🚀🔥 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.

In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.

Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.

👉 Apply now: https://go.readytensor.ai/cert-551-agentic-ai-certification

Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:

1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.

Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.

Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.

2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.

These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.

Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.

3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.

Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.

4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.

LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.

5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.

Join for more: https://t.me/machinelearning_deeplearning

Amazon Interview Process for Data Scientist position

📍Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.

After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).

📍 𝗥𝗼𝘂𝗻𝗱 𝟮- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵:
In this round the interviewer tested my knowledge on different kinds of topics.

📍𝗥𝗼𝘂𝗻𝗱 𝟯- 𝗗𝗲𝗽𝘁𝗵 𝗥𝗼𝘂𝗻𝗱:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.

📍𝗥𝗼𝘂𝗻𝗱 𝟰- 𝗖𝗼𝗱𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱-
This was a Python coding round, which I cleared successfully.

📍𝗥𝗼𝘂𝗻𝗱 𝟱- This was 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 where my fitment for the team got assessed.

📍𝗟𝗮𝘀𝘁 𝗥𝗼𝘂𝗻𝗱- 𝗕𝗮𝗿 𝗥𝗮𝗶𝘀𝗲𝗿- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.

So, here are my Tips if you’re targeting any Data Science role:
-> Never make up stuff & don’t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

Statistical Tests in AB Testing