• 🧠 Understand the theory behind principal components analysis (PCA)
• 💡 Know why PCA is useful for dimensionality reduction, visualization, de-correlation, and denoising
• ✍️ Derive the PCA algorithm by hand
• 💻 Write the code for PCA
• 🌀 Understand the theory behind t-SNE
• 💻 Use t-SNE in code
• 🚫 Understand the limitations of PCA and t-SNE
• 🧠 Understand the theory behind autoencoders
• 💻 Write an autoencoder in Theano and Tensorflow
• 🧠 Understand how stacked autoencoders are used in deep learning
• 💻 Write a stacked denoising autoencoder in Theano and Tensorflow
• 🧠 Understand the theory behind restricted Boltzmann machines (RBMs)
• 🤔 Understand why RBMs are hard to train
• 🔄 Understand the contrastive divergence algorithm to train RBMs
• 💻 Write your own RBM and deep belief network (DBN) in Theano and Tensorflow
• 🖼️ Visualize and interpret the features learned by autoencoders and RBMs
• 🤖 Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

• 💡 This course delves into the workings of AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion.

• 💻 It serves as a progression in deep learning, data science, and machine learning education, particularly focusing on unsupervised deep learning.

• 📊 Fundamental techniques covered include principal components analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction.

• 🧠 Special attention is given to autoencoders, nonlinear counterparts of PCA, and their role in enhancing supervised deep neural networks.

• 🌀 Restricted Boltzmann machines (RBMs) are explored as another tool for pretraining deep neural networks, employing methods like Gibbs sampling and Contrastive Divergence (CD-k).

• 📈 The course illustrates the application of these concepts in visually interpreting patterns learned by unsupervised neural networks, using techniques like PCA and t-SNE on learned features.

• 🛠️ All materials for the course are freely available, assuming familiarity with calculus, linear algebra, and Python programming, and require installation of essential libraries like Numpy, Theano, and Tensorflow.

• 🎯 Emphasizing understanding over mere usage, the course encourages experimentation and visualization to grasp internal workings of machine learning models, catering to those seeking in-depth knowledge beyond superficial understanding.

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Artificial Intelligence: Reinforcement Learning in Python

Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications

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💡 Apply gradient-based supervised machine learning methods to reinforcement learning

🧠 Understand reinforcement learning on a technical level

🤖 Understand the relationship between reinforcement learning and psychology

🛠️ Implement 17 different reinforcement learning algorithms

🔑 Understand important foundations for OpenAI ChatGPT, GPT-4

------------------------------What’s covered in this course?

🎰 The multi-armed bandit problem and the explore-exploit dilemma:Discusses the trade-off between exploring new options and exploiting known ones.

Central to decision-making in uncertain environments like reinforcement learning.

📈 Ways to calculate means and moving averages and their relationship to stochastic gradient descent:Explains methods like simple averaging and exponential moving averages.

These techniques are foundational in optimizing algorithms like stochastic gradient descent.

🎲 Markov Decision Processes (MDPs):Framework for modeling decision-making in a stochastic environment.

Comprises states, actions, transition probabilities, and rewards.

🧩 Dynamic Programming:Algorithmic technique to solve complex problems by breaking them down into simpler subproblems.

Widely used in reinforcement learning for solving MDPs.

🎲 Monte Carlo:Method for estimating outcomes through random sampling.

Applied in reinforcement learning for estimating value functions.

🔄 Temporal Difference (TD) Learning (Q-Learning and SARSA):Algorithms for learning value functions directly from experience.

Q-Learning and SARSA are popular TD learning methods.

🧠 Approximation Methods:Incorporating complex models, like deep neural networks, into reinforcement learning algorithms.

Enables handling large state and action spaces.

🏋️‍♂️ How to use OpenAI Gym, with zero code changes:Introduction to OpenAI Gym, a toolkit for developing and comparing reinforcement learning algorithms.

Allows for seamless testing of different RL algorithms without code adjustments.

🤖 Project: Apply Q-Learning to build a stock trading bot:Utilizing Q-Learning algorithm to develop an autonomous trading system.

Aims to optimize trading decisions based on past experiences and rewards.

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If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.

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