🚥 Deep Learning and Neural Network Specialization

Andrew Ng's specialization course with all assignments in neural network and deep learning.

Course 1 : Neural Network and Deep Learning

• PDF lectures:

  • Week1
  • Week2
  • Week3
  • Week4

• In the first course of the Deep Learning Specialization, comprises the foundational concept of neural networks and deep learning.

• Getting 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.

• Understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

🎲Programming Assignmets:

• The section assignment in the table and their dependencies.

  Numpy	LogReg with NN	Shallow NN	Deep NN A1	Deep NN A2
  Test Utils.py	Lr Utils.py	NN TestUtils.py	DNN TestUtils.py	DNN2 TestUtils.py
  PublicTest.py	Lr PublicTest.py	NN PublicTest.py	DNN PublicTest.py	DNN2 PublicTest.py
  		NN PlanarUtils.py	DNN Utils.py	DNN2 Utils.py
  		NN TestCases.py	DNN TestCases.py	Train Data
  				Test Data

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Course 2 : Improving Deep Neural Network

• PDF lectures:

  • Week1
  • Week2
  • Week3

• Discover and experiment with various initialization methods, apply L2 regularization and dropout to avoid model overfitting, and use gradient checking to identify errors in a fraud detection model.

• Develop your deep learning toolbox by adding more advanced optimizations, random mini-batching, and learning rate decay scheduling to speed up your models.

• Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily and train a neural network on a TensorFlow dataset..

🎲Programming Assignmets:

• The section assignment in the table and their dependencies.

  Initialization	Regularization	GradientCheck	OptimizationMethods	TensorFlowIntro
  Init Utils.py	Regu Utils.py	Grad Utils.py	Opt Utils.py	TfUtils.py ImprovUtils.py
  Init PublicTest.py	Regu PublicTest.py	Grad PublicTest.py	Opt PublicTest.py	ImprovPublicTest.py
  	Regu TestCases.py	Grad TestCases.py	Opt TestCases.py	ImprovTestUtils.py

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Course 3 : Structuring Machine Learning Projects

• PDF Lectures

  • Week 1
  • Week 2

• Learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

• Will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.

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Course 4 : Convolutional Neural Networks

• PDF lectures:

  • Week1
  • Week2
  • Week3
  • Week4

• Understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more

• Able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.

🎲Programming Assignmets:

• The section assignment in the table and their dependencies.

  a	b	c	d	e
  A	B	C	D	E
  f	g	h	i	j
  F	G	H	D	E