This is the repository for my implementations on the Deep Learning Specialization from Coursera.
Taught by Andrew Ng
Done and pass 100% all Quiz and Programming Assignments
Course 1. Neural Networks and Deep Learning
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
- Week2: Neural Network Basics
- Week3: Shallow Neural Network Implementation
- Week4: Deep Neural Network Implementation
- 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.
- Week1: Initialization, Regularization & Gradient Check
- Week2: Optimization Algorithms
- Week3: Hyperparameter tuning, Batch Normalization & Tensorflow Implementation
Course 3. Structuring Machine Learning Projects
- 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
Course 4. Convolutional Neural Networks
- 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.
- Week1: Convolutional Neural Network Implementation in Numpy
- Week2:
- Week3:
- Week4:
Course 5. Sequential Models
- 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.
- Week1:
- Week2:
- Week3: