15 Deep Learning Open Courses and Tutorials [ UPDATED ]

15 Deep Learning Open Courses and Tutorials

Best Deep Learning FREE Courses Online

 

The rise of artificial intelligence is grounded in the success of deep learning. Neural networks are a broad family of algorithms that have formed the basis for deep learning.Deep learning and deep reinforcement learning have as of late been effectively connected in an extensive variety of real-world problems.

Here are 15 online courses and tutorials in deep learning and deep reinforcement learning, and applications in natural language processing (NLP), computer vision, and control systems.

The courses cover the basics of neural networks, convolutional neural networks, recurrent networks and variants, difficulties in training deep networks, unsupervised learning of representations, deep belief networks, deep Boltzmann machines, deep Q-learning, value function estimation and optimization, and Monte Carlo tree search.

Deep Learning has accomplished huge increases over other machine learning approaches on numerous troublesome learning assignments, prompting cutting edge execution crosswise over a wide range of areas.

Deep Learning does successful automatic feature extraction, reducing the need for guesswork and heuristics on this key issue.

Current programming gives adaptable designs that can be adjusted for new areas effectively.

 

But However:

 

  • Deep Learning can require immense amount of training information.
  • Deep Learning can require colossal amount of processing power.
  • Designs can be unpredictable and regularly should be customized to a particular application.
  • The subsequent models may not be effectively interpretable.

 

Also you might don’t want to miss Top 10 Free Machine Learning Online Courses And  Top 7 Best Online Courses to Learn Deep Learn which are free courses here.

 

For Latest new deep learning courses, see below:

15 deep learning open courses and tutorials for both theoretical and uses of deep learning.

 

1.) Deep Learning

Teacher: Russ Salakhutdinov

Department: Machine Learning

Institution: Carnegie Mellon University

Year: 2017

Value: Free

Portrayal:

This course covers a portion of the hypothesis and philosophy of Deep learning.

 

Course Link: Click Here

 

2.) Deep Learning

 

Teacher: Vincent Vanhoucke, Arpan Chakraborty

Institution: Google

Stage: Udacity

Value: Free

Prerequisites:

Before taking this course, and notwithstanding the essentials and prerequisites delineated for the Machine Learning Architect Nanodegree program, you should know the accompanying knowledge and aptitudes: Least 2 years of programming background Git and GitHub encounter. Fundamental machine learning information (particularly supervised learning) Basic statistics knowledge (mean, variance, standard deviation, etc.) Linear algebra (vectors, matrices, etc.) Calculus (differentiation, integration, partial derivatives, etc.)

 

Portrayal:

In this course, you’ll build up a reasonable comprehension of the inspiration for Deep learning, and outline shrewd frameworks that gain from complex or potentially huge scale datasets. We’ll demonstrate to you industry standards to prepare and enhance fundamental neural systems, convolutional neural systems, and long here and now memory systems. Complete learning frameworks in TensorFlow will be presented through ventures and assignments. You will figure out how to take care of new classes of issues that were once thought restrictively difficult, and come to better welcome the perplexing idea of human insight as you take care of these same issues easily utilizing Deep learning strategies.

Course Link: Click Here

 

 

3.) Theories of Deep Learning

Teacher: David Donoho, Hatef Monajemi, Vardan Papyan

Department: Statistics

Institution: Stanford University

Stage: Independent

Year: 2017

Value: Free

Portrayal:

The astounding late achievements of Deep learning are absolutely exact. By the by savvy people dependably endeavor to clarify imperative improvements hypothetically. In this writing course we will audit late work of Bruna and Mallat, Mhaskar and Poggio, Papyan and Elad, Bolcskei and co-writers, Baraniuk and co-writers, and others, looking to manufacture hypothetical systems inferring Deep systems as results. After beginning foundation addresses, we will have a portion of the creators showing addresses on particular papers. This course meets once week after week.

 

Course Link: Click Here

 

 

4.) Deep Learning

 

Teacher: Yoshua Bengio

Department: Département d’informatique et recherche opérationnelle

Institution: Université de Montréal

Stage: Independent

Year: 2016

Value: Free

 

Portrayal:

This is a course on portrayal learning when all is said in done and Deep learning specifically. Deep learning has as of late been in charge of a substantial number of amazing observational picks up over a wide exhibit of utilizations incorporating most significantly in protest acknowledgment and identification in pictures and discourse acknowledgment. In this course we will investigate both the basics and late advances in the zone of Deep learning. Our attention will be on neural system write models including convolutional neural systems and intermittent neural systems, for example, the LSTM. We will likewise think about some probabilistic graphical models, including undirected models, for example, the Boltzmann machines and coordinated models that have as of late indicated guarantee. Guideline style: Generally, this will be a “flipped class”. We will spend approx. half of the class time working through inquiries. Understudies are in charge of staying up with the latest with the course material outside of class time. The material to be investigated for each class will be made accessible on the course site.

Course Link: Click Here

 

 

5.) Deep Learning Specialization

 

Teacher: Andrew Ng

Stage: Coursera

Year: 2017

Value: Free

 

Portrayal:

 

On the off chance that you need to break into AI, this Specialization will enable you to do as such. Deep Learning is a standout amongst the most very looked for after abilities in tech. We will enable you to wind up noticeably great at Deep Learning. In five courses, you will take in the establishments of Deep Learning, see how to assemble neural systems, and figure out how to lead effective machine learning ventures.

You will find out about Convolutional systems, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He instatement, and that’s only the tip of the iceberg. You will chip away at contextual analyses from human services, self-governing driving, gesture based communication perusing, music age, and characteristic dialect preparing.

You will ace the hypothesis, as well as perceive how it is connected in industry. You will rehearse every one of these thoughts in Python and in TensorFlow, which we will educate. You will likewise get notification from numerous best pioneers in Deep Realizing, who will impart to you their own stories and give you vocation exhortation. AI is changing various enterprises. In the wake of completing this specialization, you will probably discover innovative approaches to apply it to your work. We will enable you to ace Deep Learning, see how to apply it, and construct a profession in AI.

Course Link: Click Here

 

 

6.) Deep Reinforcement Learning

 

Teacher: Pieter Abbeel

Department: The Simons Institute for the Theory of Computing

Institution: University of California Berkeley

Stage: Independent

Year: 2017

Value: Free

 

Portrayal:

This is an one hour instructional exercise covers the fundamental of Deep Reinforcement Learning and open inquiries in the field. It will cover four sections: 1. Classical reinforcement learning: policy gradients, Actor-Critic, Q-learning, 2. Representation in exploration, 3. Different Approaches / Architectures: value iteration networks, prediction, modular networks, Option-Critic, feudal networks, and 4. Meta learning: MAML, RL2. This tutorial has a video lecture.

 

Course Link: Click Here

 

 

7.) Tutorial on Deep Learning

Teacher: Ruslan Salakhutdinov

Department: Simons Institute for the Theory of Computing

Institution: University of California Berkeley

Stage: Independent

Year: 2017

Value: Free

Portrayal:


This tutorial includes Supervised Learning, Deep Networks, Unsupervised Learning, Learning Deep Generative Models, and Model Evaluation and Open Research Questions. There are 4 video lectures, and their slides which can be found at the below following links.

talk_Simons_part1

talk_Simons_part2

talk_Simons_part3

talk_Simons_part4

 

8.) Deep Reinforcement Learning

Teacher: Sergey Levine

Department: Computer Science

Institution: University of California Berkeley

Stage: Independent

Year: 2017

Value: Free

CS189 or equal is an essential for the course. This course will expect some recognition with reinforcement learning, numerical optimization and machine learning. Understudies who are not comfortable with the ideas underneath are urged to review utilizing the references first.

 

Depiction:

The course addresses are accessible underneath. The course isn’t being offered as an online course, and the recordings are given just to your own enlightening and stimulation purposes. They are not some portion of any course necessity or degree-bearing college program.

Course Link: Click Here

 

 

9.) DEEP LEARNING AND REINFORCEMENT LEARNING SUMMER SCHOOL 2017

Teacher: Yoshua Bengio, Joelle Pineau, Doina Precup

Institution: Montreal Institute for Learning Algorithms (MILA)

Stage: Independent

Year: 2017

Value: Free

 

Portrayal:

The Deep Learning Summer School (DLSS) is done for graduate understudies and modern designers and analysts who as of now have some fundamental information of machine learning and wish to take in more about this quickly developing field of research.

Deep neural networks that learn to represent data in numerous layers of increasing abstraction have drastically enhanced the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks.

In a joint effort with DLSS we will hold the main release of the Montreal Reinforcement Learning Summer School (RLSS). RLSS will cover the nuts and bolts of reinforcement learning and demonstrate its latest research patterns and disclosures, and additionally introduce a chance to interface with graduate understudies and senior scientists in the field.

The school is planned for graduate understudies in Machine Learning and related fields. Members ought to have progressed earlier preparing in software engineering and arithmetic, and inclination will be given to understudies from inquire about labs partnered with the CIFAR program on Learning in Machines and Brains.

 

Course Link: Click Here

 

10.) Lecture Collection | Natural Language Processing with Deep Learning

 

Teacher: Chris Manning, Richard Socher

Department: Computer Science

Institution: Stanford University

Stage: Independent

Year: 2017

Value: Free

 

Portrayal:

This is the documented variant of “Natural Language Processing with Deep Learning”, educated in Winter 2017 at Stanford. Natural language processing manages the key artificial intelligence innovation of understanding complex human language communication. This address arrangement gives a careful prologue to the front line explore in Deep learning connected to NLP, an approach that has as of late gotten elite crosswise over a wide range of NLP undertakings including question noting and machine translation.

 

Course Link: Click Here

 

11.) Deep Learning for Natural Language Processing

 

Teacher: Phil Blunsom, Chris Dyer, Edward Grefenstette, Karl Moritz Hermann, Andrew Senior

Department: Computer Science

Institution: University of Oxford

Stage: Independent

Year: 2017

Value: Free

Portrayal:


This is an advanced course on natural language processing. This course will be lead by Phil Blunsom and conveyed in partnership with the DeepMind Natural Language Research Group. Consequently processing natural language inputs and delivering language outputs is a key segment of Artificial General Intelligence.

The course will cover a scope of uses of neural networks in NLP incorporating analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions.

All through the course the practical execution of such models on CPU and GPU equipment will likewise be talked about.

Course Link: Click Here

 

 

12.) Convolutional Neural Networks for Visual Recognition

Teacher: Fei-Fei Li

Department: Computer Science

Institution: Stanford University

Stage: Independent

Year: 2017

Value: Free

 

Portrayal:


Computer Vision has turned out to be universal in our general public, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Center to a considerable lot of these applications are image classification, localization and detection.

This course is a deep jump into points of interest of the deep learning architectures with an emphasis on learning end-to-end models for these tasks, particularly image classification. Amid the 10-week course, understudies will figure out how to implement, train and debug their own neural networks and gain a detailed comprehension of cutting-edge research in computer vision.

The last assignment will include training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet).

Course Link: Click Here

 

 

13.) Tutorial: Deep Reinforcement Learning

Teacher: David Silver

Department: Computer Science

Institution: University College London

Stage: Independent

Year: 2015

Value: Free

Portrayal:

In this instructional exercise we will examine how reinforcement learning (RL) can be joined with Deep learning (DL). There are a few approaches to consolidate DL and RL together, including value-based, policy-based, and model-based approaches with planning. A few of these methodologies have understood uniqueness issues, and will display straightforward strategies for tending to these hazards.

The discussion will incorporate a contextual analysis of late triumphs in the Atari 2600 space, where a solitary specialist can figure out how to play a wide range of amusements straightforwardly from crude pixel input. This instructional exercise is from second Multidisciplinary Meeting on Fortification Learning and Basic leadership (RLDM), Edmonton 2015.

Course Link: Click Here

 

14.) Deep Reinforcement Learning and Control

Teacher: Katerina Fragkiadaki, Ruslan Satakhutdinov

Department: Machine Learning

Institution: Carnegie Mellon University

Stage: Independent

Year: 2017

Value: Free

 

Prerequisites:

Recommended applicable courses in MLD are 10701 Introduction to Machine Learning, 10807 Topics in Deep Learning, 10725 Convex Optimization, or online equivalent versions of these courses. Understudies less comfortable with reinforcement learning can warm start with the first chapters of Sutton&Barto and with the first lectures of Dave Silver’s course.

Portrayal:
This course permits to Implement and try different things with existing algorithms for learning control strategies guided by reinforcement, expert demonstrations or self-trials.

Have the capacity to comprehend look into papers in the field of robotic learning.

Experiment with a few thoughts/extensions of your own. Specific concentrate on joining genuine tangible flag from vision or material detecting, and investigating the collaboration between gaining from reenactment as opposed to gaining from genuine experience.

 

Course Link: Click Here

 

 

15.) Tutorial on Deep Reinforcement Learning

Teacher: John Schulman

Institution: Machine Learning Summer School

Stage: Independent

Year: 2016

Value: Free

Portrayal:


This instructional exercise will cover deep reinforcement learning, with 4 recorded video
lectures. Each video is around one-hour long. The lecture slide and lab material are available at

http://learning.mpi-sws.org/mlss2016/speakers/ at “deep reinforcement learning” section instructed by John Schulman from UC Berkeley.

Reinforcement learning studies decision making and control, and how a decision-making agent can learn to act ideally in a previously unknown environment. Deep reinforcement learning studies how neural networks can be used in reinforcement learning algorithms, making it possible to learn the mapping from raw sensory inputs to raw motor outputs, removing the need to hand-engineer this pipeline.

The point of the instructional exercise is to acquaint you with the most vital procedures in Deep reinforcement learning. This course will incorporate hands-on labs, where you will actualize the calculations talked about in the addresses.

Course Link : Click Here

 

 

 

BONUS

 

 

Creative Applications of Deep Learning with TensorFlow : Click Here

S094: Deep Learning for Self-Driving Cars Click Here

Practical Deep Learning For Coders, Part 1 Click Here

Cutting Edge Deep Learning for Coders — Part 2 Click Here

Learn TensorFlow and deep learning, without a Ph.D.  Click Here

Online Course on Neural Networks Click Here

Deep Learning 101 Click Here

 

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