NavinManaswi / Book Deep Learning Applications With Applications Using Python
Chapterwise code available in the book
Stars: ✭ 151
Labels
Projects that are alternatives of or similar to Book Deep Learning Applications With Applications Using Python
Math With Python
Various math-related things in Python code
Stars: ✭ 150 (-0.66%)
Mutual labels: jupyter-notebook
Designing Data Intensive Applications Notes
Reading notes on the excellent "Designing Data-Intensive Applications"
Stars: ✭ 151 (+0%)
Mutual labels: jupyter-notebook
Homework fall2020
Assignments for Berkeley CS 285: Deep Reinforcement Learning (Fall 2020)
Stars: ✭ 149 (-1.32%)
Mutual labels: jupyter-notebook
The Python Workshop
A New, Interactive Approach to Learning Python
Stars: ✭ 150 (-0.66%)
Mutual labels: jupyter-notebook
Ml From Scratch
All the ML algorithms, ML models are coded from scratch by pure Python/Numpy with the Math under the hood. It works well on CPU.
Stars: ✭ 151 (+0%)
Mutual labels: jupyter-notebook
Stereoconvnet
Stereo convolutional neural network for depth map prediction from stereo images
Stars: ✭ 150 (-0.66%)
Mutual labels: jupyter-notebook
Deeplearningbook
Repositório do Deep Learning Book - www.deeplearningbook.com.br
Stars: ✭ 150 (-0.66%)
Mutual labels: jupyter-notebook
Hands On Machine Learning With Scikit Learn Keras And Tensorflow
Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron
Stars: ✭ 151 (+0%)
Mutual labels: jupyter-notebook
Sphereface Plus
SphereFace+ Implementation for <Learning towards Minimum Hyperspherical Energy> in NIPS'18.
Stars: ✭ 151 (+0%)
Mutual labels: jupyter-notebook
Spark With Python
Fundamentals of Spark with Python (using PySpark), code examples
Stars: ✭ 150 (-0.66%)
Mutual labels: jupyter-notebook
Fifa 2018 World Cup Predictions
I used Machine Learning to make a Logistic Regression model using scikit-learn, pandas, numpy, seaborn and matplotlib to predict the results of FIFA 2018 World Cup.
Stars: ✭ 151 (+0%)
Mutual labels: jupyter-notebook
Parcels
Main code for Parcels (Probably A Really Computationally Efficient Lagrangian Simulator)
Stars: ✭ 148 (-1.99%)
Mutual labels: jupyter-notebook
Data Engineering Nanodegree
Projects done in the Data Engineering Nanodegree by Udacity.com
Stars: ✭ 151 (+0%)
Mutual labels: jupyter-notebook
Tfvos
Semi-Supervised Video Object Segmentation (VOS) with Tensorflow. Includes implementation of *MaskRNN: Instance Level Video Object Segmentation (NIPS 2017)* as part of the NIPS Paper Implementation Challenge.
Stars: ✭ 151 (+0%)
Mutual labels: jupyter-notebook
Ai Matrix
To make it easy to benchmark AI accelerators
Stars: ✭ 151 (+0%)
Mutual labels: jupyter-notebook
Book-Deep-Learning-with-Applications-using-Python
This repository consists of all the codes(chapterwise) that are explained in my Book.
To learn Deep Learning from scratch, plz have a copy
https://www.amazon.in/Deep-Learning-Applications-Using-Python/dp/1484235150
Chapter 1 is not the part of the book. Chapters 2-13 can be considered as chapters 1-12
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].