This is the repository for my implementations on the Deep Learning Specialization from Coursera.

Taught by Andrew Ng

Foundations of Deep Learning:

• Understand the major technology trends driving Deep Learning
• Be able to build, train and apply fully connected deep neural networks
• Know how to implement efficient (vectorized) neural networks
• Understand the key parameters in a neural network's architecture

Codes:

• Week2: Neural Network Basics
• Week3: Shallow Neural Network Implementation
• Week4: Deep Neural Network Implementation
  • Mathematical demonstration: Backpropagation
  • Mathematical demonstration: Cross-entropy & Softmax gradients

• Understand industry best-practices for building deep learning applications.
• Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
• Be 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
• Be able to implement a neural network in TensorFlow.

Codes:

• Week1: Initialization, Regularization & Gradient Check
• Week2: Optimization Algorithms
• Week3: Hyperparameter tuning, Batch Normalization & Tensorflow Implementation
  • Mathematical demostration: Batch Normalization Gradient
  • Paper: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

• Understand how to diagnose errors in a machine learning system, and
• Be able to prioritize the most promising directions for reducing error
• Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
• Know how to apply end-to-end learning, transfer learning, and multi-task learning

• 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.

Codes:

• Week1: Convolutional Neural Network Implementation in Numpy
• Week2:
  • Keras CNN Implementation
  • ResNet Keras Implementation
  • Paper: Deep Residual Learning for Image Recognition
• Week3:
  • YOLO Implementation(You Only Look Once)
  • Paper: You Only Look Once: Unified, Real-Time Object Detection
  • Paper: YOLO9000: Better, Faster, Stronger
• Week4:
  • Neural Style Transfer
  • Paper: A Neural Algorithm of Artistic Style
  • Face Recognition
  • Paper: FaceNet: A Unified Embedding for Face Recognition and Clustering
  • Paper: Going deeper with convolutions (Inception Networks)

• Understand 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.
• Be able to apply sequence models to audio applications, including speech recognition and music synthesis.

Codes:

• Week1:
  • RNN & LSTM Implementation in Numpy (Including backpropagation)
    • Mathematical demonstration: RNN gradient through time
    • Mathematical demonstration: LSTM gradient through time
    • Mathematical demonstration: GRU gradient through time
    • Paper: Vanishing/Exploding gradient & Clipping
  • Character-Level Language Modeling
  • Sequence Sampling Generation LSTM
• Week2:
  • Natural Language Processing & Word Embeddings
  • Operations on word vectors - Debiasing
• Week3:
  • Neural Machine Translation with Attention
  • Trigger word detection