Deep Learning Specialization by Andrew Ng, deeplearning.ai.

Related tags

Deep Learning Deep-Learning-Coursera
Overview

Deep Learning Specialization on Coursera

Master Deep Learning, and Break into AI

This is my personal projects for the course. The course covers deep learning from begginer level to advanced. Highly recommend anyone wanting to break into AI.

Instructor: Andrew Ng, DeepLearning.ai

Course 1. Neural Networks and Deep Learning

  1. Week1 - Introduction to deep learning
  2. Week2 - Neural Networks Basics
  3. Week3 - Shallow neural networks
  4. Week4 - Deep Neural Networks

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

  1. Week1 - Practical aspects of Deep Learning - Setting up your Machine Learning Application - Regularizing your neural network - Setting up your optimization problem
  2. Week2 - Optimization algorithms
  3. Week3 - Hyperparameter tuning, Batch Normalization and Programming Frameworks

Course 3. Structuring Machine Learning Projects

  1. Week1 - Introduction to ML Strategy - Setting up your goal - Comparing to human-level performance
  2. Week2 - ML Strategy (2) - Error Analysis - Mismatched training and dev/test set - Learning from multiple tasks - End-to-end deep learning

Course 4. Convolutional Neural Networks

  1. Week1 - Foundations of Convolutional Neural Networks
  2. Week2 - Deep convolutional models: case studies - Papers for read: ImageNet Classification with Deep Convolutional Neural Networks, Very Deep Convolutional Networks For Large-Scale Image Recognition
  3. Week3 - Object detection - Papers for read: You Only Look Once: Unified, Real-Time Object Detection, YOLO
  4. Week4 - Special applications: Face recognition & Neural style transfer - Papers for read: DeepFace, FaceNet

Course 5. Sequence Models

  1. Week1 - Recurrent Neural Networks
  2. Week2 - Natural Language Processing & Word Embeddings
  3. Week3 - Sequence models & Attention mechanism

*************************************************************************************************************************************

You might also like...
Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.
Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.

Deep Image Search - AI-Based Image Search Engine Deep Image Search is an AI-based image search engine that includes deep transfer learning features Ex

null 139 Jan 1, 2023
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

H2O H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Fl

H2O.ai 6.1k Jan 5, 2023
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Machine Learning From Scratch About Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose

Erik Linder-Norén 21.8k Jan 9, 2023
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading

A tour through tensorflow with financial data I present several models ranging in complexity from simple regression to LSTM and policy networks. The s

null 195 Dec 7, 2022
PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos
PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos

PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos. By adopting a unified pipeline-based API design, PyKale enforces standardization and minimalism, via reusing existing resources, reducing repetitions and redundancy, and recycling learning models across areas.

PyKale 370 Dec 27, 2022
🧠 A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016
🧠 A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016

Deep CORAL A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation. B Sun, K Saenko, ECCV 2016' Deep CORAL can learn

Andy Hsu 200 Dec 25, 2022
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods

ADGC: Awesome Deep Graph Clustering ADGC is a collection of state-of-the-art (SOTA), novel deep graph clustering methods (papers, codes and datasets).

yueliu1999 297 Dec 27, 2022
🔥 Cogitare - A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python
🔥 Cogitare - A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python

Cogitare is a Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python. A friendly interface for beginners and a powerful too

Cogitare - Modern and Easy Deep Learning with Python 76 Sep 30, 2022
Visualizer for neural network, deep learning, and machine learning models
Visualizer for neural network, deep learning, and machine learning models

Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Tens

Lutz Roeder 21k Jan 6, 2023
Comments
  • auxiliary python files missing

    auxiliary python files missing

    Hi enggen, quite good that you've downloaded all the coursework materials but I've noticed you didn't download the auxiliary python files. that would be a nice addition.

    opened by MiroljubA 3
  • Where can I find yolo_anchors.txt?

    Where can I find yolo_anchors.txt?

    Hello, thank you for sharing you work :) I am confused by how to get to file with the values of the anchors, which you call yolo_anchors.txt. Can you tell me how to get it? Thank you in advance :)

    opened by mariaundich 0
  • Written Notes and Grammatical Correction

    Written Notes and Grammatical Correction

    Hey enggen,

    I hope you are doing well! I recently finished the specialization and added my written notes to the repo. This will help anyone who wants to refresh the topics taught in the courses. I have divided the notes into weeks so it's compatible with your repo and have dropped direct links to them in the README.md. I have also made some necessary grammar corrections.

    Let's merge this!

    Thanks

    opened by Captainspockears 0
  • Breaking Coursera Honor Code

    Breaking Coursera Honor Code

    Hello @enggen, thank you for your willingness to share about the DL Specialization. I just wanted to remind you that every student who has enrolled in a course in Coursera (you, me, and many more) agreed to follow Coursera's Honor Code. Which literally says:

    I will not make solutions to homework, quizzes, exams, projects, and other assignments available to anyone else (except to the extent an assignment explicitly permits sharing solutions). This includes both solutions written by me, as well as any solutions provided by the course staff or others.

    Same as you, I completed the DL Specialization, and I personally do not like the idea of anyone having access to solved assignments because it means unethical users could go on and "complete" the Specialization without any effort other than copying your code. Opening this "shortcut" diminishes the value of this great Specialization.

    I understand you have > 900 stars in this repository and it gets traction to your GitHub profile, but it's better to play a fair game and avoid breaking Coursera's Honor Code.

    Just my humble opinion, thank you.

    opened by jmcarrillog 0
Owner
Engen
Machine Learning Engineer with Interest in Deep Learning and Artificial General Intelligence (AGI). Lifelong learner.
Engen
GitHub
PyTorchVideo is a deeplearning library with a focus on video understanding work

PyTorchVideo is a deeplearning library with a focus on video understanding work. PytorchVideo provides resusable, modular and efficient components needed to accelerate the video understanding research. PyTorchVideo is developed using PyTorch and supports different deeplearning video components like video models, video datasets, and video-specific transforms.

Facebook Research 2.7k Jan 7, 2023
LogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.

LogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.

donglee 279 Dec 13, 2022
DeepLearning Anomalies Detection with Bluetooth Sensor Data

Final Year Project. Constructing models to create offline anomalies detection using Travel Time Data collected from Bluetooth sensors along the route.

null 1 Jan 10, 2022
Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases.

Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases. Ivy wraps the functional APIs of existing frameworks. Framework-agnostic functions, libraries and layers can then be written using Ivy, with simultaneous support for all frameworks. Ivy currently supports Jax, TensorFlow, PyTorch, MXNet and Numpy. Check out the docs for more info!

Ivy 8.2k Jan 2, 2023
Deep learning (neural network) based remote photoplethysmography: how to extract pulse signal from video using deep learning tools

Deep-rPPG: Camera-based pulse estimation using deep learning tools Deep learning (neural network) based remote photoplethysmography: how to extract pu

Terbe Dániel 138 Dec 17, 2022
deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

null 63 Oct 17, 2022
Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

Stock Price Prediction Using Deep Learning Univariate Time Series Predicting stock price using historical data of a company using Neural networks for

Abdultawwab Safarji 7 Nov 27, 2022
FTIR-Deep Learning - FTIR Deep Learning With Python

CANDIY-spectrum Human analyis of chemical spectra such as Mass Spectra (MS), Inf

Wei Mei 1 Jan 3, 2022
Deep Learning: Architectures & Methods Project: Deep Learning for Audio Super-Resolution

Deep Learning: Architectures & Methods Project: Deep Learning for Audio Super-Resolution Figure: Example visualization of the method and baseline as a

Oliver Hahn 16 Dec 23, 2022
PyTorch implementation of the Deep SLDA method from our CVPRW-2020 paper "Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis"

Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis This is a PyTorch implementation of the Deep Streaming Linear Discriminant

Tyler Hayes 41 Dec 25, 2022