For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3wFLTVF

Professor Christopher Manning, Stanford University & Margaret Mitchell, Google AI
http://onlinehub.stanford.edu/

Professor…

How to turn a collection of small building blocks into a versatile tool for solving regression problems.

In this tutorial, I will give an overview of the TensorFlow 2.x features through the lens of deep reinforcement learning (DRL)
by implementing an advantage actor-critic (A2C) agent, solving the classic CartPole-v0 environment.
While the goal is to showcase…

It can be convenient to use a standard computer vision dataset when getting started with deep learning methods for computer vision.

Standard datasets are often well understood, small, and easy to load. They can provide the basis for testing techniques…

Deep Learning for Computer Vision Crash Course. Bring Deep Learning Methods to Your Computer Vision Project in 7 Days. We are awash in digital images from photos, videos, Instagram, YouTube, and increasingly live video streams. Working with image data is…

Cohort analysis is one of the best ways to understand your company’s growth — it often reveals things that are important but not obvious from top-line measures like MRR, MAUs, etc. Historically, it’s been kind of a slog to get cohort analysis up and running…

I've built an app called NeuralFund that uses Tensorflow 2.0 to make automated investment decisions. I used Tensorflow 2.0 to train a transformer network on time series data that i downloaded using the Yahoo Finance API. Then, I used Tensorflow Serving +…

Dive into Deep Learning
UC Berkeley, STAT 157

Slides are at
http://courses.d2l.ai
The book is at
http://www.d2l.ai

RNN in Python

There are conventions for storing and structuring your image dataset on disk in order to make it fast and efficient to load and when training and evaluating deep learning models. Once structured, you can use tools like the ImageDataGenerator class in the…

Supplementary code for the paper "Hyperbolic Image Embeddings". - GitHub - leymir/hyperbolic-image-embeddings: Supplementary code for the paper "Hyperbolic Image Embeddings".

Outperforms Pre-Activation ResNet, WRN, Inception-ResNet, ResNeXt

Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Training deep learning neural network models on more data can result in more skillful models…

Mysteries of Neural Networks Part V

Data augmentation is a technique often used to improve performance and reduce generalization error when training neural network models for computer vision problems. The image data augmentation technique can also be applied when making predictions with a fit…

Обработка естественного языка сейчас не используются разве что в совсем консервативных отраслях. В большинстве технологических решений распознавание и обработка «человеческих» языков давно...

UC Berkeley CS294-158 Deep Unsupervised Learning (Spring 2019)
Instructors: Pieter Abbeel, Xi (Peter) Chen, Jonathan Ho, Aravind Srinivas
https://sites.google.com/view/berkele...
Week 8c Lecture Contents:
- Ilya Sutskever guest lecture on GPT-2

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📝 The paper "Better Language Models and Their Implications" is available here:
https://openai.com/blog/better-language-models/

GPT-2 Reddit bot:
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YoloV3 Implemented in Tensorflow 2.0. Contribute to zzh8829/yolov3-tf2 development by creating an account on GitHub.