Week 1: The Engine of AI & The Data-Driven Paradigm
Ditch the endless if-else statements. This week strips away the magic of Machine Learning so you can build it from the ground up. You will transition from rigid hand-coded rules to the dynamic Data-Driven Paradigm. We break down Linear Classifiers, translate raw scores into probabilities, quantify mistakes with Cross-Entropy, and code the ultimate optimization engine: Gradient Descent. Core Topics: The Data-Driven Approach, k-Nearest Neighbor (k-NN) & Distance Metrics, Softmax & Cross-Entropy Loss, Optimization via Gradient Descent. Week 1 Lab: Write loop-free vectorized NumPy code to classify MNIST digits, implement k-NN for CIFAR, and code your first PyTorch Gradient Descent loop. Time to physically visualize the "brain" of your classifier.
8 weeks · 59 lectures · free to watch
Start Week 1 →- 3.1Transfer Learning: Standing on the Shoulders of Giants19m
- 3.2Embeddings, Vector Search, and Retrieval Augmented Generation (RAG)13m
- 3.3Babysitting - The Learning Process23m
- 3.4Hyperparameter Optimization28m
- 3.5Deep Learning 102: Mastering the Convolutional Building Block33m
- 3.6Building Blocks - Convolution12m
- 3.7Building Block - Max Pool13m
- 3.8Cross Entropy vs Mean Square Loss4m
- 4.1Cross Validation and Hyperparameters9m
- 4.2Receptive Field of Deep Convolutional Networks4m
- 4.3Weight Initialization17m
- 4.4LeCun's Cake & The Hidden Geometry of Data13m
- 4.5Why High-Dimensional Space is a Lonely Place (The Sea Urchin)20m
- 4.6How FaceID Works: Siamese Networks & One-Shot Learning23m
- 4.7From Siamese to Triplet Networks: How Google Trained FaceNet14m
- 5.1The Deep Learning Story: From Cat Brains to AlphaFold9m
- 5.1-2Why Data, GPUs, and ReLU Changed Everything13m
- 5.2CNN Architectures: Evolution of Depth, Width, and Residuals22m
- 5.3From Fixed Inputs to Infinite Sequences: Introduction to RNNs13m
- 5.4From Vanishing Gradients to LSTMs: Solving the Memory Problem10m
- 5.5Sequence-to-Sequence: Encoder-Decoders and the Vanishing Gradient16m
- 6.1Breaking the Bottleneck: From RNNs to the Attention Revolution7m
- 6.2The Trinity of Transformers: Queries, Keys, and Values Explained24m
- 6.3Inside the Transformer: How Queries, Keys, and Values Create Meaning9m
- 6.4Assembling the Transformer: From Positional Encodings to GPT22m
- 6.5The Evolution of the Transformer: From "Attention Is All You Need" to Llama 39m
- 6.6Everything is a Transformer: Applying Attention to Images, Audio, and Robots9m
- 8.1Why do we need to post train LLMs2m
- 8.2Post Training LLMs29m
- 8.3Supervised Fine tuning14m
- 8.4RL based Fine Tuning16m
- 8.5Pitfalls and Advanced RL8m
- 8.6The Full Pipeline in Practice23m
- 8.7Mathematical Reasoning and Tool Calling - Notebook Walkthrough8m
- 8.8Mathematical Reasoning and Tool Calling - Notebook 2 Walkthrough18m