Top Deep Learning Courses Available on LinkedIn

 Deep Learning CoursesIf you are looking to learn Deep learning, these courses are best for you.

Deep Learning is now everywhere i.e., in internal and external environment of any and every industry. It’s not just using its applications on healthcare, finance, corporate, education and many other. Deep Leaning Courses are now available everywhere and anywhere. Deep Learning which also primarily known as Artificial Intelligence is much efficient and effective in terms of work. To make it easy and flexible here are the top deep learning courses on LinkedIn.

Deep Learning Courses provide great functionality to deal with different things including AI, Programming Languages, Math, Scientific Function and much more. Here are top courses available on LinkedIn.

 

Building Deep Learning Applications with Keras 2.0

Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. Here in this course, you can learn how to install Keras and how to use it to build deep learning model. Learn about many powerful pre-trained deep learning models included in Keras and how to use them. Discover how to deploy Keras models, and how to transfer data between Keras and TensorFlow so that you can take advantage of all the TensorFlow tools while using Keras. When you wrap up this course, you’ll be ready to start building and deploying your own models with Keras

 

Deep Learning: Face Recognition

Face recognition is used for everything from automatically tagging pictures to unlocking cell phones. And with recent advancements in deep learning, the accuracy of face recognition has improved. In this course, learn how to develop a face recognition system that can detect faces in images, identify the faces, and even modify faces with “digital makeup” like you’ve experienced in popular mobile apps. Find out how to set up a development environment. Discover tools you can leverage for face recognition. See how a machine learning model can be trained to analyze images and identify facial landmarks. Learn the steps involved in coding facial feature detection, representing a face as a set of measurements, and encoding faces. Additionally, learn how to repurpose and adjust pre-existing systems.

 

Deep Learning: Image Recognition

Thanks to deep learning, image recognition systems have improved and are now used for everything from searching photo libraries to generating text-based descriptions of photographs. In this course, learn how to build a deep neural network that can recognize objects in photographs. Find out how to adjust state-of-the-art deep neural networks to recognize new objects, without the need to retrain the network. Explore cloud-based image recognition APIs that you can use as an alternative to building your own systems. Learn the steps involved to start building and deploying your own image recognition system.

 

Building and Deploying Deep Learning Applications with TensorFlow

TensorFlow is one of the most popular deep learning frameworks available. It’s used for everything from cutting-edge machine learning research to building new features for the hottest start-ups in Silicon Valley. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. The instructor explains how to deploy models locally or in the cloud.

 

Training Neural Networks in Python

Having a variety of great tools at your disposal isn’t helpful if you don’t know which one you really need, what each tool is useful for, and how they all work. In this course, take a deep dive into the inner workings of neural networks, so that you’re able to work more effectively with machine learning tools. Instructor Eduardo Corpeño helps you learn by example by providing a series of exercises in Python to help you to grasp what’s going on inside. Learn how to relate parts of a biological neuron to Python elements, which allows you to make a model of the brain. Then, learn how to build and train a network, as well as create a neural network that recognizes numbers coming from a seven-segment display. Even though you’ll probably work with neural networks from a software suite rather than by writing your own code, the knowledge you’ll acquire in this course can help you choose the right neural network architecture and training method for each problem you face.

 

Neural Networks and Convolutional Neural Networks Essential Training

Take a deep dive into neural networks and convolutional neural networks, two key concepts in the area of machine learning. In this course you can learn fundamental neural and convolutional neural network concepts. Beginning from introduction to the components of neural networks, activation functions and backpropagation to convolutional neural networks. Also learn how to build a neural network model using Keras. Plus, learn about VGG16, the history of the ImageNet challenge, and more.

 

Learning TensorFlow with JavaScript

JavaScript developers can use the TensorFlow framework to create a machine learning (ML) project. This course introduces you to ML basics, and demonstrates how to set up and use TensorFlow to train a model and generate live results. Here you can learn how to create a new project; how to work with different tensor types, variables, models, and layers; how to import a project and explore datasets; how TensorFlow executes model training; how to convert a saved model for the web; and more.

 

Introduction to AWS DeepLens

AWS DeepLens is the world’s first deep learning-enabled video camera for developers. In this hands-on course, you can learn how to Deeplens works, how to set it up and to solve issues. You can also go through variety of projects available with Deeplens, including ones dealing with object recognition.view

 

NLP with Python for Machine Learning Essential Training

With the increased amount of data publicly available and the increased focus on unstructured text data, understanding how to clean, process, and analyze that text data is tremendously valuable. In this course, you can gain knowledge about how you need to tackle complex problems using machine learning. This course provides a summary of basic natural language processing (NLP) concepts, covers advanced data cleaning and vectorization techniques, and then takes a deep dive into building machine learning classifiers. You can also learn how to build two different types of machine learning models, as well as how to evaluate and test variations of those models.

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