Your Comprehensive Guide to Learning Deep Learning

Are you looking for a place to learn Deep Learning? Whether you are a beginner or an experienced Machine Learning Engineer, this guide is designed to provide you with a curated collection of resources that will help you navigate the complex world of Deep Learning.

In this article, we have gathered a wealth of resources and organized them into a step-by-step guide to help you learn all the popular Deep Learning architectures and algorithms as efficiently and quickly as possible. You will find articles focused on specific applications such as Computer Vision and Natural Language Processing (NLP), as well as insights into 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 on a GPU using OpenCL. This foundational knowledge is crucial for understanding how neural networks operate under the hood.

Convolutional Neural Network Library from Scratch

Building on the previous article, this section extends the library to include Convolutional Neural Networks (CNNs). You will learn to define convolutional and pooling layers and program OpenCL kernels to run backpropagation in parallel, enhancing your understanding of CNNs, which are pivotal in image processing tasks.

Intuitive Explanation of Skip Connections in Deep Learning

Skip connections are a powerful tool in Deep Learning that help mitigate the vanishing gradient problem. This article explains their significance and how they are applied in popular architectures like ResNet, DenseNet, and UNet, making complex models more efficient and effective.

Predict Bitcoin Price with LSTM

Dive into the world of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. This resource will guide you through using LSTMs to predict Bitcoin prices using Python and Keras, illustrating the practical applications of RNNs in time series forecasting.

How to Generate Images Using Autoencoders

Explore the inner workings of Autoencoders and Variational Autoencoders (VAEs). This article will teach you how to generate new images using PyTorch, showcasing the creative potential of these models in generating original content.

Decrypt Generative Artificial Intelligence and GANs

Generative models, particularly Generative Adversarial Networks (GANs), are revolutionizing the field of AI. This article explains how GANs learn from data and generate new data points, providing insights into their unique architecture and applications.

Graph Neural Networks

Neural Networks can also be applied to graph data. This resource introduces Graph Neural Networks (GNNs), which can encode graph information into numeric vectors, opening new avenues for data representation and analysis.

Explain Neural Arithmetic Logic Units (NALU)

NALUs address a significant limitation in traditional machine learning architectures by enabling counting and arithmetic operations. This article explains how NALUs work and their potential applications in various domains.

Computer Vision and Deep Learning

Semantic Segmentation in the Era of Neural Networks

Semantic segmentation involves classifying each pixel in an image. This article discusses how UNets utilize skip connections to excel in this task, providing a comprehensive guide to implementing a UNet using Keras.

Localization and Object Detection with Deep Learning

Learn about the techniques used for object localization and detection, focusing on R-CNN and its improvements, Fast R-CNN and Faster R-CNN. This resource is essential for understanding how to identify and classify objects within images.

YOLO – You Only Look Once

YOLO is a popular single-shot detector that provides fast object detection and localization. This article reveals the secrets behind YOLO and explains why it has become the industry standard, especially for low-power devices like smartphones.

Applications

Self-Driving Cars Using Deep Learning

Discover the foundational steps behind developing a self-driving car’s autopilot system. This resource guides you through using a game simulator and Python to create your own autonomous vehicle.

Human Pose Estimation

This overview covers significant research papers on 2D and 3D Human Pose Estimation, providing intuitive explanations of algorithms like OpenPose, DensePose, and VIBE.

Deep Learning in Medical Imaging: 3D Medical Image Segmentation with PyTorch

Explore the application of deep learning in medical imaging, focusing on 3D medical image segmentation. This article discusses the challenges of class imbalance and limited data, along with preliminary experimental results.

Reinforcement Learning

The Secrets Behind Reinforcement Learning

This article provides an overview of the fundamental principles of reinforcement learning, including agent-environment interactions and various RL algorithms.

Deep Q Learning

Delve into Q Learning and learn how neural networks enhance this technique. This resource includes code examples for building your own Deep Q Learning agent in Python.

Taking Deep Q Networks a Step Further

Building on the previous article, this section introduces advanced concepts like Moving and Fixed Q targets, Maximation Bias, and Experience Replay, along with improvements like Double Deep Q Networks and Dueling Deep Q Networks.

Unravel Policy Gradients and REINFORCE

Explore policy-based methods and their differences from value-based methods, focusing on Monte Carlo policy gradients (REINFORCE) with practical Python code examples.

The Idea Behind Actor-Critics and How A2C and A3C Improve Them

Learn about actor-critic methods and their variations, including Advantage Actor-Critics (A2C) and Asynchronous Advantage Actor-Critics (A3C).

Trust Region and Proximal Policy Optimization (TRPO and PPO)

This article presents two of the most recent algorithms in reinforcement learning: Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO).

Conclusion

That’s all for now! We are constantly producing new content, so expect this list to be updated frequently. If you managed to read through all of these resources, let me say that you are awesome!

If you think something is missing or have suggestions for topics, don’t hesitate to contact us. We aim to keep this article as comprehensive as possible.

And don’t forget to subscribe to our newsletter to be notified when new articles are published. Stay tuned and keep learning Deep Learning!

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