Deep Learning o Redes Neuronales Profundas, Inteligencia Artificial&nbsp;Generativa y Agentes IA. ¡Únete a nosotros y desbloquea el potencial de la Inteligencia Artificial!

You're looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, Python, R or Deep Learning, right?You've found the right Machine Learning course!After completing this course you will be able to:· Confidently build predictive Machine Learning and Deep Learning models using R, Python to solve business problems and create business strategy· Answer Machine Learning, Deep Learning, R, Python related interview questions· Participate and perform in online Data Analytics and Data Science competitions such as Kaggle competitions Check out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn.How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.If you are a business manager or an executive, or a student who wants to learn and apply machine learning and deep learning concepts in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning and deep learning. You will also get exposure to data science and data analysis tools like R and Python.Why should you choose this course?This course covers all the steps that one should take while solving a business problem through linear regression. It also focuses Machine Learning and Deep Learning techniques in R and Python.Most courses only focus on teaching how to run the data analysis but we believe that what happens before and after running data analysis is even more important i.e. before running data analysis it is very important that you have the right data and do some pre-processing on it. And after running data analysis, you should be able to judge how good your m