Develop-Packt
Popular repositories
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Generate and paraphrase text using different models for use in Python. Understand the applications and challenges of text summarization models.
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This course examines the Monte Carlo methods and its types and solves the frozen lake problem with Monte Carlo methods.
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Experiment with Neural Network architectures to build and evaluate both single and multi-layer sequential models in Keras
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Learn to compare, contrast, and apply different types of machine learning algorithm. Also analyze overfitting and implement regularization and solve real-world problems using the machine learning a…
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This module enables you summarize and identify the quality of the data using concepts such as aggregation and window functions.
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Learn the fundamental concepts of data wrangling and statistics, and understand how they relate to data visualization.
Repositories
- Introduction-to-Reinforcement-Learning Public
This module introduces the world of reinforcement learning and discusses some common applications. You will solve an autonomous driving problem using pure Python
- Discussing-Advancements-for-Reinforcement-Learning Public
This module discusses the current state of reinforcement learning and describes some promising approaches being taken to advance the field.
- Discussing-Evolutionary-Strategies-for-Reinforcement-Learning Public
This module discusses the motivation for evolutionary strategies, and breaks down the components of genetic algorithms and how they can be tailored for reinforcement learning.
- Introduction-to-Policy-Based-Methods-for-Reinforcement-Learning Public
This module looks at policy based methods of reinforcement learning, principally the drawbacks to value based methods like Q learning that motivate the use of policy gradients.
- Markov-Decision-Processes-and-Bellman-Equations Public
The module covers the theory behind reinforcement learning and introduces Markov chains and Markov Decision Processes
- Introduction-to-Artificial-Intelligence Public
This module introduces you to the fundamentals of Artificial Intelligence. You will be implementing your first AI through a simple Tic-Tac-Toe game where you will be teaching the program on how to win against a human player
- Introduction-to-Data-Wrangling-with-Python Public
Briefly review the foundational components of data wrangling and Python data structures.
- Deep-Learning-for-Sequences Public
This module explores how important Recurrent Neural Networks (RNNs) are for sequence modeling. It particularly focuses on deep learning approaches for sequences, particularly plain RNNs and 1D convolutions Foundations more advanced RNN-based models are laid in this module
- Introduction-to-Deep-Learning-and-Neural-Networks Public
In this chapter you will be introduced to the final topic on neural networks and deep learning. You will come across TensorFlow, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). You will also be implementing an image classification program using neural networks and deep learning
- Image-Recognition-with-Convolutional-Neural-Networks-CNN Public
This module introduces the architecture of CNN and explains how to implement it to develop image classifiers from scratch
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