andersy005 / Deep Learning Specialization Coursera
Licence: mit
Deep Learning Specialization by Andrew Ng on Coursera.
Stars: ✭ 483
Projects that are alternatives of or similar to Deep Learning Specialization Coursera
Coursera Deep Learning Specialization
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
Stars: ✭ 188 (-61.08%)
Mutual labels: jupyter-notebook, coursera, neural-networks
Deeplearning.ai Natural Language Processing Specialization
This repository contains my full work and notes on Coursera's NLP Specialization (Natural Language Processing) taught by the instructor Younes Bensouda Mourri and Łukasz Kaiser offered by deeplearning.ai
Stars: ✭ 473 (-2.07%)
Mutual labels: jupyter-notebook, coursera, neural-networks
Coursera Deep Learning Deeplearning.ai
(完结)网易云课堂微专业《深度学习工程师》听课笔记,编程作业和课后练习
Stars: ✭ 344 (-28.78%)
Mutual labels: jupyter-notebook, coursera
Amazon Forest Computer Vision
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
Stars: ✭ 346 (-28.36%)
Mutual labels: jupyter-notebook, neural-networks
Start Machine Learning In 2020
A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!
Stars: ✭ 357 (-26.09%)
Mutual labels: coursera, neural-networks
Augmentor
Image augmentation library in Python for machine learning.
Stars: ✭ 4,594 (+851.14%)
Mutual labels: jupyter-notebook, neural-networks
Tbd Nets
PyTorch implementation of "Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning"
Stars: ✭ 345 (-28.57%)
Mutual labels: jupyter-notebook, neural-networks
Tf 2.0 Hacks
Contains my explorations of TensorFlow 2.x
Stars: ✭ 369 (-23.6%)
Mutual labels: jupyter-notebook, neural-networks
Cs231
Complete Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
Stars: ✭ 317 (-34.37%)
Mutual labels: jupyter-notebook, neural-networks
Deep Learning Resources
由淺入深的深度學習資源 Collection of deep learning materials for everyone
Stars: ✭ 422 (-12.63%)
Mutual labels: jupyter-notebook, neural-networks
Edward2
A simple probabilistic programming language.
Stars: ✭ 419 (-13.25%)
Mutual labels: jupyter-notebook, neural-networks
Machine learning basics
Plain python implementations of basic machine learning algorithms
Stars: ✭ 3,557 (+636.44%)
Mutual labels: jupyter-notebook, neural-networks
Supervisely
AI for everyone! 🎉 Neural networks, tools and a library we use in Supervisely
Stars: ✭ 332 (-31.26%)
Mutual labels: jupyter-notebook, neural-networks
Probability
Probabilistic reasoning and statistical analysis in TensorFlow
Stars: ✭ 3,550 (+634.99%)
Mutual labels: jupyter-notebook, neural-networks
Easy Deep Learning With Keras
Keras tutorial for beginners (using TF backend)
Stars: ✭ 367 (-24.02%)
Mutual labels: jupyter-notebook, neural-networks
Bayesian Analysis Recipes
A collection of Bayesian data analysis recipes using PyMC3
Stars: ✭ 479 (-0.83%)
Mutual labels: jupyter-notebook, neural-networks
Gdrl
Grokking Deep Reinforcement Learning
Stars: ✭ 304 (-37.06%)
Mutual labels: jupyter-notebook, neural-networks
Deep Reinforcement Learning
Repo for the Deep Reinforcement Learning Nanodegree program
Stars: ✭ 4,012 (+730.64%)
Mutual labels: jupyter-notebook, neural-networks
Deep Learning Specialization on Coursera
Master Deep Learning, and Break into AI
Instructor: Andrew Ng
This repo contains all my work for this specialization. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera.
Goals
- Learn the foundations of Deep Learning
- Understand how to build neural networks
- Learn how to lead successful machine learning projects
- Learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.
- Work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing.
- Practice all these ideas in Python and in TensorFlow.
Courses
Course 1: Neural Networks and Deep Learning
Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
-
Week 1 - Practical aspects of Deep Learning
-
Learning Objectives
- Recall that different types of initializations lead to different results
- Recognize the importance of initialization in complex neural networks.
- Recognize the difference between train/dev/test sets
- Diagnose the bias and variance issues in your model
- Learn when and how to use regularization methods such as dropout or L2 regularization.
- Understand experimental issues in deep learning such as Vanishing or Exploding gradients and learn how to deal with them
- Use gradient checking to verify the correctness of your backpropagation implementation
-
-
Week 2 - Optimization algorithms
-
Learning Objectives
- Remember different optimization methods such as (Stochastic) Gradient Descent, Momentum, RMSProp and Adam
- Use random minibatches to accelerate the convergence and improve the optimization
- Know the benefits of learning rate decay and apply it to your optimization
-
Try notebooks in the cloud
To try out example notebooks interactively in your web browser, just click on the binder link:
Contributing
Contributions are welcome! For bug reports or requests please submit an issue.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].