AI Dev 25 x NYC | David Park: Impact of Agentic AI in Financial Services on Document Extraction

DeepLearningAI · Beginner ·🤖 AI Agents & Automation ·3mo ago
David Park, Director of Customer Facing MLE at Landing AI, focused his AI Dev 25 x NYC talk in financial services on multimodal document extraction. Agentic AI is transforming financial services by enabling intelligent, autonomous workflows that combine language, vision, and structured reasoning to extract insights from complex, multimodal documents at scale. By orchestrating specialized models and decision-making agents, these systems move beyond static OCR and rule-based parsing to handle diverse data types — from regulatory filings and KYC forms to contracts and blueprints — with greater …
Watch on YouTube ↗ (saves to browser)

Playlist

Uploads from DeepLearningAI · DeepLearningAI · 0 of 60

← Previous Next →
1 Forward and Backward Propagation (C1W4L06)
Forward and Backward Propagation (C1W4L06)
DeepLearningAI
 2 deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
DeepLearningAI
 3 deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
DeepLearningAI
 4 deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
DeepLearningAI
 5 deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
DeepLearningAI
 6 deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
DeepLearningAI
 7 deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
DeepLearningAI
 8 Using an Appropriate Scale (C2W3L02)
Using an Appropriate Scale (C2W3L02)
DeepLearningAI
 9 Gradient Checking (C2W1L13)
Gradient Checking (C2W1L13)
DeepLearningAI
 10 Gradient Checking Implementation Notes (C2W1L14)
Gradient Checking Implementation Notes (C2W1L14)
DeepLearningAI
 11 Learning Rate Decay (C2W2L09)
Learning Rate Decay (C2W2L09)
DeepLearningAI
 12 Understanding Mini-Batch Gradient Dexcent (C2W2L02)
Understanding Mini-Batch Gradient Dexcent (C2W2L02)
DeepLearningAI
 13 Mini Batch Gradient Descent (C2W2L01)
Mini Batch Gradient Descent (C2W2L01)
DeepLearningAI
 14 The Problem of Local Optima (C2W3L10)
The Problem of Local Optima (C2W3L10)
DeepLearningAI
 15 Exponentially Weighted Averages (C2W2L03)
Exponentially Weighted Averages (C2W2L03)
DeepLearningAI
 16 Tuning Process (C2W3L01)
Tuning Process (C2W3L01)
DeepLearningAI
 17 Understanding Exponentially Weighted Averages (C2W2L04)
Understanding Exponentially Weighted Averages (C2W2L04)
DeepLearningAI
 18 Bias Correction of Exponentially Weighted Averages (C2W2L05)
Bias Correction of Exponentially Weighted Averages (C2W2L05)
DeepLearningAI
 19 Gradient Descent With Momentum (C2W2L06)
Gradient Descent With Momentum (C2W2L06)
DeepLearningAI
 20 Normalizing Activations in a Network (C2W3L04)
Normalizing Activations in a Network (C2W3L04)
DeepLearningAI
 21 Hyperparameter Tuning in Practice (C2W3L03)
Hyperparameter Tuning in Practice (C2W3L03)
DeepLearningAI
 22 Adam Optimization Algorithm (C2W2L08)
Adam Optimization Algorithm (C2W2L08)
DeepLearningAI
 23 RMSProp (C2W2L07)
RMSProp (C2W2L07)
DeepLearningAI
 24 Fitting Batch Norm Into Neural Networks (C2W3L05)
Fitting Batch Norm Into Neural Networks (C2W3L05)
DeepLearningAI
 25 Why Does Batch Norm Work? (C2W3L06)
Why Does Batch Norm Work? (C2W3L06)
DeepLearningAI
 26 Batch Norm At Test Time (C2W3L07)
Batch Norm At Test Time (C2W3L07)
DeepLearningAI
 27 Softmax Regression (C2W3L08)
Softmax Regression (C2W3L08)
DeepLearningAI
 28 Deep Learning Frameworks (C2W3L10)
Deep Learning Frameworks (C2W3L10)
DeepLearningAI
 29 Neural Network Overview (C1W3L01)
Neural Network Overview (C1W3L01)
DeepLearningAI
 30 Training Softmax Classifier (C2W3L09)
Training Softmax Classifier (C2W3L09)
DeepLearningAI
 31 Why Deep Representations? (C1W4L04)
Why Deep Representations? (C1W4L04)
DeepLearningAI
 32 Gradient Descent For Neural Networks (C1W3L09)
Gradient Descent For Neural Networks (C1W3L09)
DeepLearningAI
 33 Neural Network Representations (C1W3L02)
Neural Network Representations (C1W3L02)
DeepLearningAI
 34 TensorFlow (C2W3L11)
TensorFlow (C2W3L11)
DeepLearningAI
 35 Activation Functions (C1W3L06)
Activation Functions (C1W3L06)
DeepLearningAI
 36 Explanation For Vectorized Implementation (C1W3L05)
Explanation For Vectorized Implementation (C1W3L05)
DeepLearningAI
 37 Getting Matrix Dimensions Right (C1W4L03)
Getting Matrix Dimensions Right (C1W4L03)
DeepLearningAI
 38 Understanding Dropout (C2W1L07)
Understanding Dropout (C2W1L07)
DeepLearningAI
 39 Building Blocks of a Deep Neural Network (C1W4L05)
Building Blocks of a Deep Neural Network (C1W4L05)
DeepLearningAI
 40 Why Non-linear Activation Functions (C1W3L07)
Why Non-linear Activation Functions (C1W3L07)
DeepLearningAI
 41 Computing Neural Network Output (C1W3L03)
Computing Neural Network Output (C1W3L03)
DeepLearningAI
 42 Backpropagation Intuition (C1W3L10)
Backpropagation Intuition (C1W3L10)
DeepLearningAI
 43 Train/Dev/Test Sets (C2W1L01)
Train/Dev/Test Sets (C2W1L01)
DeepLearningAI
 44 Deep L-Layer Neural Network (C1W4L01)
Deep L-Layer Neural Network (C1W4L01)
DeepLearningAI
 45 Random Initialization (C1W3L11)
Random Initialization (C1W3L11)
DeepLearningAI
 46 Other Regularization Methods (C2W1L08)
Other Regularization Methods (C2W1L08)
DeepLearningAI
 47 Normalizing Inputs (C2W1L09)
Normalizing Inputs (C2W1L09)
DeepLearningAI
 48 Derivatives Of Activation Functions (C1W3L08)
Derivatives Of Activation Functions (C1W3L08)
DeepLearningAI
 49 Parameters vs Hyperparameters (C1W4L07)
Parameters vs Hyperparameters (C1W4L07)
DeepLearningAI
 50 Vectorizing Across Multiple Examples (C1W3L04)
Vectorizing Across Multiple Examples (C1W3L04)
DeepLearningAI
 51 What does this have to do with the brain? (C1W4L08)
What does this have to do with the brain? (C1W4L08)
DeepLearningAI
 52 Dropout Regularization (C2W1L06)
Dropout Regularization (C2W1L06)
DeepLearningAI
 53 Vanishing/Exploding Gradients (C2W1L10)
Vanishing/Exploding Gradients (C2W1L10)
DeepLearningAI
 54 Basic Recipe for Machine Learning (C2W1L03)
Basic Recipe for Machine Learning (C2W1L03)
DeepLearningAI
 55 Bias/Variance (C2W1L02)
Bias/Variance (C2W1L02)
DeepLearningAI
 56 Forward Propagation in a Deep Network (C1W4L02)
Forward Propagation in a Deep Network (C1W4L02)
DeepLearningAI
 57 Weight Initialization in a Deep Network (C2W1L11)
Weight Initialization in a Deep Network (C2W1L11)
DeepLearningAI
 58 Numerical Approximations of Gradients (C2W1L12)
Numerical Approximations of Gradients (C2W1L12)
DeepLearningAI
 59 Regularization (C2W1L04)
Regularization (C2W1L04)
DeepLearningAI
 60 Why Regularization Reduces Overfitting (C2W1L05)
Why Regularization Reduces Overfitting (C2W1L05)
DeepLearningAI
Introducing Claude Opus 4.5 | The Best Coding Model Ever #shorts #simplilearn
Next Up
Introducing Claude Opus 4.5 | The Best Coding Model Ever #shorts #simplilearn
Simplilearn