Intermediate TensorFlow

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.

In this course you will learn how to deploy Machine Learning Deep Learning Models using various techniques.&nbsp; This course takes you beyond model development and explains how the model can be consumed by different applications with hands-on examples Course Structure:Creating a Classification Model using Scikit-Learn Saving the Model and the standard Scaler Exporting the Model to another environment - Local and Google Colab Creating a REST API using Python Flask and using it locally Creating a Machine Learning REST API on a Cloud virtual server Creating a Serverless Machine Learning REST API using Cloud Functions Building and Deploying TensorFlow and Keras models using TensorFlow Serving Building and Deploying&nbsp; PyTorch Models Converting a PyTorch model to TensorFlow format using ONNX Creating REST API for PyTorch and TensorFlow Models Deploying tf-idf and text classifier models for Twitter sentiment analysis Deploying models using TensorFlow.js and Java Script Tracking Model training experiments and deployment with MLF Low Running ML Flow on Colab and Databricks Appendix - Generative AI - Miscellaneous Topics.OpenAI and the history of GPT models Creating an OpenAI account and invoking a text-to-speech model from Python code Invoking OpenAI Chat Completion, Text Generation, Image Generation models from Python code Creating a Chatbot with OpenAI API and ChatGPT Model using Python on Google Colab ChatGPT, Large Language Models (LLM) and prompt engineering New Section : Agent-Mode Model Building and Deployment with Git Hub Copilot Vibe Coding: Model Development with Git Hub Copilot Using a Single Prompt<li

Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again.In this course, we cover everything you need to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR.We cover several key NLP frameworks including:Hugging Face's Transformers TensorFlow 2Py Torchspa CyNLTK Flair And learn how to apply transformers to some of the most popular NLP use-cases:Language classification/sentiment analysis Named entity recognition (NER)Question and Answering Similarity/comparative learning Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:History of NLP and where transformers come from Common preprocessing techniques for NLP The theory behind transformers How to fine-tune transformers We cover all this and more, I look forward to seeing you in the course!

In this course I will cover, how to develop a Sentiment Analysis model to categorize a tweet as Positive or Negative using NLP techniques and Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model and finally deploying the same on Cloud platforms to let your customers interact with your model via an user interface.This course will walk you through the initial data exploration and understanding, data analysis, data pre-processing, data preparation, model building, evaluation and deployment techniques. We will explore NLP concepts and then use multiple ML algorithms to create our model and finally focus into one which performs the best on the given dataset.At the end we will learn to create an User Interface to interact with our created model and finally deploy the same on Cloud.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1&nbsp; :&nbsp; Installing Packages.Task 2&nbsp; :&nbsp; Importing Libraries.Task 3&nbsp; :&nbsp; Loading the data from source.Task 4&nbsp; :&nbsp; Understanding the data Task 5&nbsp; :&nbsp; Preparing the data for pre-processing Task 6&nbsp; :&nbsp; Pre-processing steps overview Task 7&nbsp; :&nbsp; Custom Pre-processing functions Task 8&nbsp; :&nbsp; About POS tagging and Lemmatization Task 9&nbsp; :&nbsp; POS tagging and lemmatization in action.Task 10 :&nbsp; Creating a word cloud of positive and negative tweets.Task 11 :&nbsp; Identifying the most frequent set of words in the dataset for positive and negative cases.Task 12 :&nbsp; Train Test Split Task 13 :&nbsp; About TF-IDF Vectorizer Task 14 :&nbsp; TF-IDF Vectorizer in action Task 15 :&nbsp; About Confusion Matrix Task 16 :&nbsp; About Classification Report Task 17 :&nbsp; About AUC-ROCT

Course Workflow:This course is focused on Embedded Deep learning in Python . Raspberry PI 4 is utilized as a main hardware and we will be building practical projects with custom data .We will start with trigonometric functions approximation . In which we will generate random data and produce a model for Sin function approximation Next is a calculator that takes images as input and builds up an equation and produces a result .This Computer vision based project is going to be using convolution network architecture for Categorical classification Another amazing project is focused on convolution network but the data is custom voice recordings . We will involve a little bit of electronics to show the output by controlling our multiple LE Ds using own voice .Unique learning point in this course is Post Quantization applied on TensorFlow models trained on Google Colab . Reducing size of models to 3 times and increasing inferencing speed up to 0.03 sec per input . Sections&nbsp; :Non-Linear Function Approximation Visual Calculator Custom Voice Controlled Led Outcomes After this Course : You can create Deep Learning Projects on Embedded Hardware Convert your models into TensorFlow Lite models Speed up Inferencing on embedded devices Post Quantization Custom Data for Ai Projects Hardware Optimized Neural Networks Computer Vision projects with OpenCV Deep Neural Networks with fast inferencing Speed Hardware Requirements Raspberry PI 412V Power Bank2 LE Ds ( Red and Green )Jumper Wires Bread

Common scenario: You try to get into machine learning and data science, but there's SO&nbsp;MUCH&nbsp;MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Linear Algebra is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science.The "data" in data science is represented using matrices and vectors, which are the central objects of study in this course.If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know linear algebra.In a normal STEM college program, linear algebra is split into multiple semester-long courses.Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of semesters.This course will cover systems of linear equations, matrix operations (dot product, inverse, transpose, determinant, trace), low-rank approximations, positive-definiteness and negative-definiteness, and eigenvalues and eigenvectors. It will even include machine learning-focused material you wouldn't normally see in a regular college course, such as how these concepts apply to GPT-4, and fine-tuning modern neural networks like diffusion models (for generative AI&nbsp;art) and LL Ms (Large Language Models) using LoRA. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of linear algebra, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can s

Uniform modeling (i.e. models from a diverse set of libraries, including Scikit-Learn, statsmodels, and TensorFlow), reporting, and data visualizations are offered through the Scalecast interfaces. Data storage and processing then becomes easy as all applicable data, predictions, and many derived metrics are contained in a few objects with much customization available through different modules.The ability to make predictions based upon historical observations creates a competitive advantage. For example, if an organization has the capacity to better forecast the sales quantities of a product, it will be in a more favorable position to optimize inventory levels. This can result in an increased liquidity of the organizations cash reserves, decrease of working capital and improved customer satisfaction by decreasing the backlog of orders. In the domain of machine learning, there’s a specific collection of methods and techniques particularly well suited for predicting the value of a dependent variable according to time, ARIMA is one of the important technique.LSTMs is the Recurrent Neural Network (RNNs) used in deep learning for its optimized architecture to easily capture the pattern in sequential data. The benefit of this type of network is that it can learn and remember over long sequences and does not rely on pre-specified window lagged observation as input. The scalecast library hosts a TensorFlow LSTMs that can easily be employed for time series forecasting tasks. The package was designed to take a lot of the headache out of implementing time series forecasts. It employs TensorFlow under-the-hood. Some of the features are:Lag, trend, and seasonality selection Hyperparameter tuning using grid search and time series Transformations Scikit models ARIMALSTM Multivariate- Assignment

Note: Have opened many videos for preview&nbsp; please only enroll if you follow the preview video's , Any suggestions or modifications ping me in Q&amp;A will respond within 3 business days in most times worst case 7 days if i am travelling&nbsp; Current Topics&nbsp; What are Transformers?&nbsp; (Technical) Concept of RNN3 main concepts in Transformers Positional Encoding Attention Self-Attention What is ChatGPT?&nbsp; (Technical) How You Can Use ChatGPT (Non-Technical) Creating your Generative Pre-trained Transformer 3 (GPT-3) account Basic Querying Generative Pre-trained Transformer 3 (GPT-3)&nbsp; and how ethical principles are upheld&nbsp;Prompt Engineering&nbsp; (Technical) Best Practices for&nbsp; Prompt Engineering OpenAI Models (Technical) &nbsp; &nbsp;We will explore when to use OpenAI&nbsp;models GPT3 Codex Content Filter Parameters in Playground (Technical) Temperature Max Tokens Stop Sequence Top-PInjecting Start &amp; Restart Text Frequency and Presence Penalty Best of Show Probabilities How is ChatGPT Trained?&nbsp; &nbsp;(Technical) What is Generative Pre-trained Transformer 3 (GPT-3)&nbsp; and how is it different from rest of transformers ChatGPT- AI Take Over ?&nbsp; (Non-Technical) Money making ideas with ChatGPT &nbsp; (Non-Technical) fiverr exampleWRITING &amp; TRANSLATION Social Media Marketing Art

En este curso veremos cómo implementar nuestros modelos de inteligencia artificial en una aplicación Android utilizando TensorFlow Lite. El TensorFlow lite es un conjunto de herramientas que nos ayuda a ejecutar modelos de TensorFlow en dispositivos móviles, integrados y de IoT. Esta nos permitirá realizar la inferencia en un dispositivo móvil. Implementaremos desde cero un modelo de “Regresión Lineal” en Python y lo llevaremos a Android utilizando TensorFlow Lite. Implementaremos desde cero un modelo de “Regresión en Múltiple” con normalización de datos y lo llevaremos a Android utilizando TensorFlow Lite. Implementaremos desde cero una “Red Neuronal Convolucional” para clasificar imágenes y llevaremos el modelo a Android utilizando TensorFlow Lite. Implementaremos un ejemplo de detección de objetos basado en la “Red Neuronal Convolucional” Mobile Net. Implementaremos desde cero una “Red Neuronal Artificial” para clasificar dígitos utilizando el dataset MNIST y llevaremos el modelo a Android para reconocer dígitos del 0 al 9 utilizando TensorFlow Lite. Entrenamiento del algoritmo Yolo en Google Colab y despliegue en Aplicación Android. Veremos también como descargar cientos de imágenes para elaborar datasets de manera automática. Implementaremos la técnica de “Data Augmentation” para incrementar la precisión de nuestros modelos de clasificación de imágenes. Además implementaremos OpenCV para segmentar y reconocer digitos escritos a mano.Los invito cordialmente a tomar el curso en donde aprenderán a implementar sus modelos de inteligencia artificial en una aplicación Android.

This course is designed to make you proficient in training and evaluating deep learning based object detection models. Specifically, you will learn about Faster R-CNNs, SSD and YOLO models. For each of these models, you will first learn about how they function from a high level perspective. This will help you build the intuition about how they work. After this, you will learn how to leverage the power of TensorFlow 2 to train and evaluate these models on your local machine. Finally, you will learn how to leverage the power of cloud computing to improve your training process. For this last part, you will learn how to use Google Cloud AI Platform in order to train and evaluate your models on powerful GP Us offered by google. I designed this course to help you become proficient in training and evaluating object detection models. This is done by helping you in several different ways, including :Building the necessary intuition that will help you answer most questions about object detection using deep learning, which is a very common topic in interviews for positions in the fields of computer vision and deep learning.By teaching you how to create your own models using your own custom dataset. This will enable you to create some powerful AI solutions.By teaching you how to leverage the power of Google Cloud AI Platform in order to push your model's performance by having access to powerful GP Us.

Atenção! Nas aulas deste curso é utilizada a versão 1.x do TensorFlow, sendo possível acompanhar as aulas utilizando essa versão. Adicionalmente, disponibilizamos o código atualizado considerando a versão 2.x. Em breve pretendemos regravar todas as aulas deste cursoA área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios&nbsp;computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas&nbsp;técnicas, tais como:&nbsp;carros autônomos, descoberta de novos medicamentos,&nbsp;cura e diagnóstico antecipado de&nbsp;doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina). E a maioria dessas aplicações foram desenvolvidas utilizando a biblioteca TensorFlow do Google, que hoje em dia é a ferramenta mais popular e utilizada nesse cenário. Por isso, é de suma importância que profissionais ligados à área de Inteligência Artificial e Machine Learning saibam como trabalhar com essa biblioteca, já que várias grandes empresas a utilizam em seus sistemas, tais como: Airbnd, Airbus, e Bay, Dropbox, Intel, IBM, Uber, Twitter, Snapchat e também o próprio Google!A área de Deep Learning&nbsp;é atualmente um dos campos de trabalho&nbsp;mais relevantes&nbsp;da Inteligência Artificial, sendo que o&nbsp;mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o

Welcome to my " Complete Python for Data Science &amp; Machine Learning from A-Z " course.Python with Machine Learning &amp; Data Science, Data Visulation, Numpy &amp; Pandas for Data Analysis, Kaggle projects from A-Z Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn't specialized for any specific problems.Python instructors at OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn.Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.Do you want to learn one of the employer’s most requested skills? If you think so, you are at the right place. Python, machine learning, Django, python programming, machine learning python, python Bootcamp, coding, data science, data analysis, programming languages.We've designed for you "Complete Python for Data Science &amp; Machine Learning from A-Z” a straightforward course for the Complete Python programming langu

Selamat datang di program Pelatihan Data Science dengan Deep Learning dan Python Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dengan titik fokus pemanfaatan Deep Learning untuk model machine learning dan data science.Peserta diharapkan sudah menguasai pemrograman Python dasar implementasi machine learning dan data science dengan menggunakan Python. Kami juga menyediakan konten mengenai Pelatihan Data Science dan Machine Learning Dengan Python yang ada di Udemy ini.Seluruh konten didalam pelatihan ini dilaksanan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihan Konsep dan teori mengenai Deep Learning Pengenalan TensorFlow dan Keras Dasar Tensor TensorFlow Pemanfaatan GPU dan TPU pada komputasi TensorFlow dan Keras Pembuat Model dan Layer Untuk TensorFlow Training dan evaluasi Deep Learning pada TensorFlow Pengenalan dan instalasi PyTorch Pemanfaatan GPU dan TPU pada komputasi PyTorch Membangun model Deep Learning dengan PyTorch Training dan evaluasi Deep Learning pada PyTorch Penggunaan Tensor Board untuk visualisasi model pada TensorFlow dan PyTorch Penerapan Hyperparameter Tuning pada TensorFlow dan Keras Penerapan Hyperparameter Tuning pada PyTorch Penggunaan Tensor Board untuk implementasi Hyperparameter Kumpulan Studi Kasus Jika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada we

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

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.</

Greetings,&nbsp;I am so excited&nbsp;to learn that you have started your path to becoming a&nbsp;Data Scientist &nbsp;with my course. Data Scientist is in-demand and&nbsp;most satisfying&nbsp;career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see the&nbsp;impact of your work around your,&nbsp;is not is amazing?This is&nbsp;one of the most&nbsp;comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making.&nbsp;Data Science Bootcamps are costly, in thousands of dollars. However,&nbsp;this course&nbsp;is only a fraction of the&nbsp;cost of any such Bootcamp&nbsp;and includes&nbsp;HD lectures&nbsp;along with &nbsp;detailed&nbsp;code notebooks&nbsp;for every lecture. The course also includes&nbsp;practice&nbsp;exercises on real data&nbsp;for each topic you cover, because the goal is "Learn by Doing"!&nbsp;For your satisfaction, I would like to mention&nbsp;few topics that we will be learning in this course:Basis Python programming for Data Science Data Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and Filter Num PyArrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting &amp; boolean masking, Arithmetic Operations &amp; Universal Functions Pandas Pandas Data Structures - Series, DataF

Hi.. Hello and welcome to my new course, Machine Learning with Python for Dummies. We will discuss about the overview of the course and the contents included in this course.Artificial Intelligence, Machine Learning and Deep Learning Neural Networks are the most used terms now a days in the technology world. Its also the most mis-understood and confused terms too.Artificial Intelligence is a broad spectrum of science which tries to make machines intelligent like humans. Machine Learning and Neural Networks are two subsets that comes under this vast machine learning platform Lets check what's machine learning now. Just like we human babies, we were actually in our learning phase then. We learned how to crawl, stand, walk, then speak words, then make simple sentences.. We learned from our experiences. We had many trials and errors before we learned how to walk and talk. The best trials for walking and talking which gave positive results were kept in our memory and made use later. This process is highly compared to a Machine Learning Mechanism Then we grew young and started thinking logically about many things, had emotional feelings, etc. We kept on thinking and found solutions to problems in our daily life. That's what the Deep Learning Neural Network Scientists are trying to achieve. A thinking machine.But in this course we are focusing mainly in Machine Learning. Throughout this course, we are preparing our machine to make it ready for a prediction test. Its Just like how you prepare for your Mathematics Test in school or college. We learn and train ourselves by solving the most possible number of similar mathematical problems. Lets call these sample data of similar problems and their solutions as the 'Training Input' and 'Training Output' Respectively. And then the day comes when we have the actual test. We will be given new set of problems to solve, but very similar to the problems we learned, and based on th

Programmer en Python pour la Data Science, le Machine Learning, la Data Viz et l'Intelligence Artificielle Ce cours a pour objectif de vous initier à la programmation en Python en lien avec les concepts essentiels du Big Data (Data Science, Machine Learning, IA, etc.). Il ne requiert aucun prérequis et vous permet d'atteindre un niveau solide en seulement 4 heures de formation.Acquérir des bases solides Plus besoin de partir à la chasse aux informations sur Google, l'essentiel de votre apprentissage est concentré dans ce cours.Gagner du temps Ce cours est conçu pour vous familiariser avec la Data Science et Python de manière rapide et efficace. Vous pourrez ainsi atteindre un niveau solide en seulement 4 heures de cours.Une formation qui va à votre rythme Les concepts sont présentés progressivement, à travers des exemples concrets issus de projets d'entreprises et d'universités, vous permettant d'appliquer ce que vous avez appris.Cours récent et régulièrement mis à jour Mis à jour récemment, ce cours est en adéquation avec les compétences actuellement recherchées par les entreprises.Éviter les pièges de débutants Ce cours détaille les bonnes pratiques d'un Data Scientist expérimenté pour rédiger un code de qualité professionnelle.Préparation réussie pour vos examens, certifications et tests techniques sur Python Les exercices inclus dans ce cours constituent un excellent moyen de préparation pour vos examens, certifications et tests techniques en entreprise.Travailler pour les plus grandes entreprises Des entreprises prestigieuses telles qu'Intel, Google, Netflix, Spotify, Meta, mais aussi Renault, la SNCF, Orange, Total, Capgemini, sont actuellement à la recherche de Data Scientists expérimentés maîtrisant Python.Se former à des métiers actuellement recherchés</stron

Jetzt neu: Zusätzlicher Bonus zum Thema Deep Learning (Neuronale Netze) mit Python, TensorFlow und Keras!Dieser Kurs enthält über 300 Lektionen, Quizze, Praxisbeispiele, ... - der einfachste Weg, wenn du Machine Learning lernen möchtest. Schritt für Schritt bringe ich dir maschinelles Lernen bei. In jedem Abschnitt lernst du ein neues Thema - zuerst die Idee / Intuition dahinter, und anschließend den Code sowohl in Python als auch in R.Machine Learning macht erst dann richtig Spaß, wenn man echte Daten auswertet. Deswegen analysierst du in diesem Kurs besonders viele Praxisbeispiele:Schätze den Wert von Gebrauchtwagen Schreibe einen Spam-Filter Diagnostiziere Brustkrebs Schreibe ein Programm, was die Bedeutung von Adjektiven lernt Lese Zahlen aus Bildern ein Alle Codebeispiele werden dir beiden Programmiersprachen gezeigt - du kannst also wählen, ob du den Kurs in Python, R, oder in beiden Sprachen sehen möchtest!Nach dem Kurs kannst du Machine Learning auch auf eigene Daten anwenden und eigenständig fundierte Entscheidungen treffen:Du weißt, wann welche Modelle in Frage kommen könnten und wie du diese vergleichst. Du kannst analysieren, welche Spalten benötigt werden, ob zusätzliche Daten benötigt werden, und weißt, die die Daten vorab aufbereitet werden müssen. Dieser Kurs behandelt alle wichtigen Themen:Regression Klassifizierung Clustering Natural Language Processing Bonus: Deep Learning (nur für Python, weil die Tools hier sehr viel ausgereifter sind)Zu allen diesen Themen lernst du verschiedene Algorithmen kennen. Die Ideen dahinter werden einfach erklärt - keine trockenen, mathematischen Formeln, sondern anschauliche, grafische Erklärungen.Wir verwenden hierbei g

Would you like to learn how to detect if someone is wearing a Face Mask or not using Artificial Intelligence that can be deployed in bus stations, airports, or other public places?Would you like to build a Convolutional Neural Network model using Deep learning to detect Covid-19 Face Mask?If the answer to any of the above questions is "YES", then this course is for you.Enroll Now in this course and learn how to detect Face Mask on the static images as well as in the video streams using TensorFlow and OpenCV. As we know, COVID-19 has affected the whole world very badly. It has a huge impact on our everyday life, and this crisis is increasing day by day. In the near future, it seems difficult to eradicate this virus completely.To counter this virus, Face Masks have become an integral part of our lives. These Masks are capable of stopping the spread of this deadly virus, which will help to control the spread. As we have started moving forward in this ‘new normal’ world, the necessity of the face mask has increased. So here, we are going to build a model that will be able to classify whether the person is wearing a mask or not. This model can be used in crowded areas like Malls, Bus stands, and other public places.This is a hands-on Data Science guided project on Covid-19 Face Mask Detection using Deep Learning and Computer Vision concepts. We will build a Convolutional Neural Network classifier to classify people based on whether they are wearing masks or not and we will make use of OpenCV to detect human faces on the video streams. No unnecessary lectures. As our students like to say :"Short, sweet, to the point course"The same techniques can be used in :Skin cancer detection Normal pneumonia detection Brain

Yapay zeka alanına giriş yapmak ve "öğrenen" uygulamalar geliştirmek istiyorsanız derin öğrenme yöntemlerini öğrenmek için sizi temelden ileri seviyeye kadar teorik anlatım ve pratik uygulamaları içeren bu kapsamlı "Derin Öğrenmeye Giriş" eğitimime davet ediyorum.Eğitimi bitirdiğinizde, derin öğrenmenin temellerini, yapay sinir ağı modelleri oluşturma ve geliştirme adımlarını ve başarılı yapay öğrenme projelerini nasıl gerçekleştirebileceğinizi öğreneceksiniz. Uygulayacağımız yöntemler:Temel yapay sinir ağları, Evrişimli sinir ağları (CNNs), Özyinelemeli sinir ağları (RNNs), Uzun-kısa vadeli bellek modeli (LSTMs), Makine öğrenmesinde optimizasyon ve regülarizasyon yöntemlerini, Kapsül ağları, Pekiştirmeli öğrenme (RL), Çekişmeli üretici ağları (GANs) Tüm bu yöntemleri Python programlama dili kullanarak TensorFlow ve gerisinde çalışan Keras kütüphanelerinde uygulayacaksınız. Yapay zeka ve derin öğrenme çoklu endüstrileri geliştirmekte ve dönüştürmektedir. Bu dersi tamamladıktan sonra, bunu işinize uygulamak için yaratıcı yollar bulabilirsiniz.

Este curso básico de TensorFlow te enseñará a crear redes neuronales para Deep Learning o aprendizaje profundo.Es una guía fácil con muchos ejemplos, para entener las complejidades del marco de TensorFlow de Google.Este curso está repleto de ejemplos escritos en Python sobre Jupyter Notebook, para que puedas probarlos tu mismo.Estos son los temas tratados en este curso de TensorFlow :- Introduccion al Machine Learning- Instalacion del entorno de trabajo- Curso básico de Python sobre las librerías usadas en este curso: - Num Py - Pandas - Matplotlib - Scikit-Learn- Introducción a las redes neuronales (Deep Learning) - Neuronas y perceptrones - Funciones de activacion - Funciones de coste - Algoritmo del gradiente descendiente - Practicar con una red neuronal en el navegador- TensorFlow - Introducción a TensorFlow - Sintaxis básica de TensorFlow - Grafos en TensorFlow - Grafos por defecto - Variables y placeholders - Ejemplo de red neuronal - parte 1 - Ejemplo de red neuronal - parte 2 - Ejemplo de regresión simple con TensorFlow - Ejemplo de clasificación con TensorFlow - Ejemplo de regresión con TensorFlow - parte 1 - Ejemplo de regresión con TensorFlow - parte 2 - Ejemplo de regresión con TensorFlow - parte 3- Redes Neuronales Convolucionales - Introducción a las redes neuronales convolucionales - MNIST - Base de datos de imágenes de dígitos escritos a mano - Ejemplo con MNIST - Importar base de datos y mostrar una imagen- Redes Neuronales Recurrentes - Introducción a las redes neuronales recurrentes - Ejemplo de una red neuronal recurrente con TensorFlow - Ejemplo de series temporales - parte 1 - Ejemplo de series temporales - parte 2 - Ejemplo de series temporales - parte 3- Bibiliotecas - Estimator API - Ke

Data Scientist wurde von Glassdoor als Nummer 1 Job gerankt und erzielt laut Indeed einen überdurchschnittlichen Gehalt. Die Karriere im Bereich Data Science ist eine bereichernde Tätigkeit und erlaubt es euch an den größten und interessantesten Herausforderungen der Welt zu arbeiten.&nbsp;Dieser Kurs richtet sich sowohl an Anfänger, die zum ersten Mal mit der Programmiersprache R in Berührung kommen, als auch für erfahrene Entwickler, die ihr Portfolio um Fähigkeiten in Richtung R, Data Sciene und Machine Learning ausbauen wollen!&nbsp; &nbsp;"Perfekter Einstieg in die Sprache R. Zuvor hatte ich keine Kenntnis dieser Sprache. Gut gefällt mir, dass direkt auch Data Science Anwendungen inbegriffen sind, da ich diese beruflich brauche. Top! (★★★★★ D. Mika)Dieser umfangreiche Kurs ist vergleichbar mit anderen Data Science Bootcamps die mehrere tausend Euro kosten. Das alles findest du in über 120 HD Video Lektionen und detaillierten Code Notebooks zu jeder Lektion. Dies macht diesen Kurs zum umfangreichsten Data Science Kurs mit R auf Udemy!Wir werden gemeinsam lernen, wie man mit R programmiert, grandiose Visualisierungen erstellt und mit echten Daten und echte Data Science Fälle umgeht. Dazu verwenden wir R-Studio und das Jupyter Notebook mit R. Hier ist eine Übersicht einiger Themen:Programmieren mit RFortgeschrittene Programmierung in RR Date Frames zur Lösung komplexer Aufgaben verwenden Mit R Excel Datein bearbeiten Web Scraping mit RR mit SQL verbindenGG Plot2 zur Visualisierung verwendenÜbersicht und Einsatz&nbsp;von DplyR&nbsp;und TidyRPlotly für interaktive Visualisierungen verwenden Analysiere echte Daten an&

Dans ce cours, vous allez découvrir et approfondir les différents aspects liés à l'apprentissage automatique avec Python. Nous utiliserons les librairies telles que TensorFlow, Keras, Pandas, Numpy, Scikit-Learn, ...Les travaux sont accessibles et exploitables en ligne grâce à l'utilisation des carnets Jupyter avec Google Colab. Aucune installation de logiciel spécifique sur son ordinateur n'est requise car tout le travail se fait en ligne.A chaque étape d'apprentissage de ce cours, de nouveaux modèles sont introduits. Des explications claires permettent de bien les comprendre à travers 6 thèmes d'étude :Structure de base d'un réseau de neurones Reconnaissance d'image avec un réseau de neurones à convolution 2DTraitement d'image avec un réseau de neurones profond à convolution 2DSystèmes de recommandations et d'analyse des ressentisDétection d'anomalies dans les données Analyse et prédiction sur les séries temporelles Les activités en Python expliquent clairement comment les exploiter. Des exercices sont régulièrement proposés pour consolider votre apprentissage.D'une durée totale de 19,5 heures, ce cours vous permettra d'être à l'aise avec les outils actuels du Deep Learning. Vous serez alors capable d'utiliser ces ressources pour créer vos propres projets et d'approfondir avec sérénité et en autonomie vos connaissances dans ce domaine.=== Prérequis ===Vous n'avez pas besoin d'être un spécialiste du langage Python. En effet, au fur et à mesure de votre progression, vous manierez ce langage et découvrirez les subtilités liées à son utilisation.Si vous êtes complètement débutant en Deep Learning, alors ce cours est fait pour vous. Ce cours est structuré de manière progressive pour acquérir petit à petit les bases du de

Common scenario: You try to get into machine learning and data science, but there's SO&nbsp;MUCH&nbsp;MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Calculus is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science. Backpropagation, the learning algorithm behind deep learning and neural networks, is really just calculus with a fancy name.If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know calculus.Normally, calculus is split into 3 courses, which takes about 1.5 years to complete.Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of years.This course will cover Calculus 1 (limits, derivatives, and the most important derivative rules), Calculus 2 (integration), and Calculus 3 (vector calculus). It will even include machine learning-focused material you wouldn't normally see in a regular college course. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of calculus, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can start applying them today.Are you ready?Let's go!Suggested prerequisites:Firm understanding of high school math (functions, algebra, trigonometry)

A-Z™ | TensorFlow ile Derin Öğrenme Kursumuzda klasik ve derin öğrenme tabanlı yöntemlerini kullanarak sınıflandırma nasıl yapıldığını öğrenip, TensorFlow kütüphaneleriyle gerçek hayat projeleri yapacağız.Projelerle Yapay Zeka ve Bilgisayarlı Görü Kursu İçeriği Giriş BölümüDerin Öğrenme Teori Derin Öğrenme Nedir Yapay Sinir AğlarıAktivasyon FonksiyonlarıOptimizasyon AlgoritmalarıLoss (Kayıp) FonksiyonlarıDerin Öğrenme TeoriCNN (Convolutional Neural Networks) Teori Evrişim İşlemiCNN (Convolutional Neural Networks)Piksel Ekleme (Padding)Adım Kaydırma (Stride)Ortaklama (Pooling)Ek Teori Epoch ve Batch Size Dropout Early Stopping Learning Rate TensorFlow ile Derin Öğrenme TensorFlow Temelleri Veriyi Hazırlama Model Oluşumu Sequential Model Egitimi Model Testi | 1. Kısım Model Testi | 2. Kısım Modeli Kaydetme/Yükleme - Save/Load Model Sonuçlarını Görselleştirme Modelin Ara Katmalarını Görselleştirme Functional Bir Model Oluşturma Callbacks | 1. kısım Callbacks | 2. kısım Data Augmentation - Veri Arttırma | 1. Kısım Data Augmentation - Veri Arttırma | 2. Kısım Transfer Learning - VGG Hazır Model Kullanma - VGG TensorFlow ile Trafik İşaretlerini Sınıflandırma Veriyi Hazırlama Model Eğitimi ve Test Real Time'da Test TensorFlow'da Weights &amp; Biases (WandB) | Özel Veri Wandb ile Keras'da Temel Fonsiyonlar Wandb ile Keras'da Sweepler Wandb ile Keras'da Sweep - Bonus Video TensorFlow Lite - Android App - Object detection - İmage Classification Efficient Det Lite Model Eğitimi - Object detection Efficient Det Lite Modeli Android'de Çalıştırma 1 - Object detection Efficient Det Lite Modeli Androi

A área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios&nbsp;computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas&nbsp;técnicas, tais como:&nbsp;carros autônomos, descoberta de novos medicamentos,&nbsp;cura e diagnóstico antecipado de&nbsp;doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina). E a maioria dessas aplicações foram desenvolvidas utilizando a biblioteca TensorFlow do Google, que hoje em dia é a ferramenta mais popular e utilizada nesse cenário. Por isso, é de suma importância que profissionais ligados à área de Inteligência Artificial e Machine Learning saibam como trabalhar com essa biblioteca, já que várias grandes empresas a utilizam em seus sistemas, tais como: Airbnd, Airbus, e Bay, Dropbox, Intel, IBM, Uber, Twitter, Snapchat e também o próprio Google!A área de Deep Learning&nbsp;é atualmente um dos campos de trabalho&nbsp;mais relevantes&nbsp;da Inteligência Artificial, sendo que o&nbsp;mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação!E para levar você até essa área, neste curso você terá uma visão teórica e principalmente prática sobre as principais e mais modernas técnicas de Deep Learning utilizando o Tensor

"Python TOTAL", el curso Best-Seller que ha enseñado Python desde cero a miles y miles, necesitaba un complemento perfecto:&nbsp;"Python TOTAL&nbsp;para Data Science y Machine Learning".¿Por qué hacía falta?Porque con este curso, además de aprender Python desde cero, podrás llevarlo hacia la ciencia del momento:&nbsp;Data Science (o Ciencias de la Información), para poder programar herramientas capaces de procesar cantidades monumentales de información, y de generar no solo visualizaciones relevantes, informativas y atractivas, sino también predicciones a partir de los datos que disponemos.Con "Python Total para Data science &amp; Machine Learning" podrás ayudar a quienes toman decisiones a entender mejor el contexto y la realidad sobre la cual están operando, para poder ser eficaces, eficientes y acertivos en sus decisiones.¿Que encontrarás en este curso?18 días de aprendizaje intenso y práctico Cientos de ejercicios de código en la plataforma (3 por cada lección)Vientos de archivos de código descargable Proyectos díarios del mundo real para aplicar lo aprendido Decenas de bases de datos para prácticas Cuestionarios Lecciones teóricas y prácticas hechas con amor por la simplicidad¿Qué temas cubre este curso?Python básico Pandas Num PyMatplotlib Seaborn Scikit-Learn TensorFlow Machine Learning Excel y Power BI para Data Science Algoritmos de Aprendizaje Supervisado, No Supervisado y por Reforzamiento Bases de DatosAP Is Deep Learning Etica y Provacidad en Data Sciencey muchísimo más<

Dieser Kurs ist dein umfassender Einstieg in die Welt des Deep Learnings – mit einem klaren Fokus auf Praxis, fundierter Theorie und moderner Python-Entwicklung mit TensorFlow 2 und Keras.Statt nur Code-Schnipsel zu kopieren, lernst du wirklich zu verstehen, wie neuronale Netze funktionieren – von der mathematischen Basis bis zur Anwendung. Du wirst eigene Modelle Schritt für Schritt selbst aufbauen und trainieren, Bilddaten analysieren und sogar Texte mit KI verarbeiten.Du startest mit den Grundlagen des Machine Learning und neuronaler Netzwerke – und steigst dann tief in die wichtigsten Netzarchitekturen ein: Von klassischen Fully Connected Networks über CNNs für Bildverarbeitung bis zu RNNs/LSTMs für Zeitreihen und Texte. Dabei kommen State-of-the-art Modelle wie Res Net und Dense Net ebenfalls nicht zu kurz.Auch Natural Language Processing (NLP) ist Teil des Kurses – perfekt, um moderne KI-Anwendungen wie Chatbots oder Textklassifizierer zu entwickeln.Kursinhalte im Überblick:Einführung in Machine Learning und neuronale Netze Mathematische Grundlagen (z. B. Aktivierungsfunktionen, Backpropagation)Eigene Modelle in TensorFlow 2 und Keras entwickeln Visualisierung und Debugging mit Tensor Board Digitale Bildverarbeitung mit CNNs Moderne Architekturen: Res Net, Dense Net Sequenzmodelle: RNNs und LSTMs für zeitabhängige Daten Einstieg in Natural Language Processing (NLP) mit Keras Praxisnahe Projekte und Übungen Ziel:Werde fit im Umgang mit modernen KI-Technologien und baue deine eigenen Deep-Learning-Modelle – fundiert, praxisnah, professionell.<p

TensorFlow Developer Professional Certificate