kazunori279 / Tensorflow For Absolute Beginners
Licence: apache-2.0
TensorFlow for absolute beginners: a 3-hour codelab for self-learning neural network basics without hard math.
Stars: ✭ 165
Labels
Projects that are alternatives of or similar to Tensorflow For Absolute Beginners
Fixy
Amacımız Türkçe NLP literatüründeki birçok farklı sorunu bir arada çözebilen, eşsiz yaklaşımlar öne süren ve literatürdeki çalışmaların eksiklerini gideren open source bir yazım destekleyicisi/denetleyicisi oluşturmak. Kullanıcıların yazdıkları metinlerdeki yazım yanlışlarını derin öğrenme yaklaşımıyla çözüp aynı zamanda metinlerde anlamsal analizi de gerçekleştirerek bu bağlamda ortaya çıkan yanlışları da fark edip düzeltebilmek.
Stars: ✭ 165 (+0%)
Mutual labels: jupyter-notebook
Cvpr18 Inaturalist Transfer
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CVPR 2018
Stars: ✭ 164 (-0.61%)
Mutual labels: jupyter-notebook
Dsp
Metis Data Science Bootcamp - Official Prework Repository
Stars: ✭ 164 (-0.61%)
Mutual labels: jupyter-notebook
Rwf2000 Video Database For Violence Detection
A large scale video database for violence detection, which has 2,000 video clips containing violent or non-violent behaviours.
Stars: ✭ 165 (+0%)
Mutual labels: jupyter-notebook
Workshops
Workshops organized to introduce students to security, AI, AR/VR, hardware and software
Stars: ✭ 162 (-1.82%)
Mutual labels: jupyter-notebook
Simple Ehm
A simple tool for a simple task: remove filler sounds ("ehm") from pre-recorded speeches. AI powered.
Stars: ✭ 164 (-0.61%)
Mutual labels: jupyter-notebook
Awesome Scientific Writing
⌨️ A curated list of awesome tools, demos and resources to go beyond LaTeX
Stars: ✭ 162 (-1.82%)
Mutual labels: jupyter-notebook
Replika Research
Replika.ai Research Papers, Posters, Slides & Datasets
Stars: ✭ 164 (-0.61%)
Mutual labels: jupyter-notebook
Appearance Flow
A deep learning framework for synthesizing novel views of objects and scenes
Stars: ✭ 164 (-0.61%)
Mutual labels: jupyter-notebook
A Journey Into Convolutional Neural Network Visualization
A journey into Convolutional Neural Network visualization
Stars: ✭ 165 (+0%)
Mutual labels: jupyter-notebook
Maml Jax
Implementation of Model-Agnostic Meta-Learning (MAML) in Jax
Stars: ✭ 164 (-0.61%)
Mutual labels: jupyter-notebook
Ta
Technical Analysis Library using Pandas and Numpy
Stars: ✭ 2,649 (+1505.45%)
Mutual labels: jupyter-notebook
Segflow
Demo code of the paper "SegFlow: Joint Learning for Video Object Segmentation and Optical Flow", in ICCV 2017
Stars: ✭ 165 (+0%)
Mutual labels: jupyter-notebook
Text Emotion Classification
Archived - not answering issues
Stars: ✭ 165 (+0%)
Mutual labels: jupyter-notebook
TensorFlow for absolulte beginners
When I opened a neural network text book and saw the bunch of math formulas, I felt like "this is not for me". But wait, TensorFlow now provides the high-level API that let you write a few lines of Python code to get started with neural network, without understanding the hard math. Try this codelab to see how machine learning works on your laptop.
This codelab is designed as an easy TensorFlow introduction for non ML experts. All you need to know is how to use Python. It would take about 2 - 3 hours to go through all the sections.
- Preparation: setting up the codelab environment with Cloud Datalab (Jupyter Notebook on GCP)
- Understanding neural network with TensorFlow Playgroud: See how "neuron" works with Playground demo
- Classify Manhattan with TensorFlow: Let's use TensorFlow to train the most basic neural network
- Why deep neural network can get smarter?: Why neural network can get smarter? Build your own deep neural network with Playground and see how it works.
- Classify MNIST images with TensorFlow: Use TensorFlow to train a neural network to clasisfy handwritten text images.
Please get started from the Preparation doc.
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].