by Satavisa Pati
March 13, 2022
Build a successful career with these 10 best 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 is especially popular in deep learning right now because it can train deep neural networks with a small amount of data. This is especially valuable in the field of data science, because most real-world situations don’t require millions of labeled data points to train complex models. Here are the 10 best transfer learning and deep learning courses to take in 2022.
Transfer Learning Tutorial for Computer Vision 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 pre-trained network, like the one trained on the imagenet 1000 dataset. The rest of the training happens as usual .
ConvNet as Fixed Feature Extractor: Here we will freeze the weights of all networks except the fully connected final layer one. This last fully connected layer is replaced by a new one with random weights and only this layer is trained.
Introduction to Transfer Learning at HackerEarth
Transfer learning involves the approach in which the knowledge acquired 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 handle single tasks, developing 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 allows you to train deep neural networks with relatively little data. In transfer learning, knowledge from an already trained machine learning model is applied to a different but related problem. The general idea is to use the 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 much data. Instead of starting the learning process from scratch, you start from patterns that were 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 data set to see if meaningful results can be extracted when the machine learning algorithm is exposed to new data. Transfer learning is extremely popular due to the efficiency represented by the domain when working on very large data sets. In this course, you will learn all the fundamental concepts you need to get started and understand how transfer learning works.
TensorFlow Machine Learning Transfer Learning at Alison.com
This free online TensorFlow Machine Learning transfer learning course will introduce you to a new neural network architecture known as a convolutional neural network (CNN). You will also learn image classification and visualization as well as transfer learning with the pre-trained convolutional neural network and TensorFlow hub. You will also learn how to use the Estimator API to build 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 an equally interesting technique: transfer learning. Using a hands-on approach, Jonathan explains the basics of transfer learning, which allows you to leverage pre-trained parameters of an existing deep learning model for other tasks. It 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, learn how to use 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 implications of deep learning and prepare you to participate in the development of cutting-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 improve them with strategies such as Dropout, BatchNorm, Xavier/He initialization, etc. Prepare to master theoretical concepts and their industrial 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 how to implement them using the PyTorch deep learning framework. 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 a technology has grown tremendously over the past few years. More and more AI solutions are using Deep Learning as a core technology. However, studying this technology presents several challenges. IT professionals from diverse backgrounds need a streamlined resource to learn concepts and build models quickly. In this course, instructor Kumaran Ponnambalam provides a simplified path to understanding the various optimization and tuning options available for deep learning models and shows you how to use these options to improve models.
Essential Python training
PyTorch is quickly becoming one of the most popular deep learning frameworks, as well as a must-have skill in your AI toolbox. It has won the admiration of industry leaders due to its deep integration with Python; its integration with major cloud platforms, including Amazon SageMaker and Google Cloud Platform; and its calculation graphs which can be defined on the fly. In this course, join Jonathan Fernandes as he dives into the basics of deep learning using PyTorch. Starting from a working image recognition model, it shows how the various components fit together and work in tandem, from tensors, loss functions, and autogradation to troubleshooting a PyTorch network.
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