Announcing New Deep Learning Courses on Coursera Announcing New Deep Learning Courses on Coursera Announcing New Deep Learning Courses on Coursera


Step by step instructions to Master Deep Learning, and Break into AI.

In the event 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. This course will enable you to wind up plainly great at Deep Learning.

In five courses listed underneath, you will learn the foundations of Deep Learning, see how to build neural systems, and figure out how to lead successful machine learning projects. You will find out about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and then some. You will chip away at case studies from medicinal services, autonomous driving, sign language reading, music generation, and natural language processing. You will ace the theory, 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.

AI is transforming multiple industries. In the wake of completing this specialization, you will probably discover inventive approaches to apply it to your work.

COURSE 1 : Neural Networks and Deep Learning

Commitment Needed: a month of study, 3-6 hours every week

About the Course

On the off chance that you need to break into cutting-edge AI, this course will enable you to do as such. Deep learning engineers are exceptionally looked for after, and acing Deep learning will give you various new vocation openings. Deep learning is additionally another “superpower” that will give you a chance to build AI frameworks that simply weren’t conceivable a couple of years back.

In this course, you will take in the foundations of Deep learning. When you complete this class, you will: – Understand the major technology trends driving Deep Learning – Have the capacity to construct, prepare and apply completely associated Deep neural systems – Know how to implement effective (vectorized) neural systems – Understand the key parameters in a neural system’s architecture

This course likewise shows you how Deep Learning really functions, as opposed to cursory or surface-level description. So in the wake of finishing it, you will have the capacity to apply Deep Learning out how to a your own particular applications. In the event that you are searching for an job in AI, after this course you will likewise have the capacity to answer fundamental interview questions.


Introduction to deep learning

Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.

Video · Welcome

Video · What is a neural network?

Video · Supervised Learning with Neural Networks

Video · Why is Deep Learning taking off?

Video · About this Course

Reading · Frequently Asked Questions

Video · Course Resources

Reading · How to use Discussion Forums

Quiz · Introduction to deep learning

Video · Geoffrey Hinton interview


Neural Networks Basics

Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.

Video · Binary Classification

Video · Logistic Regression

Video · Logistic Regression Cost Function

Video · Gradient Descent

Video · Derivatives

Video · More Derivative Examples

Video · Computation graph

Video · Derivatives with a Computation Graph

Video · Logistic Regression Gradient Descent

Video · Gradient Descent on m Examples

Video · Vectorization

Video · More Examples of Vectorization

Video · Vectorizing Logistic Regression

Video · Vectorizing Logistic Regression’s Gradient Output

Video · Broadcasting in Python

Video · A note on python/numpy vectors

Video · Quick tour of Jupyter/iPython Notebooks

Video · Explanation of logistic regression cost function (optional)

Quiz · Neural Network Basics

Reading · Programming Assignment FAQ

Other · Python Basics with numpy (optional)

Practice Programming Assignment · Python Basics with numpy (optional)

Other · Logistic Regression with a Neural Network mindset

Programming Assignment · Logistic Regression with a Neural Network mindset

Video · Pieter Abbeel interview


Shallow neural networks

Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.

Video · Neural Networks Overview

Video · Neural Network Representation

Video · Computing a Neural Network’s Output

Video · Vectorizing across multiple examples

Video · Explanation for Vectorized Implementation

Video · Activation functions

Video · Why do you need non-linear activation functions?

Video · Derivatives of activation functions

Video · Gradient descent for Neural Networks

Video · Backpropagation intuition (optional)

Video · Random Initialization

Quiz · Shallow Neural Networks

Other · Planar data classification with a hidden layer

Programming Assignment · Planar data classification with a hidden layer

Video · Ian Goodfellow interview


Deep Neural Networks

Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.

Video · Deep L-layer neural network

Video · Forward and Backward Propogation

Video · Forward Propagation in a Deep Network

Video · Getting your matrix dimensions right

Video · Why deep representations?

Video · Building blocks of deep neural networks

Video · Parameters vs Hyperparameters

Video · What does this have to do with the brain?

Quiz · Key concepts on Deep Neural Networks

Other · Building your Deep Neural Network: Step by Step

Programming Assignment · Building your deep neural network: Step by Step

Other · Deep Neural Network – Application

Programming Assignment · Deep Neural Network Application


COURSE 2 : Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Commitment : 3 weeks, 3-6 hours per week

About the Course

This course will show you the “magic” of getting deep learning out how to function well. Instead of the Deep learning process being a discovery, you will understand what drives execution, and have the capacity to all the more deliberately get great outcomes. You will likewise learn TensorFlow. Following 3 weeks, you will: – Comprehend industry best-hones for building Deep learning applications. – Have the capacity to successfully utilize the basic neural system “traps”, including instatement, L2 and dropout regularization, Group standardization, inclination checking, – Have the capacity to implement and apply a variety of optimization algorithms, for example, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.. – See new prescribed procedures for the Deep learning period of how to set up prepare/dev/test sets and examine bias/variance – Have the capacity to implement a neural system in TensorFlow.


Practical aspects of Deep Learning

Video · Train / Dev / Test sets

Video · Bias / Variance

Video · Basic Recipe for Machine Learning

Video · Regularization

Video · Why regularization reduces overfitting?

Video · Dropout Regularization

Video · Understanding Dropout

Video · Other regularization methods

Video · Normalizing inputs

Video · Vanishing / Exploding gradients

Video · Weight Initialization for Deep Networks

Video · Numerical approximation of gradients

Video · Gradient checking

Video · Gradient Checking Implementation Notes

Quiz · Practical aspects of deep learning

Other · Initialization

Programming Assignment · Initialization

Other · Regularization

Programming Assignment · Regularization

Other · Gradient Checking

Programming Assignment · Gradient Checking

Video · Yoshua Bengio interview


Optimization algorithms

Video · Mini-batch gradient descent

Video · Understanding mini-batch gradient descent

Video · Exponentially weighted averages

Video · Understanding exponentially weighted averages

Video · Bias correction in exponentially weighted averages

Video · Gradient descent with momentum

Video · RMSprop

Video · Adam optimization algorithm

Video · Learning rate decay

Video · The problem of local optima

Quiz · Optimization algorithms

Other · Optimization

Programming Assignment · Optimization

Video · Yuanqing Lin interview


Hyperparameter tuning, Batch Normalization and Programming Frameworks

Video · Tuning process

Video · Using an appropriate scale to pick hyperparameters

Video · Hyperparameters tuning in practice: Pandas vs. Caviar

Video · Normalizing activations in a network

Video · Fitting Batch Norm into a neural network

Video · Why does Batch Norm work?

Video · Batch Norm at test time

Video · Softmax Regression

Video · Training a softmax classifier

Video · Deep learning frameworks

Video · TensorFlow

Quiz · Hyperparameter tuning, Batch Normalization, Programming Frameworks

Other · Tensorflow

Programming Assignment · Tensorflow


COURSE 3 : Structuring Machine Learning Projects

Commitment:2 weeks of study, 3-4 hours/week

About the Course

You will figure out how to build an effective machine learning project. On the off chance that you try to be a specialized pioneer in AI, and know how to set bearing for your collaboration, this course will demonstrate to you how. A lot of this substance has never been instructed somewhere else, and is drawn from experience building and dispatching numerous Deep learning products. This course additionally has two “flight simulators” that let you rehearse basic leadership as a machine learning project leader. This gives “industry encounter” that you may some way or another get simply following quite a while of ML work involvement. Following 2 weeks, you will: – See how to analyze mistakes in a machine learning framework, and – Have the capacity to organize the most encouraging headings for lessening blunder – Comprehend complex ML settings, for example, bungled preparing/test sets, and contrasting with as well as outperforming human-level execution – Know how to apply end-to-end learning, transfer learning, and multi-task learning.


ML Strategy (1)

Video · Why ML Strategy

Video · Orthogonalization

Video · Single number evaluation metric

Video · Satisficing and Optimizing metric

Video · Train/dev/test distributions

Video · Size of the dev and test sets

Video · When to change dev/test sets and metrics

Video · Why human-level performance?

Video · Avoidable bias

Video · Understanding human-level performance

Video · Surpassing human-level performance

Video · Improving your model performance

Reading · Machine Learning flight simulator

Quiz · Bird recognition in the city of Peacetopia (case study)

Video · Andrej Karpathy interview


ML Strategy (2)

Video · Carrying out error analysis

Video · Cleaning up incorrectly labeled data

Video · Build your first system quickly, then iterate

Video · Training and testing on different distributions

Video · Bias and Variance with mismatched data distributions

Video · Addressing data mismatch

Video · Transfer learning

Video · Multi-task learning

Video · What is end-to-end deep learning?

Video · Whether to use end-to-end deep learning

Quiz · Autonomous driving (case study)

Video · Ruslan Salakhutdinov interview


COURSE 4 : Convolutional Neural Networks

About the Course

This course will show you how to construct convolutional neural systems and apply it to image data. On account of Deep learning, computer vision is working obviously better than only two years back, and this is empowering various exciting applications going from safe self-ruling driving, to accurate face recognition, to automatic reading of radiology images. You will: – See how to construct a convolutional neural system, including recent variations such as residual networks. – Know how to apply convolutional systems to visual identification and acknowledgment undertakings. – Know to utilize neural style transfer to generate art. – Have the capacity to apply these algorithms to a variety of image, video, and other 2D or 3D data.


COURSE 5 : Sequence Models

About the Course

This course will show you how to build models for natural language, audio, and other sequence data. On account of Deep learning, sequence algorithms are working much better than only two years prior, and this is empowering various energizing applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: – See how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. – Be able to apply sequence models to natural language problems, including text synthesis. Have the capacity to apply sequence models to audio applications, including speech recognition and music synthesis.


Andrew Ng

Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain

Teaching Assistant – Younes Bensouda Mourri

Mathematical & Computational Sciences, Stanford University

Teaching Assistant – Kian Katanforoosh

M.S. Stanford University (Walter J. Gores 2017), B.S Ecole Centrale Paris

Details on the course can be found here.

Note: When these courses become available for free in future will update here.

PS: Financial Aid is available for learners who cannot afford the fee.




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