MLG 023 Deep NLP 2

This podcast episode explores different aspects of recurrent neural networks (RNNs) and their applications in natural language processing (NLP) tasks, stock market predictions, and time series analysis. The host discusses the types of neural networks, including RNNs, and introduces solutions to the vanishing and exploding gradient problem, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs). The episode also covers the differences between reinforcement learning and supervised learning, and how they can be applied in stock market predictions. Additionally, the episode explains the concepts of bi-directional RNNs, sequence-to-sequence models, encoder-decoder RNNs, and the limitations and benefits of using LSTM cells in RNNs.

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Machine Learning Guide

Deep NLP Part 2: RNNs, Bidirectional RNNs, and LSTMs/GRUs

Reinforcement Learning vs. Supervised Learning: Choosing the Right Approach for Time Series Tasks

Stock Market Predictions: Supervised vs. Reinforcement Learning

RNNs: The Magic of Sequence Processing

Vanilla vs. Bi-directional RNNs: Unlocking Contextual Understanding

Sequence-to-Sequence Models: Understanding the Encoding and Decoding Process

Encoder Decoder RNNs: The Perfect Tool for Capturing Contextual Meaning

RNNs: Problems and Solutions

Backpropagation Through Time: The Gradient Descent of Recurrent Neural Networks

LSTM: The Solution to the Vanishing Gradient Problem

LSTM: The Key to Solving the Vanishing Gradient Problem

LSTM: A Sophisticated and Powerful Variant of RNN

MLG 023 Deep NLP 2

Machine Learning Guide

00:00Deep NLP Part 2: RNNs, Bidirectional RNNs, and LSTMs/GRUs

Deep NLP Part 2: RNNs, Bidirectional RNNs, and LSTMs/GRUs

03:49Reinforcement Learning vs. Supervised Learning: Choosing the Right Approach for Time Series Tasks

Reinforcement Learning vs. Supervised Learning: Choosing the Right Approach for Time Series Tasks

06:49Stock Market Predictions: Supervised vs. Reinforcement Learning

Stock Market Predictions: Supervised vs. Reinforcement Learning

11:17RNNs: The Magic of Sequence Processing

RNNs: The Magic of Sequence Processing

13:36Vanilla vs. Bi-directional RNNs: Unlocking Contextual Understanding

Vanilla vs. Bi-directional RNNs: Unlocking Contextual Understanding

17:42Sequence-to-Sequence Models: Understanding the Encoding and Decoding Process

Sequence-to-Sequence Models: Understanding the Encoding and Decoding Process

20:51Encoder Decoder RNNs: The Perfect Tool for Capturing Contextual Meaning

Encoder Decoder RNNs: The Perfect Tool for Capturing Contextual Meaning

24:51RNNs: Problems and Solutions

RNNs: Problems and Solutions

27:44Backpropagation Through Time: The Gradient Descent of Recurrent Neural Networks

Backpropagation Through Time: The Gradient Descent of Recurrent Neural Networks

31:06LSTM: The Solution to the Vanishing Gradient Problem

LSTM: The Solution to the Vanishing Gradient Problem

34:50LSTM: The Key to Solving the Vanishing Gradient Problem

LSTM: The Key to Solving the Vanishing Gradient Problem

39:54LSTM: A Sophisticated and Powerful Variant of RNN

LSTM: A Sophisticated and Powerful Variant of RNN