Build a successful career with these top 10 transfer learning and deep learning courses in 2022.
The reuse of a previously learned model on a new problem is known as transfer learning. It’s particularly popular in deep learning right now since it can train deep neural networks with a small amount of data. This is particularly valuable in the field of data science, as most real-world situations do not require millions of labeled data points to train complicated models. Here are the top 10 transfer learning and deep learning courses to take up in 2022.
Transfer Learning for Computer Vision Tutorial at PyTorch.org
In this tutorial, you will learn how to train a convolutional neural network for image classification using transfer learning. Here you will learn the two most important transfer learning scenarios and they are :
Fine Tuning the convnet: Instead of random initialization, we initialize the network with a pretrained network, like the one that is trained on imagenet 1000 dataset. The rest of the training looks as usual.
ConvNet as a fixed feature extractor: Here, we will freeze the weights for all of the networks except that of the final fully connected layer. This last fully connected layer is replaced with a new one with random weights and only this layer is trained.
Transfer Learning Introduction at HackerEarth
Transfer learning involves the approach in which knowledge learned in one or more source tasks is transferred and used to improve the learning of a related target task. While most machine learning algorithms are designed to address single tasks, the development of algorithms that facilitate transfer learning is a topic of ongoing interest in the machine-learning community.
Hands-On Transfer Learning with TensorFlow 2.0 at Udemy
Transfer learning involves using a pre-trained model on a new problem. It is currently very popular in the field of Deep Learning because it enables you to train Deep Neural Networks with comparatively little data. In Transfer learning, knowledge of an already trained Machine Learning model is applied to a different but related problem. The general idea is to use knowledge, which a model has learned from a task where a lot of labeled training data is available, in a new task where we don’t have a lot of data. Instead of starting the learning process from scratch, you start from patterns that have been learned by solving a related task.
Introduction to Transfer Learning at mygreatlearning.com
Transfer learning is a very popular form of learning today. It aims to use a pre-trained model to work on an entirely different dataset to see if meaningful results can be extracted when the machine learning algorithm is exposed to new data. Transfer learning is extremely popular because of the efficiency that is portrayed by the domain when working on really large datasets. In this course, you will learn all of the foundational concepts that are necessary for you to get started with and understand the working of transfer learning.
TensorFlow Machine Learning Transfer Learning at Alison.com
This free online course in TensorFlow Machine Learning transfer learning will introduce you to a new neural network architecture known as Convolutional Neural Network (CNNs). You will also learn about image classification and visualization as well as transfer Learning with pre-trained Convolutional Neural Network and TensorFlow hub. You will also be introduced to the method of using Estimator API to create machine learning models.
Transfer Learning for Images Using PyTorch: Essential Training at Linkedin
In this course, Jonathan Fernandes shows you how to leverage this popular machine learning framework for a similarly buzzworthy technique: transfer learning. Using a hands-on approach, Jonathan explains the basics of transfer learning, which enables you to leverage the pretrained parameters of an existing deep-learning model for other tasks. He then shows how to implement transfer learning for images using PyTorch, including how to create a fixed feature extractor and freeze neural network layers. Plus, find out about using learning rates and differential learning rates.
Deep Learning Specialization at Coursera
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
Deep Learning at Udacity
Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website.
Deep Learning: Model Optimization and Tuning
Deep Learning as technology has grown leaps and bounds in the last few years. More and more AI solutions use Deep Learning as their foundational technology. Studying this technology, however, presents several challenges. IT professionals from varying backgrounds need a simplified resource to learn the concepts and build models quickly. In this course, instructor Kumaran Ponnambalam provides a simplified path to understand various optimization and tuning options available for deep learning models and shows you how to use these options to improve models.
Python Essential Training
PyTorch is quickly becoming one of the most popular deep learning frameworks around, as well as a must-have skill in your artificial intelligence tool kit. It’s gained admiration from industry leaders due to its deep integration with Python; its integration with top cloud platforms, including Amazon SageMaker and Google Cloud Platform; and its computational graphs that can be defined on the fly. In this course, join Jonathan Fernandes as he dives into the basics of deep learning using PyTorch. Starting with a working image recognition model, he shows how the different components fit and work in tandem—from tensors, loss functions, and autograde all the way to troubleshooting a PyTorch network.
Source: analyticsinsight.net
