Pathways in Machine Learning/Data Science
Skills:
Data Literacy60%
-Welcome to our virtual event on pathways in Data Science and Machine Learning. We are excited to feature industry leaders including:
-Caroline Lair, Founder of The Good AI, Co-founder of Women in AI https://www.linkedin.com/in/carolinelair/
-Sadie St Lawrence, Founder and CEO, Women in Data https://www.linkedin.com/in/sadiestlawrence/
-Gabriela de Queiroz, Chief Data Scientist, AI Strategy and Innovation, IBM https://www.linkedin.com/in/gabrieladequeiroz/
-Brooke Wenig, Director of Machine Learning Practice, Databricks https://www.linkedin.com/in/brookewenig/
Our speakers will be covering:
-When and how to decide to go all-in on Data Science and Machine Learning in your career.
-What role does MLOps play in your organization today, and what skills are required for up-and-coming ML practitioners to succeed within it.
-How to prepare for what it will be like to work on a high-performing AI team.
-Difficult pivots, when should these be made, and ways to advance your career.
-And much much more! Please post your questions in the YouTube live chat.
Event hosts:
-DeepLearning.AI: to pre-sign up for the most popular foundational machine learning course, click here: https://bit.ly/3kSqt2z
- FourthBrain: https://www.fourthbrain.ai/
Let us know what you think about the event by filling out a short survey here! https://docs.google.com/forms/d/e/1FAIpQLSd0o6pMK-nepiqUNPpwgVRgGFbYe0Nc-mWj-GAIGDqSHNcYRA/viewform
Are you on Discourse? Join our community!
The DeepLearning.AI Discourse community (https://community.deeplearning.ai/) is active and ready for you to engage. Post questions to our speakers and add discuss topics with others on machine learning and data science.
If you are new to our Discourse community, click here to sign up first: https://community.deeplearning.ai/invites/VVGtXQuWNR
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Forward and Backward Propagation (C1W4L06)
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deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
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deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
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deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
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deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
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deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
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deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
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Using an Appropriate Scale (C2W3L02)
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Gradient Checking (C2W1L13)
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Gradient Checking Implementation Notes (C2W1L14)
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Learning Rate Decay (C2W2L09)
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Understanding Mini-Batch Gradient Dexcent (C2W2L02)
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Mini Batch Gradient Descent (C2W2L01)
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The Problem of Local Optima (C2W3L10)
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Exponentially Weighted Averages (C2W2L03)
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Tuning Process (C2W3L01)
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Understanding Exponentially Weighted Averages (C2W2L04)
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Bias Correction of Exponentially Weighted Averages (C2W2L05)
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Gradient Descent With Momentum (C2W2L06)
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Normalizing Activations in a Network (C2W3L04)
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Hyperparameter Tuning in Practice (C2W3L03)
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Adam Optimization Algorithm (C2W2L08)
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RMSProp (C2W2L07)
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Fitting Batch Norm Into Neural Networks (C2W3L05)
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Why Does Batch Norm Work? (C2W3L06)
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Batch Norm At Test Time (C2W3L07)
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Softmax Regression (C2W3L08)
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Deep Learning Frameworks (C2W3L10)
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Neural Network Overview (C1W3L01)
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Training Softmax Classifier (C2W3L09)
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Why Deep Representations? (C1W4L04)
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Gradient Descent For Neural Networks (C1W3L09)
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Neural Network Representations (C1W3L02)
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TensorFlow (C2W3L11)
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Activation Functions (C1W3L06)
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Explanation For Vectorized Implementation (C1W3L05)
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Getting Matrix Dimensions Right (C1W4L03)
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Understanding Dropout (C2W1L07)
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Building Blocks of a Deep Neural Network (C1W4L05)
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Why Non-linear Activation Functions (C1W3L07)
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Computing Neural Network Output (C1W3L03)
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Backpropagation Intuition (C1W3L10)
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Train/Dev/Test Sets (C2W1L01)
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Deep L-Layer Neural Network (C1W4L01)
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Random Initialization (C1W3L11)
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Other Regularization Methods (C2W1L08)
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Normalizing Inputs (C2W1L09)
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Derivatives Of Activation Functions (C1W3L08)
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Parameters vs Hyperparameters (C1W4L07)
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Vectorizing Across Multiple Examples (C1W3L04)
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What does this have to do with the brain? (C1W4L08)
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Dropout Regularization (C2W1L06)
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Vanishing/Exploding Gradients (C2W1L10)
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Basic Recipe for Machine Learning (C2W1L03)
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Bias/Variance (C2W1L02)
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Forward Propagation in a Deep Network (C1W4L02)
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Weight Initialization in a Deep Network (C2W1L11)
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Numerical Approximations of Gradients (C2W1L12)
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Regularization (C2W1L04)
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Why Regularization Reduces Overfitting (C2W1L05)
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