Stack/Queue FAQs, CN continues, Convolutional Neural Networks

Andrew Ng DL Spec Course 4: Convolutional Neural Networks (Week 1: The convolution operation, edge detection, padding: valid vs same, stride, multi-channel convolutions, pooling layers: max vs average)

HTTP/1.0, HTTP/1.1 (Persistent connections, Pipelining), HTTP/2 (Multiplexing, Header compression), HTTP/3 (QUIC over UDP), HTTP Methods, Status Codes + Quiz

Andrew Ng DL Spec Course 4 (Week 2: LeNet-5, AlexNet, VGG-16, 1x1 convolutions / Network in Network, introduction to Inception and ResNet architectures)

Introduction to Cryptography, Security Goals of Cryptography (Confidentiality, Integrity, Availability - CIA Triad), Symmetric vs Asymmetric Encryption, HTTPS and TLS Handshake & Perfect Forward Secrecy, Diffie-Hellman Key Exchange, Hash Functions, MAC & HMAC, Digital Signatures, PKI, Certificate Pinning, DNSSEC, Cookies vs Sessions vs JWT, WebSockets + Quiz: Introduction to Cryptography

PyTorch/Keras: Build, train, and evaluate a convolutional neural network on CIFAR-10 with data augmentation (flips, crops), Dropout, and Batch Normalization; push to GitHub with README

Deep reading of Ioffe & Szegedy (2015) "Batch Normalization" & Srivastava et al. (2014) "Dropout"; implement custom BatchNorm1d layer.

Quant M19 Progressions (Basic & Advance) + Verbal/Logical Mixed Practice

Deep reading of He et al. (2015) "Deep Residual Learning for Image Recognition"; implement skip connection and ResidualBlock in PyTorch.

Firewalls (Packet filtering, Stateful, Application), Symmetric vs Asymmetric encryption, Content Delivery Networks (CDNs), Forward vs Reverse Proxies