Landing a Job in AI: Hiring Secrets from Technical Recruiters
Welcome! DeepLearning.AI, in partnership with FourthBrain, has assembled a panel of AI technical recruiters who will share suggestions and best interviewing practices. While millions of job-seekers are enrolling for AI courses, finding an ML job that matches their skillset remains challenging. We aim to provide an event to help support the global AI community as they continue to look and apply for jobs in the tech space and other industries.
We will be taking questions during the event. Please submit your question or upvote others' here:
https://app.sli.do/event/iFyiCRFJTHBd3s6vJHmSw9
Speakers
Jeffrey Bank, Talent Attractor
https://www.linkedin.com/in/jeffbank/
Nikita Gupta, Senior Tech Recruiter
https://www.linkedin.com/in/guptanikita16/
Puneet Kohli, Co-founder and CEO, Careerflow
https://www.linkedin.com/in/punkohl/
Moderator
Linda Lee, Talent Partner, AI fund
https://www.linkedin.com/in/linda317lee/
Let us know how we're doing? We will be giving out discount codes for a selected number of people who fill out the survey:
https://forms.gle/kHckUDW4WuRwRunH7
Looking to connect with your peer learners, share projects, and swap advice? Join our AI community:
https://community.deeplearning.ai/invites/ddzDLVa2jv
To learn more about us and signup for future events:
DeepLearning.AI: https://www.deeplearning.ai/
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Forward and Backward Propagation (C1W4L06)
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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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Random Initialization (C1W3L11)
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Parameters vs Hyperparameters (C1W4L07)
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Dropout Regularization (C2W1L06)
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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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