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Neural-Networks-and-Deep-Learning

Shristi Kedia(BITS Pilani)

Course 1 : Neural Networks and Deep Learning

  • In this course, the foundations of deep learning were covered. The major learnings after completing this course were :
    • Understood the major technology trends driving Deep Learning.
    • Been able to build, train and apply Fully Connected Deep Neural Networks.
    • Leant how to implement efficient (Vectorized) neural networks.
    • Understood the key parameters in a neural network's architecture.
    • This course also taught how Deep Learning actually works, rather than presenting only a cursory or surface-level description.

Programming Assignments :

  • The assignments of this course are in the folder Course 1 and it consits of the following assignments:
    • Python Basics with Numpy : [notebook]
    • Logistic Regression with a Neural Network mindset : [notebook]
    • Planar data classification with one hidden layer : [notebook]
    • Building your Deep Neural Network Step by Step : [notebook]
    • Deep Neural Network Application : [notebook]

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

  • After finishing the course the major takeaways were:
    • Understood industry best-practices for building deep learning applications.
    • Been able to effectively use the common neural network "tricks", including Initialization, L2 and Dropout Regularization, Batch Normalization, Gradient Checking.
    • Been able to implement and apply a variety of optimization algorithms, such as mini-batch Gradient Descent, Momentum, RMSprop and Adam, and check for their convergence.
    • Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze Bias/Variance.
    • Been able to implement a neural network in TensorFlow.

Programming Assignments :

  • The assignments of this course are in the folder Course 2 and it consits of the following assignments:

Course 3 : Structuring Machine Learning Projects

  • This course taught how to build a successful machine learning project. The major learning after finishing this course were as follows :
    • Understood how to Diagnose Errors in a machine learning system, and been able to prioritize the most promising directions for reducing error.
    • Understood complex ML settings, such as Mismatched training/test sets, and comparing to and/or surpassing human-level performance
    • Learnt how to apply End-To-End Learning, Transfer Learning, and Multi-Task Learning.

Course 4 : Convolutional Neural Networks

  • This course taught how to build Convolutional Neural Networks and apply it to image data. The major learnings after completing this course were :
    • Understand how to build a convolutional neural network, including recent variations such as residual networks.
    • Know how to apply convolutional networks to visual detection and recognition tasks.
    • Know to use neural style transfer to generate art.
    • Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.

Programming Assignments :

  • The assignments of this course are in the folder Course 4 and it consits of the following assignments:

Course 5 : Sequence Models

  • This course taught how to build models for Natural Language, Audio, and other Sequence Data. After finishing the course the major takeaways were :
    • Understood how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
    • Been able to apply sequence models to natural language problems, including Text Synthesis.
    • Been able to apply sequence models to audio applications, including Speech Recognition and Music Synthesis.

Programming Assignments :

  • The assignments of this course are in the folder Course 5 and it consits of the following assignments: