deep learning

Rating: 4.8/5 with 1.219 ratings Provider: DeepLearning.AI Teacher: Laurence Moroney, Eddy Shyu Price: $49/month with a 7-day free trial Duration: Approx. 2 months if you study 10 hours per week (80 hours total) Pre-requisites: basic calculus, linear algebra, stats, knowledge of deep learning, experience with Python and a deep learning framework (e.g., TensorFlow, Keras, PyTorch) Level: Intermediate Certificate: Yes Coursera Plus: No Deepening your understanding of TensorFlow and its advanced techniques can feel like a daunting task....

Rating: 4.7/5 with 1.251 ratings Provider: DeepLearning.AI Teacher: Laurence Moroney Price: $49/month with a 7-day free trial Duration: Approx. 1 months if you study 10 hours per week (40 hours total) Pre-requisites: basic Javascript, Kotlin or Swift, and TensorFlow knowledge Level: Intermediate Certificate: Yes Coursera Plus: No If you’re a data science student with an interest in TensorFlow and machine learning, this article will guide you through a selection of courses offered in the TensorFlow: Data and Deployment Specialization, offered by DeepLearning....

Rating: 4.7/5 with 20.466 ratings Provider: DeepLearning.AI Teacher: Laurence Moroney Price: $49/month Duration: Approx. 2 months if you study 10 hours per week (80 hours total) Pre-requisites: intermediate Python skills, basic Linear Algebra and machine learning knowledge Level: Intermediate Certificate: Yes Coursera Plus: No Are you worried about how to prepare effectively for the TensorFlow Developer Certification exam? You’re not alone. Many aspiring TensorFlow developers are in the same boat, grappling with the challenge of finding comprehensive, reliable study materials to help them confidently take the exam....

Rating: 4.7/5 with 1.974 ratings Provider: DeepLearning.AI Teacher: Sharon Zhou, Eda Zhou, Eric Zelikman Price: $49/month with a 7-day free trial Duration: Approx. 2 months if you study 10 hours per week (80 hours total) Pre-requisites: basic calculus, linear algebra, and stats, basic knowledge of deep learning and CNNs, intermediate Python skills, experience with Tensorflow, Keras or PyTorch Certificate: Yes Coursera Plus: No Deep Learning is an exciting field that is rapidly advancing and gaining popularity....

Rating: 4.8/5 with 540 ratings Provider: Imperial College London Teacher: Dr Kevin Webster Price: $49/month with a 7-day free trial Duration: Approx. 2 months if you study 10 hours per week (80 hours total) Pre-requisites: Python 3, basic machine learning and deep learning knowledge, probability and statistics Level: Intermediate Certificate: Yes Coursera Plus: Yes Are you finding it tough to get a grip on TensorFlow 2 for deep learning?...

Deep learning is a powerful subset of artificial intelligence that uses artificial neural networks to learn from data, enabling computers to perform tasks that were previously thought to require human intelligence, such as image recognition, natural language processing, and even playing games. By mastering deep learning, you can gain valuable skills that are highly sought after in various industries, including tech, finance, and healthcare. Finding the right deep learning course on Coursera can be challenging, especially with so many options available....

Provider: DeepLearning.AI, Stanford University Teacher: Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh Price: $49/month with a 7-day free trial Duration: Approx. 5 months if you study 7 hours per week Pre-requisites: intermediate Python skills, basic Linear Algebra and machine learning knowledge Level: Intermediate Certificate: Yes Are you interested in deepening your knowledge of machine learning and neural networks, but unsure where to start? Maybe you’ve tried to learn on your own with the thousands of data science resources out there, but found the material too complex or poorly structured....

Imagine having a robust forecasting solution capable of handling multiple time series data without relying on complex feature engineering. That’s where N-BEATS comes in! In this tutorial, I’ll break down its inner workings, walk you through the process of installing and configuring NeuralForecast to train an N-BEATS model in Python, and show you how to effectively prepare and split your time series data. Furthermore, we’ll explore hyperparameter tuning with Optuna....

So, you’ve already explored the world of LSTMs and now you’re curious about their sibling GRUs (Gated Recurrent Units) and how they can enhance your time series forecasting projects… Great! As machine learning practitioners, we’re always looking for ways to expand our knowledge and improve our model choices. In this tutorial, we’ll take a deep dive into GRUs, covering their inner workings, and comparing them to LSTMs. By the end of this tutorial, you’ll have a solid understanding of GRUs and be well-equipped to use them effectively in Python....

In short, feature scaling or normalization is not strictly required for neural networks, but it is highly recommended. Scaling or normalizing the input features can be the difference between a neural network that converges in a few iterations and one that takes hundreds of iterations to converge or even fails to converge at all. The optimization process may become slower because the gradients in the direction of the larger-scale features will be significantly larger than the gradients in the direction of the smaller-scale features....

In this post, we will learn how to use DeepAR to forecast multiple time series using GluonTS in Python. DeepAR is a deep learning algorithm based on recurrent neural networks designed specifically for time series forecasting. It works by learning a model based on all the time series data, instead of creating a separate model for each one. In my experience, this often works better than creating a separate model for each time series....

In this article you will learn an easy, fast, step-by-step way to use Convolutional Neural Networks for multiple time series forecasting in Python. We will use the NeuralForecast library which implements the Temporal Convolutional Network (TCN) architecture. Temporal Convolutional Network (TCN) This architecture is a variant of the Convolutional Neural Network (CNN) architecture that is specially designed for time series forecasting. It was first presented as WaveNet. Source: WaveNet: A Generative Model for Raw Audio...

You’ve probably heard about LSTMs, and might be curious about how they can help you with multiple time series forecasting. As machine learning practitioners, we come across various forecasting tasks, and choosing the right model can sometimes be a challenge. LSTMs have gained attention for their ability to handle long-term dependencies in sequential data, making them a promising choice for time series problems. By the end of this tutorial, you’ll have a deeper understanding of LSTMs and be better prepared to use them effectively for multiple time series forecasting projects....