( ** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka video on “Deep Learning Frameworks” (https://goo.gl/27nAwR) provides you an insight into the top 8 Deep Learning frameworks you should consider learning
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#PyTorch #TensorFlow #DeepLearning #Python
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About the Course
Edureka’s Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
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Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a ‘Data Scientist’
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. Information Architects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
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Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
How it Works?
1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!
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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.
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During the mid-80s, Yoshua Bengio began his research in neural networks. This became the gateway to his career in artificial intelligence (AI) and deep learning (DL). In his presentation, Yoshua takes us through the evolution of DL: How it started in supervised learning, its progression from speech recognition to computer vision, till it reached human level processing and where we can expect it to go from here. At its core, of AI needs knowledge. Early models of AI failed as human knowledge, such as intuition, is implicit. DL was born as a way to let machines help intelligent decisions on its own. Empowered by increasing computational power and improved algorithms, DL and deep neural networks has advanced rapidly speech recognition, computer vision and natural language processing and visual question answering.
Yoshua Bengio (PhD in CS, McGill University, 1991), post-docs at M.I.T. (Michael Jordan) and AT&T Bell Labs (Yann LeCun), CS professor at Université de Montréal, Canada Research Chair in Statistical Learning Algorithms, NSERC Chair, CIFAR Fellow, member of NIPS foundation board and former program/general chair, co-created ICLR conference, authored two books and over 300 publications, the most cited being in the areas of deep learning, recurrent networks, probabilistic learning, natural language and manifold learning. He is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks.
This presentation took place at RE•WORK Deep Learning Summit in Boston, May 2016. View more videos from RE•WORK Summits here: http://videos.re-work.co/
RE•WORK events bring together breakthrough technology, cutting-edge science and entrepreneurship to re-work the future, finding solutions to challenges in business and society.
More about our events: https://re-work.co/events
More on this event: https://re-work.co/events/deep-learning-boston-2016
In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples, eventually coming to a definitive conclusion.
The code for the TensorFlow vs Theano part of the video is here:
An article that explains the differences in more detail:
I created a Slack channel for us, sign up here:
Learn more about TF Learn here:
Learn more about TensorFlow here:
More on Keras here:
More on SciKit Learn here:
More on Caffe here:
More on Theano here:
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