Are you looking for a place to learn Deep Learning? Whether you are a beginner or an experienced Machine Learning Engineer, I am sure you will find the below collection useful.
In this post, we gathered together all of our resources and organized them in a step by step guide to help you learn all the popular Deep Learning architectures and algorithms as efficiently and as fast as possible.
You will also find articles focused on specific applications such as Computer Vision and Natural Language Processing (NLP) but also posts about how Reinforcement Learning works.
So, without further ado, let’s get started…
Deep Learning Architectures
Neural network library from scratch
In this post, you will build a Feedforward Neural Network from scratch using C++. You will implement the backpropagation algorithm, define the network’s structure and train it in GPU using OpenCL
Convolutional Neural network library from scratch
In the second part, you will extend the library by including Convolutional neural networks. You will define the convolutional and pooling layers and program the OpenCL kernels to run the backpropagation in parallel.
Intuitive Explanation of Skip Connections in Deep Learning
What are skip connections, why they solve the vanishing gradient problem and how they are applied to popular Convolutional Neural Network architectures such as ResNet, DenseNet and UNet.
Predict Bitcoin price with LSTM
Learn how recurrent neural networks works, what are LSTMs and what problem do they solve and finally use one to predict the bitcoin price using Python and Keras
How to Generate Images using Autoencoders
The inner working of Autoencoders, what is a Variational Autoencoder (VAE) and how they can be used to generate original new images using PyTorch
Decrypt Generative Artificial Intelligence and GANs
How Generative models differ from other machine learning architectures, how Generative Adversarial Networks (GAN) learn from data and why they are able to generate new data points?
Graph Neural Networks
Neural Networks can also be applied in graph data besides images and text. Graph Neural Networks have the ability to take a Graph as an input and encode its information into a single numeric vector.
Explain Neural Arithmetic Logic Units (NALU)
Neural Arithmetic Logic Units solve a problem that most machine learning architectures can’t tackle. They are able to count. With NALU we can perform arithmetic operations such additions and multiplications and approximate simple arithmetic functions.
Computer Vision and Deep Learning
Semantic Segmentation in the era of Neural Networks
In semantic segmentation, the goal is to classify each pixel of the image in a specific category based. That way we can extract contextual information of every object in the image. To achieve this, skip connections are used in neural networks, forming a new architecture called UNets.
Understand how UNets work, why the perform well in semantic segmentation and program one using Keras.
Localization and Object Detection with Deep Learning
Localization is the task of identifying the location of an object in an image, while Object Detection is the classification and detection of all objects in it. To do this, the most popular method is an R-CNN alongside with its improvements Fast R-CNN and Faster R-CNN.
YOLO - You only look once
SIngle shot detectors like YOLO provide a fast way to detect and localize objects in an image. In this post, you will learn the secrets behind YOLO and why it became the industry standard in low-power devices such as smartphones.
Self-driving cars using Deep Learning
Learn the basics steps behind the development of a car’s autopilots and use a game simulator and python to make your own car drive all by itself.
Human Pose Estimation
This is an overview of the most important research papers on 2D and 3D Human Pose Estimation. You will find intuitive explanations on algorithms like OpenPose, DensePose and VIBE.
Deep learning in medical imaging: 3D medical image segmentation with PyTorch
In this document, we tackle the 3D medical image segmentation with deep learning models using PyTorch. The basic MRI foundations are presented for tensor representation, as well as the basic components to apply a deep learning method that handles the task-specific problems(class imbalance, liited data). Moreover, we present some features of the open source medical image segmentation library. Finally, we discuss our preliminary experimental results and provide sources to find medical imaging data.
The secrets behind Reinforcement Learning
The purpose of this article is to give you an idea of the fundamentals principles behind reinforcement learning, how we define our agent, the environment and the learning process. It also includes an overview of the different types of RL algorithms and the basic logic behind them.
Deep Q Learning
Here we dive into Q Learning, we analyze what exactly the Q value is and how we can approximate it and also how neural networks and deep learning revolutionize this technique. You will also find code examples on how to build your own Deep Q Learning agent in Python.
Taking Deep Q Networks a step further
As a continuance of the previous post, here we introduce topics like Moving and Fixed Q targets, concepts like Maximation Bias and Experience Replay and we describe how Double Deep Q Networks and Dueling Deep Q Networks improve over the original idea
Unravel Policy Gradients and REINFORCE
What are policy-based methods, why they are different from value-based methods and what’s the idea behind Monte Carlo policy gradients (aka REINFORCE). As always python code is available in the end.
The idea behind Actor-Critics and how A2C and A3C improve them
In this article, you will learn about actor critics and many variations of them such as Advantage Actor-Critics (A2C) and Asynchronous Advantage Actor-Critics (A3C)
Trust Region and Proximal policy optimization (TRPO and PPO)
Returning to policy methods, we present two of the most recent algorithms in the field: Trust region policy optimization (TRPO) and Proximal policy optimization (PPO)
That’s all. But for now. We are constantly producing new content so expect the list to be updated frequently.
If you managed to read all of them, let me say that you are awesome.
Also, if you think that something is missing, don’t hesitate to contact us and suggest a topic. We want to keep the article as complete as possible.
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Stay tuned and keep learning Deep Learning.
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