Here's a list of best online Deep Learning courses from top learning platforms Udemy, courses, edx, linkedin and more. Learn deep learning, Artificial Neural Networks, Deep Learning and Neural Networks in Python, TensorFlow, Deep Learning with Python and PyTorch and more.
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Hire Deep Learning Freelancer - Hire best Deep learning Freelance personal Tutor/ expert support from $5.
★ 4.5 (36,775+ ratings) | 303,445+ students | 22.5 hours on-demand video | Certificate of completion.
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included. Apply Artificial Neural Networks in practice.
Understand the intuition behind Artificial Neural Networks. Understand the intuition behind Convolutional Neural Networks. Understand the intuition behind Self-Organizing Maps. Apply Self-Organizing Maps in practice.
★ 4.6 (19,063+ ratings) | 241,475+ students | 6 hours on-demand video | Certificate of completion.
You will understand supervised machine learning (classification and regression) with real-world examples using Scikit-Learn. Make use of Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms.
Understand the pros and cons of various machine learning models, including Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and More.
★ 4.6 (7,315+ ratings) | 46,513+ students | 11.5 hours on-demand video | Certificate of completion.
Learn how Deep Learning REALLY works (not just some diagrams and magical black box code). Code a neural network from scratch in Python and numpy.
Learn how a neural network is built from basic building blocks (the neuron). Code a neural network using Google's TensorFlow. Derive the backpropagation rule from first principles.
★ 4.6 (4,796+ ratings) | 27,068+ students | 6.5 hours on-demand video | Certificate of completion.
Learn Data science, machine learning, and artificial intelligence in Python for students and professionals.
Derive and solve a linear regression model, and apply it appropriately to data science problems. Program your own version of a linear regression model in Python.
Use CNNs for Image Recognition, Natural Language Processing (NLP) +More! For Data Science, Machine Learning, and AI.
Understand convolution and why it's useful for Deep Learning. Implement a CNN in TensorFlow 2.
Understand and explain the architecture of a convolutional neural network (CNN).
Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI.
Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications.
Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data.
Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer vision applications.
Build natural language processing systems using TensorFlow.
Handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout.
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.
This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow.
The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models.
Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.
Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
In this course, you will learn What is deep learning, How do neural networks learn and what are activation functions, What are deep learning libraries and how do they compare to one another.
Start with this course, that will not only introduce you to the field of deep learning but give you the opportunity to build your first deep learning model using thepopular Keras library.
This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch..
In the first course, you learned the basics of PyTorch, learn how to build deep neural networks in PyTorch.
Also, you will learn how to train these models using state of the art methods.
In this TensorFlow course, you will learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline.
Much of theworld's data is unstructured. Think images, sound, and textual data. Learn how to apply Deep Learning with TensorFlow to this type of data to solve real-world problems.
Learn how deep learning algorithms can be used to solve important engineering problems.
You will will gain a principled understanding of the motivation, justification, and design considerations of the deep neural network approach to machine learning and will complete hands-on projects using TensorFlow and Keras.
This learning path is your entryway into the tools, concepts, and finer points of computer vision, natural language processing, and more.
The hottest new frontier in the universe of AI and machine learning is in deep learning and neural networks.
In this course, instructor Harshit Tyagi provides a complete guide to understanding NLP using recurrent neural networks (RNNs).
You will learn how you can train RNNs to predict the next word in a sentence, which in turn allows you to generate some original text.
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.
In this hands-on course, learn how to use the Python scientific stack to complete common data science tasks.
You can effectively process data with the Python scientific stack, including Pandas for data crunching, matplotlib for data visualization, NumPy for numeric computation, and more.
In this course, learn how to install TensorFlow and use it to build a simple deep learning model. After he shows how to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to create and train a machine learning model, as well as how to leverage visualization tools to analyze and improve your model.
Learn to build the deep learning models that are revolutionizing artificial intelligence.
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.
Learn advanced machine learning techniques and algorithms, including deployment to a production environment.
Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models.
In this hands-on introduction to deep learning, you will learn about different neural network types. You’ll develop your understanding of key deep learning vocabulary, concepts, and algorithm, enabling you to understand how deep learning frameworks work.
Discover deep learning with Python using Microsoft Cognitive Toolkit, and explore deep learning algorithms and neural networks.
Discover deep learning in Azure in this ExpertTrack covering AI fundamentals, machine learning, and deep learning with Python.
This ExpertTrack offers advanced training in artificial intelligence and deep learning for AI professionals, students, analysts and engineers looking to take their AI skills and career to a higher level.
This course is an introduction to artificial intelligence, using Microsoft Cognitive Services, with Python programming language as the medium.
Start your deep learning journey with this introductory Python-based course, exploring some of the fundamental applications of AI.
Learn Deep Learning online from one of these best deep learning and machine learning certification courses to develop the necessary industry ready skills and knowledge.