RNNs & Sequence Models Learning Path

This course explores advanced Convolutional Neural Networks (CNNs), Transfer Learning, and Recurrent Neural Networks (RNNs). It delves into sophisticated architectures like VGG16 and their practical applications.

The fifth course in the Deep Learning Specialization, this course focuses on sequence models for applications like speech recognition, music synthesis, and natural language processing. You will learn to build and train Recurrent Neural Networks (RNNs) and their variants like GRUs and LSTMs.

A project-based course where you will build and train a bidirectional LSTMs neural network model to recognize named entities in text data using Keras with a TensorFlow backend. This is a key tool for information extraction and a preprocessing step for other NLP applications.

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!This course is designed for ML practitioners who want to enhance their skills and move up the ladder with Deep Learning!This course is made to give you all the required knowledge at the beginning of your journey so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips, and tricks you would require to work in the Deep Learning space.It gives a detailed guide on TensorFlow and Keras along with in-depth knowledge of Deep Learning algorithms. All the algorithms are covered in detail so that the learner gains a good understanding of the concepts. One needs to have a clear understanding of what goes behind the scenes to convert a good model to a great model. This course will enable you to develop complex deep-learning architectures with ease and improve your model performance with several tips and tricks.Deep Learning Algorithms Covered:1. Feed Forward Networks (FFN)2. Convolutional Neural Networks (CNNs)3. Recurring Neural Networks (RNNs)4. Long Short-Term Memory Networks (LSTMs)5. Gated Recurrent Unit (GRUs)6. Autoencoders7. Transfer Learning8. Generative Adversarial Networks (GANs)Our exotic journey will include the concepts of:1. The most important concepts of TensorFlow and Keras from very basic.2. The two ways of model building i.e. Sequential and Functional API.3. All the building blocks of Deep Learning models are explained in detail to enable students to make decisions while training their model and improving model performance.4. Hands-on learning of Deep Learning algorithms from the beginner

Course Contents Deep Learning and revolutionized Artificial Intelligence and data science.  Deep Learning teaches computers to process data in a way that is inspired by the human brain.This is complete and comprehensive course on deep learning. This course covers the theory and intuition behind deep learning models and then implementing all the deep learning models both in PyTorch and TensorFlow.Practical Oriented explanations Deep Learning Models with implementation both in PyTorch and TensorFlow.No need of any prerequisites. I will teach you everything from scratch.Job Oriented Structure Sections of the Course· Introduction of the Course· Introduction to Google Colab· Python Crash Course· Data Preprocessing· Regression Analysis· Logistic Regression· Introduction to Neural Networks and Deep Learning· Activation Functions· Loss Functions· Back Propagation· Neural Networks for Regression Analysis· Neural Networks for Classification· Dropout Regularization and Batch Normalization· Optimizers· Adding Custom Loss Function and Custom Layers to Neural Networks· Convolutional Neural Network (CNNs)· One Dimensional CNNs· Setting Early Stopping Criterion in CNNs· Recurrent Neural Network (RNNs)· Long Short-Term Memory (LSTMs) Network· Bidirectional LSTMs· Generative Adversarial Network (GANs)· DCGA Ns· Autoencoders· LSTMs Autoencoders· Variational Autoencoders· Neural Style Transfer· Transformers· Vision Transformer· Time Series Transformers. K-means Clustering. Principle Component Analysis.  Deep Learning Models with implementation both in PyTorch and TensorFlow.

As a practitioner of Deep Learning, I am trying to bring many relevant topics under one umbrella in the following topics. Deep Learning has been most talked about for the last few years and the knowledge has been spread across multiple places.1. The content (80% hands-on and 20% theory) will prepare you to work independently on Deep Learning projects2. Foundation of Deep Learning TensorFlow 2.x3. Use TensorFlow 2.x for Regression (2 models)4. Use TensorFlow 2.x for Classifications (2 models)5. Use Convolutional Neural Net (CNNs) for Image Classifications (5 models)6. CNNs with Image Data Generator7. Use Recurrent Neural Networks (RNNs) for Sequence data (3 models)8. Transfer learning9. Generative Adversarial Networks (GANs)10. Hyperparameters Tuning11. How to avoid Overfitting12. Best practices for Deep Learning and Award-winning Architectures

Interested in the field of Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!This course has been designed by a software engineer. I hope with the experience and knowledge I did gain throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.I will walk you step-by-step into Deep Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course is fun and exciting, but at the same time, we dive deep into Recurrent Neural Network. Throughout the brand new version of the course, we cover tons of tools and technologies including:Deep Learning.Google Colab Keras.Matplotlib.Splitting Data into Training Set and Test Set. Training Neural Network.Model building.Analyzing Results.Model compilation.Make a Prediction.Testing Accuracy.Confusion Matrix.ROC Curve.Text analysis.Image analysis.Embedding layers.Word embedding.Long short-term memory (LSTMs) models.Sequence-to-vector models.Vector-to-sequence models.Bi-directional LSTMs.Sequence-to-sequence models.Transforming words into feature vectors.frequency-inverse document frequency.Cleaning text data.Processing documents into tokens.Topic modelling with latent Dirichlet allocation Decomposing text documents with LDA.Autoencoder.Numpy.Pandas.TensorFlow.Sentiment Analysis.Matplotlib.out-of-core learnin

„Künstliche Intelligenz in der Praxis: ChatGPT u. Prompt Engineering“In diesem Kurs erhalten die Teilnehmer eine fundierte Einführung in die Theorie und Praxis der Künstlichen Intelligenz (KI) mit besonderem Fokus auf das Sprachmodell ChatGPT von OpenAI. Dieser Kurs vermittelt detailliertes Wissen über die grundlegenden Konzepte des maschinellen Lernens und der Sprachverarbeitung und beleuchtet, wie KI-Modelle wie ChatGPT zur Optimierung von Geschäftsprozessen genutzt werden können. Dabei werden sowohl die technischen Mechanismen hinter ChatGPT – wie die innovative Transformer-Architektur – als auch die praktischen Anwendungen und Optimierungspotenziale von KI im betrieblichen Alltag anschaulich erläutert.Kursinhalte:Einführung in die Künstliche Intelligenz und maschinelles Lernen Grundlagen der Sprachverarbeitung und Funktionsweise der Transformer-Architektur Praktische Anwendungsbereiche von ChatGPT: Kundenservice, interne Kommunikation und Wissensmanagement Datenanalyse und Mustererkennung zur Identifikation von Optimierungspotenzialen Implementierung von ChatGPT zur Effizienzsteigerung: Automatisierung von Routineaufgaben und Verbesserung der Reaktionszeiten Ethische und rechtliche Aspekte der KI-Nutzung, insbesondere Datenschutz und DSGVO-Konformität Lernziele: Am Ende dieses Kurses verstehen die Teilnehmer die Funktionsweise und Potenziale von ChatGPT und ähnlichen KI-Systemen und können deren Einsatz für die Prozessoptimierung in verschiedenen Bereichen bewerten. Sie erlernen, wie sie KI-Technologien in der Praxis sicher und effektiv einsetzen, Prozesse analysieren und potenzielle Effizienzsteigerungen identifizieren können. Zusätzlich erhalten sie das nötige Bewusstsein für die ethischen und rechtlichen Anforderungen, die den Einsatz von KI-Systemen begleiten.Zielgruppe: Dieser Kurs richtet sich an Fach- und Fü

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

 NOW IN TensorFlow 2 and PYTHON 3 Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Learn about one of the most powerful Deep Learning architectures yet!The Recurrent Neural Network (RNNs) has been used to obtain state-of-the-art results in sequence modeling.This includes time series analysis, forecasting and natural language processing (NLP).Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models.This course will teach you:The basics of machine learning and neurons (just a review to get you warmed up!)Neural networks for classification and regression (just a review to get you warmed up!)How to model sequence data How to model time series data How to model text data for NLP (including preprocessing steps for text)How to build an RNNs using TensorFlow 2How to use a GRUs and LSTMs in TensorFlow 2How to do time series forecasting with TensorFlow 2How to predict stock prices and stock returns with LSTMs in TensorFlow 2 (hint: it's not what you think!)How to use Embeddings in TensorFlow 2 for NLP How to build a Text Classification RNNs for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflo